app.py

import gradio as gr
import random
import json
import re
import os
import shutil
from PIL import Image
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
import time
import psutil
from sentence_transformers import SentenceTransformer

#Imported Long Variables - comment for each move to search
from relatively_constant_variables import *

# # Initialize the zero tensor on CUDA
# zero = torch.Tensor([0]).cuda()
# print(zero.device)  # This will print 'cpu' outside the @spaces.GPU decorated function

# # Load the model and tokenizer
# llmguide_model = AutoModelForCausalLM.from_pretrained(
#     "Qwen/Qwen2-0.5B-Instruct",
#     torch_dtype="auto",
#     device_map="auto"
# )
# llmguide_tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")

# @spaces.GPU
# def llmguide_generate_response(prompt, stream=False):
#     print(zero.device)  # This will print 'cuda:0' inside the @spaces.GPU decorated function
    
#     messages = [
#         {"role": "system", "content": "You are a helpful assistant."},
#         {"role": "user", "content": prompt}
#     ]
#     text = llmguide_tokenizer.apply_chat_template(
#         messages,
#         tokenize=False,
#         add_generation_prompt=True
#     )
#     model_inputs = llmguide_tokenizer([text], return_tensors="pt").to(zero.device)

#     start_time = time.time()
#     total_tokens = 0

#     if stream:
#         streamer = TextIteratorStreamer(llmguide_tokenizer, skip_special_tokens=True)
#         generation_kwargs = dict(
#             model_inputs,
#             streamer=streamer,
#             max_new_tokens=512,
#             temperature=0.7,
#         )
#         thread = Thread(target=llmguide_model.generate, kwargs=generation_kwargs)
#         thread.start()

#         generated_text = ""
#         for new_text in streamer:
#             generated_text += new_text
#             total_tokens += 1
#             current_time = time.time()
#             tokens_per_second = total_tokens / (current_time - start_time)
#             yield generated_text, f"{tokens_per_second:.2f}"
#     else:
#         generated_ids = llmguide_model.generate(
#             model_inputs.input_ids,
#             max_new_tokens=512
#         )
#         generated_ids = [
#             output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
#         ]
#         response = llmguide_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
#         total_tokens = len(generated_ids[0])
#         end_time = time.time()
#         tokens_per_second = total_tokens / (end_time - start_time)
#         yield response, f"{tokens_per_second:.2f}"


#---------
#----------

# # Initialize GPU tensor
# zero = torch.Tensor([0]).cuda()
# print(zero.device)  # This will print 'cpu' outside the @spaces.GPU decorated function

# # Load the embedding model
# embedding_model = SentenceTransformer('all-MiniLM-L6-v2')

# # Load the Qwen model and tokenizer
# llmguide_model = AutoModelForCausalLM.from_pretrained(
#     "Qwen/Qwen2-0.5B-Instruct",
#     torch_dtype="auto",
#     device_map="auto"
# )
# llmguide_tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")

# # Sample knowledge base (replace with your own data)
# knowledge_base = [
#     "The capital of France is Paris.",
#     "Python is a popular programming language.",
#     "Machine learning is a subset of artificial intelligence.",
#     "The Earth orbits around the Sun.",
#     "orbits are a group of fans of a music group"
# ]

# # Create embeddings for the knowledge base
# knowledge_base_embeddings = embedding_model.encode(knowledge_base)

# def retrieve(query, k=2):
#     query_embedding = embedding_model.encode([query])
#     similarities = torch.nn.functional.cosine_similarity(torch.tensor(query_embedding), torch.tensor(knowledge_base_embeddings))
#     top_k_indices = similarities.argsort(descending=True)[:k]
#     return [knowledge_base[i] for i in top_k_indices]

# def get_resource_usage():
#     ram_usage = psutil.virtual_memory().percent
#     gpu_memory_allocated = torch.cuda.memory_allocated() / (1024 ** 3)  # Convert to GB
#     gpu_memory_reserved = torch.cuda.memory_reserved() / (1024 ** 3)  # Convert to GB
#     return f"RAM Usage: {ram_usage:.2f}%, GPU Memory Allocated: {gpu_memory_allocated:.2f}GB, GPU Memory Reserved: {gpu_memory_reserved:.2f}GB"

# @spaces.GPU
# def llmguide_generate_response(prompt, stream=False):
#     print(zero.device)  # This will print 'cuda:0' inside the @spaces.GPU decorated function
    
#     messages = [
#         {"role": "system", "content": "You are a helpful assistant."},
#         {"role": "user", "content": prompt}
#     ]
#     text = llmguide_tokenizer.apply_chat_template(
#         messages,
#         tokenize=False,
#         add_generation_prompt=True
#     )
#     model_inputs = llmguide_tokenizer([text], return_tensors="pt").to(zero.device)

#     start_time = time.time()
#     total_tokens = 0

#     if stream:
#         streamer = TextIteratorStreamer(llmguide_tokenizer, skip_special_tokens=True)
#         generation_kwargs = dict(
#             model_inputs,
#             streamer=streamer,
#             max_new_tokens=512,
#             temperature=0.7,
#         )
#         thread = Thread(target=llmguide_model.generate, kwargs=generation_kwargs)
#         thread.start()

#         generated_text = ""
#         for new_text in streamer:
#             generated_text += new_text
#             total_tokens += 1
#             current_time = time.time()
#             tokens_per_second = total_tokens / (current_time - start_time)
#             yield generated_text, f"{tokens_per_second:.2f}", ""

#         resource_usage = get_resource_usage()
#         yield generated_text, f"{tokens_per_second:.2f}", resource_usage
#     else:
#         generated_ids = llmguide_model.generate(
#             model_inputs.input_ids,
#             max_new_tokens=512
#         )
#         generated_ids = [
#             output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
#         ]
#         response = llmguide_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
#         total_tokens = len(generated_ids[0])
#         end_time = time.time()
#         tokens_per_second = total_tokens / (end_time - start_time)
#         resource_usage = get_resource_usage()
#         yield response, f"{tokens_per_second:.2f}", resource_usage

# def rag(query, stream=False):
#     retrieved_docs = retrieve(query)
#     context = " ".join(retrieved_docs)
#     prompt = f"Context: {context}\nQuestion: {query}\nAnswer:"
    
#     generator = llmguide_generate_response(prompt, stream)
    
#     if stream:
#         def stream_output():
#             for generated_text, tokens_per_second, ram_usage in generator:
#                 yield generated_text, tokens_per_second, ram_usage
#         return stream_output()
#     else:
#         # For non-streaming, we just need to get the final output
#         for generated_text, tokens_per_second, ram_usage in generator:
#             pass  # This will iterate to the last yield
#         return generated_text, tokens_per_second, ram_usage


# Load the embedding model
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')

# Load the Qwen model and tokenizer
llmguide_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2-0.5B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
llmguide_tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")

#import knowledge_base from relatively_constant_variables

# Create embeddings for the knowledge base
knowledge_base_embeddings = embedding_model.encode([doc["content"] for doc in knowledge_base])

def retrieve(query, k=2):
    query_embedding = embedding_model.encode([query])
    similarities = torch.nn.functional.cosine_similarity(torch.tensor(query_embedding), torch.tensor(knowledge_base_embeddings))
    top_k_indices = similarities.argsort(descending=True)[:k]
    return [(knowledge_base[i]["content"], knowledge_base[i]["id"]) for i in top_k_indices]

def get_ram_usage():
    ram = psutil.virtual_memory()
    return f"RAM Usage: {ram.percent:.2f}%, Available: {ram.available / (1024 ** 3):.2f}GB, Total: {ram.total / (1024 ** 3):.2f}GB"

@spaces.GPU
def llmguide_generate_response(prompt, doc_ids=None, stream=False):
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": prompt}
    ]
    text = llmguide_tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    model_inputs = llmguide_tokenizer([text], return_tensors="pt").to(llmguide_model.device)

    start_time = time.time()
    total_tokens = 0

    if stream:
        streamer = TextIteratorStreamer(llmguide_tokenizer, skip_special_tokens=True)
        generation_kwargs = dict(
            model_inputs,
            streamer=streamer,
            max_new_tokens=512,
            temperature=0.7,
        )
        thread = Thread(target=llmguide_model.generate, kwargs=generation_kwargs)
        thread.start()

        generated_text = ""
        for new_text in streamer:
            generated_text += new_text
            total_tokens += 1
            current_time = time.time()
            tokens_per_second = total_tokens / (current_time - start_time)
            yield generated_text, f"{tokens_per_second:.2f}", "", ", ".join(doc_ids) if doc_ids else "N/A"

        ram_usage = get_ram_usage()
        yield generated_text, f"{tokens_per_second:.2f}", ram_usage, ", ".join(doc_ids) if doc_ids else "N/A"
    else:
        generated_ids = llmguide_model.generate(
            model_inputs.input_ids,
            max_new_tokens=512
        )
        generated_ids = [
            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
        ]
        response = llmguide_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
        total_tokens = len(generated_ids[0])
        end_time = time.time()
        tokens_per_second = total_tokens / (end_time - start_time)
        ram_usage = get_ram_usage()
        yield response, f"{tokens_per_second:.2f}", ram_usage, ", ".join(doc_ids) if doc_ids else "N/A"

def process_query(query, use_rag, stream=False):
    if use_rag:
        retrieved_docs = retrieve(query)
        context = " ".join([doc for doc, _ in retrieved_docs])
        doc_ids = [doc_id for _, doc_id in retrieved_docs]
        prompt = f"Context: {context}\nQuestion: {query}\nAnswer:"
    else:
        prompt = query
        doc_ids = None
    
    generator = llmguide_generate_response(prompt, doc_ids, stream)
    
    if stream:
        def stream_output():
            for generated_text, tokens_per_second, ram_usage, doc_references in generator:
                yield generated_text, tokens_per_second, ram_usage, doc_references
        return stream_output()
    else:
        # For non-streaming, we just need to get the final output
        for generated_text, tokens_per_second, ram_usage, doc_references in generator:
            pass  # This will iterate to the last yield
        return generated_text, tokens_per_second, ram_usage, doc_references


#--------------------------------------------------------------------------------------------------------------------------------

#importing FAQAllprompts from relatively_constant_variables

#--------------------------------------------------------------------------------------------------------------------------------

#importing default_config from relatively_constant_variables

# Helper functions to dynamically add items
def add_inventory_item(inventory_items, type, name, description):
    new_item = {"type": type, "name": name, "description": description}
    inventory_items.append(new_item)
    return inventory_items

def add_skill(skills_items, branch, name, learned):
    new_skill = {"branch": branch, "name": name, "learned": learned == 'True'}
    skills_items.append(new_skill)
    return skills_items

def add_objective(objectives_items, id, name, complete):
    new_objective = {"id": id, "name": name, "complete": complete == 'True'}
    objectives_items.append(new_objective)
    return objectives_items

def add_target(targets_items, name, x, y, collisionType, collisiontext):
    new_target = {"name": name, "x": int(x), "y": int(y), "collisionType": collisionType, "collisiontext": collisiontext}
    targets_items.append(new_target)
    return targets_items

#-----------------------------------------------------------------------------------------------------------------------------------

#importing player_engagement_items and story_events from relatively_constant_variables

def pick_random_items(items, n):
    return random.sample(items, n)

def generate_timeline(events, label):
    timeline = []
    for event in events:
        timeline.append((random.randint(1, 100), label, event))
    return timeline

def create_story(timeline):
    story = []
    for entry in timeline:
        if entry[1] == "Story":
            story.append(f"The hero {entry[2].replace('engageBattle', 'engaged in a fierce battle').replace('solveRiddle', 'solved a complex riddle').replace('exploreLocation', 'explored a mysterious location')}.")
        else:
            story.append(f"The player interacted with {entry[2]}.")
    return " ".join(story)

def generate_story_and_timeline(no_ui_timeline_points=10, no_media_timeline_points=5, include_media=True):
    # Pick 10 random UI items
    random_ui_items = pick_random_items(player_engagement_items, no_ui_timeline_points)

    # Generate UI and story timelines
    ui_timeline = generate_timeline(random_ui_items, "UI")
    story_timeline = generate_timeline(story_events, "Story")

    # Initialize merged timeline with UI and story timelines
    merged_timeline = ui_timeline + story_timeline
    no_media_merged_timeline = ui_timeline + story_timeline
    #print(merged_timeline)
    #print(no_media_merged_timeline)

    # Include media-related items if specified
    if include_media:
        media_files = generate_media_file_list(no_media_timeline_points)
        #rendered_media = render_media_with_dropdowns(media_files)
        media_timeline = generate_timeline(media_files, "Media")
        merged_timeline += media_timeline

    print(merged_timeline)

    # Sort the merged timeline based on the random numbers
    merged_timeline.sort(key=lambda x: x[0])
    no_media_merged_timeline.sort(key=lambda x: x[0])

    # Create the story
    story = create_story(merged_timeline)

    # Format the timeline for display
    formatted_timeline = "\n".join([f"{entry[0]}: {entry[1]} - {entry[2]}" for entry in merged_timeline])
    no_media_formatted_timeline = "\n".join([f"{entry[0]}: {entry[1]} - {entry[2]}" for entry in no_media_merged_timeline])

    game_structure_with_media, game_structure_without_media = generate_game_structures(formatted_timeline, no_media_formatted_timeline)

    return formatted_timeline, no_media_formatted_timeline, story, json.dumps(game_structure_with_media, indent=2), json.dumps(game_structure_without_media, indent=2) #, game_structure_with_media

media_file_types = ["image", "video", "audio"]

def generate_media_file_list(n):
    return [random.choice(media_file_types) for _ in range(n)]


def show_elements(text):
    # Parse the input text
    pattern = r'(\d+): (UI|Story|Media) - (.+)'
    blocks = re.findall(pattern, text)
    
    # Sort blocks by their timestamp
    blocks.sort(key=lambda x: int(x[0]))
    
    outputs = []
    
    for timestamp, block_type, content in blocks:
        if block_type == 'UI':
            # Create HTML for UI elements
            ui_html = f'<div class="ui-element">{content}</div>'
            outputs.append(gr.HTML(ui_html))
        elif block_type == 'Story':
            # Display story elements as Markdown
            outputs.append(gr.Markdown(f"**{content}**"))
        elif block_type == 'Media':
            if content.lower() == 'audio':
                # Placeholder for audio element
                outputs.append(gr.Audio(label=f"Audio at {timestamp} in the order"))
            elif content.lower() == 'video':
                # Placeholder for video element
                outputs.append(gr.Video(label=f"Video at {timestamp} in the order"))
            elif content.lower() == 'image':
                # Placeholder for image element
                outputs.append(gr.Image(label=f"Image at {timestamp} in the order"))
    
    return outputs

def show_elements_json_input(json_input):
    data = json.loads(json_input)
    masterlocation1 = data['masterlocation1']
    
    outputs = []
    
    for location, details in masterlocation1.items():
        if location == 'end':
            continue
        
        with gr.Accordion(f"Location: {location} - Previous description {details['description']}", open=False):
            description = gr.Textbox(label="Description", value=details['description'], interactive=True)
            outputs.append(description)
            
            events = gr.Textbox(label="Events", value=json.dumps(details['events']), interactive=True)
            outputs.append(events)
            
            choices = gr.Textbox(label="Choices", value=json.dumps(details['choices']), interactive=True)
            outputs.append(choices)
            
            transitions = gr.Textbox(label="Transitions", value=json.dumps(details['transitions']), interactive=True)
            outputs.append(transitions)
            
            # New media field
            media = gr.Textbox(label="Media", value=json.dumps(details.get('media', [])), interactive=True)
            outputs.append(media)
    
    def update_json(*current_values):
        updated_data = {"masterlocation1": {}}
        locations = [loc for loc in masterlocation1.keys() if loc != 'end']
        for i, location in enumerate(locations):
            updated_data["masterlocation1"][location] = {
                "description": current_values[i*5],
                "events": json.loads(current_values[i*5 + 1]),
                "choices": json.loads(current_values[i*5 + 2]),
                "transitions": json.loads(current_values[i*5 + 3]),
                "media": json.loads(current_values[i*5 + 4])  # New media field
            }
        updated_data["masterlocation1"]["end"] = masterlocation1["end"]
        return json.dumps(updated_data, indent=2)
    
    update_button = gr.Button("Update JSON")
    json_output = gr.Textbox(label="Updated JSON", lines=10)
    
    update_button.click(update_json, inputs=outputs, outputs=json_output)
    
    return outputs + [update_button, json_output]

def create_media_component(file_path):
    print(file_path)
    _, extension = os.path.splitext(file_path)
    extension = extension.lower()[1:]  # Remove the dot and convert to lowercase

    if extension in ['jpg', 'jpeg', 'png', 'gif', 'webp']:
        return gr.Image(value=file_path, label="Image Input")
    elif extension in ['mp4', 'avi', 'mov']:
        return gr.Video(value=file_path, label="Video Input")
    elif extension in ['mp3', 'wav', 'ogg']:
        return gr.Audio(value=file_path, label="Audio Input")
    else:
        return gr.Textbox(value=file_path, label=f"File: {os.path.basename(file_path)}")

def convert_timeline_to_game_structure(timeline):
    lines = timeline.split('\n')
    game_structure = {}
    current_location = 0
    sub_location = 0

    for i, line in enumerate(lines):
        if line.strip() == "":
            continue
        
        if line[0].isdigit():  # New location starts
            current_location += 1
            sub_location = 0
            location_key = f"location{current_location}"
            game_structure[location_key] = {
                "description": "",
                "events": [],
                "choices": ["continue"],
                "transitions": {}
            }
        else:  # Continue with sub-locations or media entries
            sub_location += 1
            location_key = f"location{current_location}_{sub_location}"
        
        # Extract the event description
        parts = line.split(': ', 1)
        if len(parts) == 2:
            prefix, rest = parts
            event_parts = rest.split(' - ', 1)
            if len(event_parts) == 2:
                event_type, event_description = event_parts
            else:
                event_type, event_description = "Unknown", rest
        else:
            event_type, event_description = "Unknown", line
        
        description = rest.strip() if event_type in ["Media", "UI"] else f"{event_type}: {event_description}"
        
        if sub_location == 0:
            game_structure[f"location{current_location}"]["description"] = description
        else:
            game_structure[f"location{current_location}"]["events"].append({
                "description": description,
                "type": event_type
            })
        
        # Set the transition to the next location or to the end
        if i < len(lines) - 1:
            next_line = lines[i + 1].strip()
            if next_line and next_line[0].isdigit():  # New location starts
                game_structure[f"location{current_location}"]["transitions"]["continue"] = f"masterlocation1_location{current_location + 1}"
            else:
                #game_structure[f"location{current_location}"]["transitions"]["continue"] = f"location_{current_location}_{sub_location + 1}"
                game_structure[f"location{current_location}"]["transitions"]["continue"] = "end"
        else:
            game_structure[f"location{current_location}"]["transitions"]["continue"] = "end"
    
    # Add an end location
    game_structure["end"] = {
        "description": "The adventure ends here.",
#         "choices": [],
#         "transitions": {}
        "choices": ["restart"],
        "transitions": {"restart": "location1"}  # Assuming location_1 is the start

    }
    
    # Wrap the game structure in master_location1
    wrapped_structure = {"masterlocation1": game_structure}
    
    return wrapped_structure
    
def generate_game_structures(timeline_with_media, timeline_without_media):
    
    game_structure_with_media = convert_timeline_to_game_structure(timeline_with_media)
    game_structure_without_media = convert_timeline_to_game_structure(timeline_without_media)
    
    return game_structure_with_media, game_structure_without_media


#-----------------------------------------------------------------------------------------------------------------------------------

class Player:
    def __init__(self):
        self.inventory = []
        self.money = 20
        self.knowledge = {}

    def add_item(self, item):
        self.inventory.append(item)

    def has_item(self, item):
        return item in self.inventory

    def update_knowledge(self, topic):
        self.knowledge[topic] = True

#importing all_states from relatively_constant_variables

def validate_transitions(all_states):
    errors = []
    for location, states in all_states.items():
        for state_key, state in states.items():
            for transition_key, transition_state in state['transitions'].items():
                # Check if the transition is to another location
                if transition_state in all_states:
                    trans_location, trans_state = transition_state, 'start'  # Assuming 'start' state for new locations
                elif '_' in transition_state:
                    trans_location, trans_state = transition_state.split('_')
                else:
                    trans_location, trans_state = location, transition_state

                # Validate the transition state
                if trans_location not in all_states or trans_state not in all_states[trans_location]:
                    errors.append(f"Invalid transition from {location}.{state_key} to {trans_location}.{trans_state}")

    return errors

path_errors = validate_transitions(all_states)
if path_errors:
    for error in path_errors:
        print(error)
else:
    print("All transitions are valid.")

class GameSession:
    def __init__(self, starting_location='village', starting_state='start'):
        self.player = Player()
        self.current_location = starting_location
        self.current_state = starting_state
        self.game_log = []

    def make_choice(self, choice_index):
        state = all_states[self.current_location][self.current_state]
        if 0 <= choice_index < len(state['choices']):
            choice = state['choices'][choice_index]
            next_state = state['transitions'][choice]

            self.game_log.append(f"You chose: {choice}")
            self.game_log.append(state['description'])

            if 'consequences' in state and choice in state['consequences']:
              if state['consequences'][choice]:
                  state['consequences'][choice](self.player)
              else:
                  # Handle empty consequence, e.g., log a message or provide a default action
                  print(f"No consequence for choice: {choice}")
                  # You can add any default action here if needed

            if '_' in next_state:
                self.current_location, self.current_state = next_state.split('_')
            else:
                self.current_state = next_state

            return self.get_current_state_info()
        else:
            return "Invalid choice. Please try again."

    def get_current_state_info(self):
        state = all_states[self.current_location][self.current_state]
        choices = [f"{idx + 1}. {choice}" for idx, choice in enumerate(state['choices'])]
        return state['description'], choices, "\n".join(self.game_log)
    
    def get_current_state_media(self):
        media = all_states[self.current_location][self.current_state]['media']
        return media


def start_game(starting_location='village', starting_state='start'):
    game_session = GameSession(starting_location, starting_state)
    description, choices, game_log = game_session.get_current_state_info()
    return description, choices, game_log, game_session

def make_choice(choice, game_session, with_media=False): #Calls the nested make choice function in the game session class
    if not choice:
        description, choices, game_log = game_session.get_current_state_info()
        return description, choices, "Please select a choice before proceeding.", game_session

    choice_index = int(choice.split('.')[0]) - 1 
    result = game_session.make_choice(choice_index)

    if with_media:
        media = game_session.get_current_state_media()
        return result[0], gr.update(choices=result[1]), result[2], game_session, media
    else:
        return result[0], gr.update(choices=result[1]), result[2], game_session

def load_game(custom_config=None, with_media=False):
    global all_states
    if not custom_config:
        return gr.update(value="No custom configuration provided."), None, None, None, None, None, None

    try:
        new_config = json.loads(custom_config)
        all_states = new_config

        # Determine the starting location and state
        starting_location = next(iter(all_states.keys()))
        starting_state = next(iter(all_states[starting_location].keys()))
        print(f"Starting location: {starting_location}, Starting state: {starting_state}")

        game_session = GameSession(starting_location, starting_state)
        description, choices, game_log = game_session.get_current_state_info()
        new_path_errors = validate_transitions(all_states)
        
        output_media = []

        if with_media:
            media_list = all_states[starting_location][starting_state].get('media', [])
            print(f"Media list: {media_list}")
            
            if media_list:
                for media_path in media_list:
                    #media_component = create_media_component(media_path)
                    output_media.append(media_path)
                print(f"Created {len(output_media)} media components")

        success_message = f"Custom configuration loaded successfully!\n{new_path_errors}"
        return (
            gr.update(value=success_message),
            game_log,
            description,
            gr.update(choices=choices),
            gr.update(value=custom_config),
            game_session,
            output_media if with_media else None
        )

    except json.JSONDecodeError as e:
        error_message = format_json_error(custom_config, e)
        return gr.update(value=error_message), None, None, None, None, gr.update(value=custom_config), None

    except Exception as e:
        error_message = f"Error loading custom configuration: {str(e)}"
        return gr.update(value=error_message), None, None, None, None, gr.update(value=custom_config), None

def format_json_error(config, error):
    lineno, colno = error.lineno, error.colno
    lines = config.split('\n')
    error_line = lines[lineno - 1] if lineno <= len(lines) else ""
    pointer = ' ' * (colno - 1) + '^'

    return f"""Invalid JSON format in custom configuration:
Error at line {lineno}, column {colno}:
{error_line}
{pointer}
Error details: {str(error)}"""

def display_website(link):
  html = f"<iframe src='{link}' width='100%' height='1000px'></iframe>"
  gr.Info("If 404 then the space/page has probably been disabled - normally due to a better alternative")
  return html

initgameinfo = start_game()

#-----------------------------------------------------------------------------------------------------------------------------------

# Set the directory where files will be saved
SAVE_DIR = os.path.abspath("saved_media")

# Ensure the save directory exists
os.makedirs(SAVE_DIR, exist_ok=True)

# Define supported file extensions
SUPPORTED_EXTENSIONS = {
    "image": [".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp"],
    "audio": [".mp3", ".wav", ".ogg"],
    "video": [".mp4", ".avi", ".mov", ".webm"]
}

def save_file(file):
    if file is None:
        return "No file uploaded.", gr.update()

    try:
        # Get the original filename and extension
        original_filename = os.path.basename(file.name)
        _, extension = os.path.splitext(original_filename)

        # Check if the file extension is supported
        if not any(extension.lower() in exts for exts in SUPPORTED_EXTENSIONS.values()):
            return f"Unsupported file type: {extension}", gr.update()

        # Create a unique filename to avoid overwriting
        base_name = os.path.splitext(original_filename)[0]
        counter = 1
        new_filename = f"{base_name}{extension}"
        while os.path.exists(os.path.join(SAVE_DIR, new_filename)):
            new_filename = f"{base_name}_{counter}{extension}"
            counter += 1

        # Copy the file from the temporary location to our save directory
        dest_path = os.path.join(SAVE_DIR, new_filename)
        shutil.copy2(file.name, dest_path)

        # Return success message and updated FileExplorer
        return f"File saved as {new_filename} in {SAVE_DIR}", gr.update(value=SAVE_DIR), gr.update(value=None)
    except Exception as e:
        return f"Error saving file: {str(e)}", gr.update(value=SAVE_DIR), gr.update()

def view_file(file_path):
    if not file_path:
        return None, None, None, "No file selected."

    try:
        full_path = os.path.join(SAVE_DIR, file_path)
        _, extension = os.path.splitext(full_path)
        extension = extension.lower()

        if extension in SUPPORTED_EXTENSIONS["image"]:
            return Image.open(full_path), None, None, None
        elif extension in SUPPORTED_EXTENSIONS["audio"]:
            return None, full_path, None, None
        elif extension in SUPPORTED_EXTENSIONS["video"]:
            return None, None, full_path, None
        else:
            return None, None, None, f"Unsupported file type: {extension}"
    except Exception as e:
        return None, None, None, f"Error viewing file: {str(e)}"

def refresh_file_explorer():
    return gr.update()

#-----------------------------------------------------------------------------------------------------------------------------------

def timeline_get_random_suggestions(num_lists, items_per_list):
    """
    Generate random suggestions from a specified number of lists.
    
    :param num_lists: Number of lists to consider
    :param items_per_list: Number of items to select from each list
    :return: A list of randomly selected suggestions
    """
    selected_lists = random.sample(all_idea_lists, min(num_lists, len(all_idea_lists)))
    suggestions = []
    
    for lst in selected_lists:
        suggestions.extend(random.sample(lst, min(items_per_list, len(lst))))
    
    return suggestions

#-----------------------------------------------------------------------------------------------------------------------------------

with gr.Blocks() as demo:
    gr.HTML("""<div style="width: 100%; text-align: center">Main ideas for this space is (June 2024) (Custom component planning?):</div>
    <div style="display: flex; justify-content: center; margin-bottom: 20px; align-items: center;">
        <div style="width: 20%; text-align: center">We can generate almost any media data and more </div>
        <div style="width: 20%; text-align: center">A program exist around data </div>
        <div style="width: 20%; text-align: center">Time moves in a straight so all considerations are flattend by the nature of time </div>
        <div style="width: 20%; text-align: center">llms good at short questions </div>
        <div style="width: 20%; text-align: center">HF + Gradio allows for api use so this my prototype tool for tool use test</div>
    </div>""")
    with gr.Accordion("Qwen 0.5B as Space Guide Tests", open=False):
        with gr.Tab("General FAQ Attempt"):
            FAQMainOutput = gr.TextArea(placeholder='Output will show here')
            FAQCustomButtonInput = gr.TextArea(lines=1, placeholder='Prompt goes here')

            for category_name, category_prompts in FAQAllprompts.items():
                with gr.Accordion(f"General {category_name} Pattern based", open=False):
                    with gr.Group():
                        for index, (prompt, _) in enumerate(category_prompts):
                            button = gr.Button(prompt)
                            button.click(llmguide_generate_response, inputs=[FAQCustomButtonInput, gr.State(index), gr.State(category_name)], outputs=FAQMainOutput)

        with gr.Tab("General RAG (Pathfinder?) Attempt"):
            gr.HTML("Placeholder for weak RAG Type Charcter interaction test aka input for JSON 'Knowledge Base' Input")
        # gr.Interface(
        #     fn=rag,
        #     inputs=[
        #         gr.Textbox(lines=2, placeholder="Enter your question here..."),
        #         gr.Checkbox(label="Stream output")
        #     ],
        #     outputs=[
        #         gr.Textbox(label="Generated Response"),
        #         gr.Textbox(label="Tokens per second"),
        #         gr.Textbox(label="Resource Usage")
        #     ],
        #     title="RAG Q&A System with GPU Acceleration and Resource Monitoring",
        #     description="Ask a question and get an answer based on the retrieved context. The response is generated using a GPU-accelerated model. Resource usage is logged at the end of generation."
        # )

            gr.Interface(
                fn=process_query,
                inputs=[
                    gr.Textbox(lines=2, placeholder="Enter your question here..."),
                    gr.Checkbox(label="Use RAG"),
                    gr.Checkbox(label="Stream output")
                ],
                outputs=[
                    gr.Textbox(label="Generated Response"),
                    gr.Textbox(label="Tokens per second"),
                    gr.Textbox(label="RAM Usage"),
                    gr.Textbox(label="Referenced Documents")
                ],
                title="RAG/Non-RAG Q&A System",
                description="Ask a question with or without using RAG. The response is generated using a GPU-accelerated model. RAM usage and referenced document IDs (for RAG) are logged."
            )

        with gr.Tab("Any Request to Qwen2-0.5B"):
            gr.HTML("Placeholder for https://huggingface.co/h2oai/h2o-danube3-500m-chat-GGUF as alternative")
            gr.HTML("Placeholder for qwen 2 72b as alternative use checkbox and gradio client api call")
            gr.Markdown("# Qwen-0.5B-Instruct Language Model")
            gr.Markdown("This demo uses the Qwen-0.5B-Instruct model to generate responses based on your input.")
            gr.HTML("Example prompts: <br>I am writing a story about a chef. please write dishes to appear on the menu. <br>What are the most common decisions that a chef story would include? <br>What are the kinds problems that a chef story would include? <br>What are the kinds of out of reach goals that a chef story would include? <br>Continue this config - Paste any complete block of the config")
            
            with gr.Row():
                with gr.Column():
                    llmguide_prompt = gr.Textbox(lines=2, placeholder="Enter your prompt here...")
                    llmguide_stream_checkbox = gr.Checkbox(label="Enable streaming")
                    llmguide_submit_button = gr.Button("Generate")
                
                with gr.Column():
                    llmguide_output = gr.Textbox(lines=10, label="Generated Response")
                    llmguide_tokens_per_second = gr.Textbox(label="Tokens per Second")
            
            llmguide_submit_button.click(
                llmguide_generate_response,
                inputs=[llmguide_prompt, llmguide_stream_checkbox],
                outputs=[llmguide_output, llmguide_tokens_per_second],
            )

    with gr.Accordion("Decisions Creation to Story to Config Conversation", open=False):
        with gr.Tab("Timeline Guide for Config Generation or evelution"):
            with gr.Accordion("Empty Config shape for explaining to LLM", open=False):
                gr.HTML(f"placeholder for current empty JSON config shape")
                gr.HTML("Structural indicators of quality of config")
            with gr.Tab("Random Suggestions"):
                timeline_num_lists_slider = gr.Slider(minimum=1, maximum=len(all_idea_lists), step=1, label="Number of Lists to Consider", value=3)
                timeline_items_per_list_slider = gr.Slider(minimum=1, maximum=10, step=1, label="Items per List", value=3)
                timeline_generate_button = gr.Button("Generate Random Suggestions")
                timeline_output_text = gr.Textbox(label="Random Suggestions", lines=10)

                timeline_generate_button.click(
                    timeline_get_random_suggestions,
                    inputs=[timeline_num_lists_slider, timeline_items_per_list_slider],
                    outputs=[timeline_output_text]
                )
            with gr.Tab("Config Specific"): 
                gr.HTML("Timeline for making Timelines?")
                gr.HTML("Componets (outside Code Support for Config): Decisions (and context explanation), Nested Sections, Media (Especially to affect decisions), Replayability (GTA and Tekken type mechanics in text form), Theme integration (Modified Varibles that affect UI or config order)")
                gr.HTML("From Nothing <br>")
                gr.HTML("From Existing <br>")
            with gr.Tab("Existing Game Analysis"): 
                gr.HTML("Existing Games eg. GTA Heists - Same Map with overlapping branching narratives, Battlefront - Elites amongst Commoners, Tekken Casino (one mistake = 1/2 or 1/3 of your Resources) and Turn based: 'Tactics' type nintendo games, Chess (and any other tile based game) ")
                gr.HTML("Existing Game Rules for text - Cyberpunk RED, ")
            with gr.Tab("Multiplayer options"):
                gr.HTML("Community playthrough = Tally of players choices, Random item placed in a random location - first person to get it wins, Survival by location or characters met")
        with gr.Tab("Some Workflow Helpers (Removed as built into Semi-Auto)"):
            gr.HTML("Song / Random Scenario to 'full game' manual or auto is end goal ")
            gr.HTML("Main Priority is to make rails for the story to work on as a simplified view of a game is a linear path with loops (gameplay mechanics) <br> Below = Manual Edit (At bottom you can save the changes and copy it to test) <br>For LLM edit copy either the Timeline")
            gr.HTML("The problem is the assets generation isnt free and using spaces as api also clogs them (no way to know when ZeroGPUs are at lowest usage) so using iFrame is better for now <br> So worklfow -- Make Skeleton -- Use Iframe to get media -- then ask for details / story / gameplay loop ideas and add back to config")
            with gr.Tab("Schema First"):
                gr.HTML("Some Kinds of game skeletons ideas - Timelines, Graph as State machine paths, Economy ecosystem")
                gr.HTML("One prompt to be used to test models - <br>Please make 10 python lists for the types of media files and their purposes in a game and then use those lists to random generate a timeline of 20 items when the function is called <br>Great next suggest ways to improve this function to create better timelines")
                with gr.Accordion("Test for config to gradio components order - ignore for now", open=False ):
                    gr.Markdown("Asset Generation")
                    gr.HTML("Splits by new line - The idea here was to allow for saving the file ")
                    input_text = gr.Textbox(label="Input Text", lines=10)
                    output_group = gr.Group()
                    
                    @gr.render(inputs=input_text)
                    def update(text):
                        return show_elements(text)
                with gr.Accordion("Proto Config Assist"):
                    with gr.Accordion("Can copy in the Test Example State Machine tab - only linear path for now", open=False):
                        gr.Markdown("# Story and Timeline Generator")
                        gr.Markdown("Click the button to generate a random timeline and story based on UI elements and story events. <br>Ask an LLM to use this to write a story around")
                        with gr.Row():
                                game_structure_output_text_with_media = gr.Code(language="json")
                                game_structure_output_text = gr.Code(language="json")
                    with gr.Accordion("JSON with no edits"):
                        with gr.Row():
                            timeline_output_with_assets = gr.Textbox(label="Timeline with Assets Considered", lines=20)
                            timeline_output = gr.Textbox(label="Timeline (Order might be different for now)", lines=20)
                            story_output = gr.Textbox(label="Generated Story (Order might be different for now)", lines=20)
                    with gr.Row():
                        generate_no_ui_timeline_points = gr.Slider(minimum=1, value=10, step=1, maximum=30, label="Choose the amount of ui timeline points")
                        generate_no_media_timeline_points = gr.Slider(minimum=1, value=5, step=1, maximum=30, label="Choose the amount of media timeline points")
                        generate_with_media_check = gr.Checkbox(label="Generate with media", value=True)
                    generate_button = gr.Button("Generate Story and Timeline")

                    @gr.render(inputs=game_structure_output_text_with_media)
                    def update(game_structure_output_text_with_media):
                        return show_elements_json_input(game_structure_output_text_with_media)
                    
                    generate_button.click(generate_story_and_timeline, inputs=[generate_no_ui_timeline_points, generate_no_media_timeline_points, generate_with_media_check], outputs=[timeline_output_with_assets, timeline_output, story_output, game_structure_output_text_with_media, game_structure_output_text])
                    
            with gr.Tab("Asset First"):
                gr.HTML("Make Asset and make the transitions using LLM")

            with gr.Tab("Export Options"):
                gr.HTML("Placeholder - My Custom JS, Playcanvas, Unreal Engine")

        with gr.Tab("Config Writing Considerations"):
            gr.HTML("Player Stats, Inventory and NPCS not implemented yet, so traversal type games best aka graph like structures <br> Game is like a universal translator so any concept can be covered")

    with gr.Accordion("Existing Config Crafting Progression - click to open", open=False):
        with gr.Tab("Quick Ways to evaluate current config"): 
            gr.HTML("Ask SOTA LLMs This prompt: <br> This config is for a basic text based game engine. I dont have any structural metrics to assess the quality of the config. What JSON things can we look at to see if it may be too bland for a person testing the game? <br> Then Paste the Config with the prompt")
            gr.HTML("""Original Claude 3.5 Sonnet Response snippets: <br>
Limited state variety: With only 13 states across 5 locations, the game might feel short and lacking in diversity. Consider adding more locations or states within existing locations.
Low average choices: An average of 1.92 choices per state might make the game feel linear. Increasing the number of choices in more states could improve player engagement.
Limited consequences: Only 3 states have consequences, which might make player choices feel less impactful. Adding more consequences could increase the sense of agency.
Short descriptions: The average description length of 13.15 words might not provide enough detail to immerse players. Consider expanding descriptions to create a richer narrative.
Lack of media: No states currently use media elements, which could make the game feel less engaging. Adding images, sound effects, or other media could enhance the player experience.
Limited narrative branching: While there are some loops and choices, the overall structure is relatively linear. Adding more branching paths could increase replayability and player interest.

To make the game less bland, consider:

Adding more states and locations
Increasing the number of choices in each state
Implementing more consequences for player actions
Expanding descriptions to create a richer narrative
Incorporating media elements
Creating more diverse paths through the game""")
            
        with gr.Tab("Main areas of considerations"):
            with gr.Tab("Mermaid Graphs and Nesting"):
                gr.HTML("Claude Artifacts to illustrate nested structure brainstorms - <br> https://claude.site/artifacts/4a910d81-1541-49f4-8531-4f27fe56cd1e <br> https://claude.site/artifacts/265e9242-2093-46e1-9011-ed6ad938be90?fullscreen=false <br> ")
                gr.HTML("")
            with gr.Tab("Structural Inspirations"):
                gr.HTML("GTA Heists - Replayability and stakes, Tekken - 2/3 mistakes = lost round ")
                gr.HTML("Sports Scores, ")
            with gr.Tab("Themes"):
                gr.HTML("")

# import originalconfigatbeinningofthisspace, claude3_5_06072024configtips, tipsupdatedconfigatbeinningofthisspace from relatively_constant_variables

        with gr.Tab("Improvement of the default config"):
            gr.HTML("Example of how to advance a game config with LLM - end goal is to have automatic worflow that takes these considerations into account <br> Things missing from the game engine - Economics and Basic Politics (NPC affiliation)")
            gr.HTML("Suggestions from claude 3.5 on how to change config")
            display_originalconfigatbeinningofthisspace = originalconfigatbeinningofthisspace.replace(' ', '&nbsp;').replace('\n', '<br>') 
            display_claude3_5_06072024configtips = claude3_5_06072024configtips.replace(' ', '&nbsp;').replace('\n', '<br>')
            display_tipsupdatedconfigatbeinningofthisspace = tipsupdatedconfigatbeinningofthisspace.replace(' ', '&nbsp;').replace('\n', '<br>') 
            gr.HTML("""<div style="display: flex; justify-content: space-between; height: 900px; overflow: auto; ">
                <div style="flex: 1; margin: 0 10px; padding: 20px;">
                    """ + display_originalconfigatbeinningofthisspace + """
                </div>
                <div style="flex: 1; margin: 0 10px; padding: 20px; width: 50%">
                    """ + display_claude3_5_06072024configtips + """
                </div>
                <div style="flex: 1; margin: 0 10px; padding: 20px;">
                    """ + display_tipsupdatedconfigatbeinningofthisspace + """
                </div>
            </div>""")


    with gr.Accordion("Temporary Asset Management Assist - click to open", open=False):
        gr.HTML("Make Files and Text ideas for the field and paste <br>When Space is restarted it will clear - zip export and import will be added later")
        with gr.Accordion("Upload Files for config"):
            gr.Markdown("# Media Saver and Explorer (refresh file list to be resolved - for now upload all files and reload the space - they persist as long as the space creator doesnt reset/update the space - will add manual clear options later)")
            with gr.Tab("Upload Files"):
                file_input = gr.File(label="Choose File to Upload")
                save_output = gr.Textbox(label="Upload Status")

            with gr.Tab("File Explorer"):
                
                file_explorer = gr.FileExplorer(
                    root_dir=SAVE_DIR,
                    glob="*.*",
                    file_count="single",
                    height=300,
                    label="Select a file to view"
                )
                with gr.Row():
                    refresh_button = gr.Button("Refresh", scale=1)
                    view_button = gr.Button("View File")
                image_output = gr.Image(label="Image Output", type="pil")
                audio_output = gr.Audio(label="Audio Output")
                video_output = gr.Video(label="Video Output")
                error_output = gr.Textbox(label="Error")

            file_input.upload(
                save_file,
                inputs=file_input,
                outputs=[save_output, file_explorer, file_input]
            )

            view_button.click(
                view_file,
                inputs=file_explorer,
                outputs=[image_output, audio_output, video_output, error_output]
            )

            refresh_button.click(
                refresh_file_explorer,
                outputs=file_explorer
            )

            with gr.Tab("Batch add files to config"):
                gr.HTML("Placeholder for Config parser to allow dropdowns for the media parts of the config inserted to make assigning media quick")
                gr.HTML("Placeholder for Config parser to allow for current zerospace creation and placement into the config (LLM can give list of media but still have to figure out workflow from there)")

            gr.HTML("Placeholder for clearing uploaded assets (public space and temporary persistence = sharing and space problems)")
    
    with gr.Tab("Test and Edit Config"):
        gr.HTML("The main issue is frequent changes add more chances for bugs in how - manual and auto refer mainly to ensuring correct JSON format ")
        with gr.Tab("Full Manual - Test Example State Machine"): 
            with gr.Tab("Config Without Assets"):
                with gr.Row():
                    with gr.Column(scale=2):
                        gr.Markdown("# Text-based Adventure Game")

                        description = gr.Textbox(label="Current Situation", lines=4, value=initgameinfo[0])
                        choices = gr.Radio(label="Your Choices", choices=initgameinfo[1])
                        submit_btn = gr.Button("Make Choice")
                        game_log = gr.Textbox(label="Game Log", lines=20, value=initgameinfo[2])
                        game_session = gr.State(value=initgameinfo[3])
                        submit_btn.click(
                            make_choice,
                            inputs=[choices, game_session],
                            outputs=[description, choices, game_log, game_session]
                        )
                    with gr.Column(scale=1):
                        gr.Markdown("# Debugging")
                        error_box = gr.Textbox(label="Path Errors", lines=4, value=path_errors)
                        with gr.Accordion("Config (Game Spoiler and Example for llm to remix)", open=False):
                            custom_config = gr.Textbox(label="Custom Configuration (JSON)", value=json.dumps(all_states, default=lambda o: o.__dict__, indent=2), lines=8)
                            custom_configbtn = gr.Button("Load Custom Config")

                            custom_configbtn.click(
                                load_game,
                                inputs=[custom_config],
                                outputs=[error_box, game_log, description, choices, game_session, custom_config]
                            )
            with gr.Tab("Config With Assets"):
                gr.HTML("Placeholder as not complete yet (still only text, current issue is how to switch gradio output, maybe output is gr.Group and we constantly add the appropriate gr for each file type? What about multi file types on one state?)")
                with gr.Row():
                    with gr.Column(scale=2):
                        gr.Markdown("# Text-based Adventure Game")

                        wadescription = gr.Textbox(label="Current Situation", lines=4, value=initgameinfo[0])
                        wamediabool = gr.State(value=True)
                        wamedia = gr.State(["testmedia/Stable Audio - Raindrops, output.wav"])

                        @gr.render(inputs=wamedia)
                        def dynamic_with_media(media_items):
                            print(media_items)
                            with gr.Group() as wamediagrouping: 
                                gr.HTML("Placeholder to load all media tests - still need to test clearing media on ")
                                if media_items == []:
                                    gr.Markdown("No media items to display.")
                                else:
                                    for item in media_items:
                                        render = create_media_component(item)
                            
                            return wamediagrouping
                        
                        wachoices = gr.Radio(label="Your Choices", choices=initgameinfo[1])
                        wasubmit_btn = gr.Button("Make Choice")
                        wagame_log = gr.Textbox(label="Game Log", lines=20, value=initgameinfo[2])
                        wagame_session = gr.State(value=initgameinfo[3])
                        wasubmit_btn.click(
                            make_choice,
                            inputs=[wachoices, wagame_session, wamediabool],
                            outputs=[wadescription, wachoices, wagame_log, wagame_session, wamedia]
                        )
                    with gr.Column(scale=1):
                        gr.Markdown("# Debugging")
                        waerror_box = gr.Textbox(label="Path Errors", lines=4, value=path_errors)
                        with gr.Accordion("Config (Game Spoiler and Example for llm to remix)", open=False):
                            wacustom_config = gr.Textbox(label="Custom Configuration (JSON)", value=json.dumps(all_states, default=lambda o: o.__dict__, indent=2), lines=8)
                            wacustom_configbtn = gr.Button("Load Custom Config")

                            wacustom_configbtn.click(
                                load_game,
                                inputs=[wacustom_config, wamediabool],
                                outputs=[waerror_box, wagame_log, wadescription, wachoices, wacustom_config, wagame_session, wamedia]
                            )
                
            with gr.Tab("Config With Minimal 3D considered"):
                gr.HTML("Placeholder for Config with 3D assets")

        with gr.Tab("Semi-Auto - Edit config while playing game"):
            gr.HTML("-- Incomplete -- Issue here is updating all variables <br> Current problem is passing values from rendered items to the config box <br>Generate Timline also makes config without mmedia key <br>Need a way have dropdowns for the filelist and transitions eg. changing transitions must auto update choices <br>Config to components has hardcoded variables based on the auto gen so changes break it")
            with gr.Row():
                with gr.Column(scale=1):
                    with gr.Group():
                        gr.Markdown("# Text-based Adventure Game")

                        ewpwadescription = gr.Textbox(label="Current Situation", lines=4, value=initgameinfo[0])
                        ewpwamediabool = gr.State(value=True)
                        ewpwamedia = gr.State(["testmedia/Stable Audio - Raindrops, output.wav"])

                        @gr.render(inputs=ewpwamedia)
                        def dynamic_with_media(media_items):
                            print(media_items)
                            with gr.Group() as ewpwamediagrouping: 
                                gr.HTML("Placeholder to load all media tests - still need to test clearing media on ")
                                if media_items == []:
                                    gr.Markdown("No media items to display.")
                                else:
                                    for item in media_items:
                                        render = create_media_component(item)
                            
                            return ewpwamediagrouping
                        
                        ewpwachoices = gr.Radio(label="Your Choices", choices=initgameinfo[1])
                        ewpwasubmit_btn = gr.Button("Make Choice")
                        ewpwagame_log = gr.Textbox(label="Game Log", lines=20, value=initgameinfo[2])
                        ewpwagame_session = gr.State(value=initgameinfo[3])
                        ewpwasubmit_btn.click(
                            make_choice,
                            inputs=[ewpwachoices, ewpwagame_session, ewpwamediabool],
                            outputs=[ewpwadescription, ewpwachoices, ewpwagame_log, ewpwagame_session, ewpwamedia]
                        )
                with gr.Column(scale=1):
                    gr.Markdown("# Debugging")
                    ewpwaerror_box = gr.Textbox(label="Path Errors", lines=4, value=path_errors)
                    ewpwacustom_config = gr.Textbox(label="Custom Configuration (JSON)", value=json.dumps(all_states, default=lambda o: o.__dict__, indent=2), lines=8)
                    ewpwacustom_configbtn = gr.Button("Load Custom Config")

                    ewpwacustom_configbtn.click(
                        load_game,
                        inputs=[ewpwacustom_config, ewpwamediabool],
                        outputs=[ewpwaerror_box, ewpwagame_log, ewpwadescription, ewpwachoices, ewpwacustom_config, ewpwagame_session, ewpwamedia]
                    )
                    with gr.Accordion("Can copy in the Test Example State Machine tab - only linear path for now", open=False):
                        gr.Markdown("# Story and Timeline Generator")
                        gr.Markdown("Click the button to generate a random timeline and story based on UI elements and story events. <br>Ask an LLM to use this to write a story around")
                        with gr.Row():
                            ewpgame_structure_output_text_with_media = gr.Code(language="json")
                            ewpgame_structure_output_text = gr.Code(language="json")
                        with gr.Row():
                            ewptimeline_output_with_assets = gr.Textbox(label="Timeline with Assets Considered", lines=20)
                            ewptimeline_output = gr.Textbox(label="Timeline (Order might be different for now)", lines=20)
                            ewpstory_output = gr.Textbox(label="Generated Story (Order might be different for now)", lines=20)
                    with gr.Row():
                        ewpgenerate_no_ui_timeline_points = gr.Slider(minimum=1, value=10, step=1, maximum=30, label="Choose the amount of ui timeline points")
                        ewpgenerate_no_media_timeline_points = gr.Slider(minimum=1, value=5, step=1, maximum=30, label="Choose the amount of media timeline points")
                        ewpgenerate_with_media_check = gr.Checkbox(label="Generate with media", value=True)
                    ewpgenerate_button = gr.Button("Generate Story and Timeline")

                    @gr.render(inputs=ewpwacustom_config) #ewpgame_structure_output_text_with_media
                    def update(ewpwacustom_config):
                        return show_elements_json_input(ewpwacustom_config)
                    
                    ewpgenerate_button.click(generate_story_and_timeline, inputs=[ewpgenerate_no_ui_timeline_points, ewpgenerate_no_media_timeline_points, ewpgenerate_with_media_check], outputs=[ewptimeline_output_with_assets, ewptimeline_output, ewpstory_output, ewpwacustom_config, ewpgame_structure_output_text]) #ewpgame_structure_output_text_with_media, ewpgame_structure_output_text])

    with gr.Tab("Asset Generation Considerations"):
        gr.HTML("Licenses for the spaces still to be evaluated - June 2024 <br> Users to follow with cool spaces - https://huggingface.co/fffiloni, https://huggingface.co/artificialguybr, https://huggingface.co/radames, https://huggingface.co/multimodalart, ")

        with gr.Accordion("LLM HF Spaces/Sites (Click Here to Open) - Ask for a story and suggestions based on the autoconfig", open=False):
            with gr.Row():
                linktochat = gr.Dropdown(choices=["https://labs.perplexity.ai/", "https://chat.lmsys.org", "https://sdk.vercel.ai/docs", "https://qwen-qwen-max-0428.hf.space", "https://cohereforai-c4ai-command-r-plus.hf.space", "https://huggingface.co/spaces/eswardivi/Phi-3-mini-128k-instruct", "https://eswardivi-phi-3-mini-4k-instruct.hf.space", "https://cyzgab-catch-me-if-you-can.hf.space", "https://snowflake-snowflake-arctic-st-demo.hf.space", "https://qwen-qwen1-5-110b-chat-demo.hf.space", "https://ysharma-chat-with-meta-llama3-8b.hf.space", "https://databricks-dbrx-instruct.hf.space", "https://qwen-qwen1-5-moe-a2-7b-chat-demo.hf.space", "https://cohereforai-c4ai-command-r-v01.hf.space", "https://ehristoforu-mixtral-46-7b-chat.hf.space", "https://stabilityai-stablelm-2-1-6b-zephyr.hf.space", "https://qwen-qwen1-5-72b-chat.hf.space", "https://deepseek-ai-deepseek-coder-7b-instruct.hf.space", "https://01-ai-yi-34b-chat.hf.space", "https://ysharma-zephyr-playground.hf.space", "https://huggingfaceh4-zephyr-chat.hf.space", "https://osanseviero-mistral-super-fast.hf.space", "https://artificialguybr-qwen-14b-chat-demo.hf.space", "https://huggingface-projects-llama-2-7b-chat.hf.space", "https://ysharma-explore-llamav2-with-tgi.hf.space", "https://mosaicml-mpt-30b-chat.hf.space", "https://huggingfaceh4-falcon-chat.hf.space", "https://uwnlp-guanaco-playground-tgi.hf.space", "https://stabilityai-stablelm-tuned-alpha-chat.hf.space", "https://mosaicml-mpt-7b-storywriter.hf.space", "https://huggingfaceh4-starchat-playground.hf.space", "https://bigcode-bigcode-playground.hf.space", "https://mosaicml-mpt-7b-chat.hf.space", "https://huggingchat-chat-ui.hf.space", "https://togethercomputer-openchatkit.hf.space"], label="Choose/Cancel type any .hf.space link here (can also type a link)'", allow_custom_value=True)
                chatspacebtn = gr.Button("Use the chosen URL to load interface with a chat model. For sdk.vercel click the chat button on the top left. For lymsys / chat arena copy the link and use a new tab")
            chatspace = gr.HTML("Chat Space Chosen will load here")
            chatspacebtn.click(display_website, inputs=linktochat, outputs=chatspace)

        with gr.Tab("Save files"):
            gr.HTML("For Dynamic events overnight or when player is not active what can LLMS edit? <br><br>eg. Waiting for a letter from a random npc can be decided by the llm <br>eg. Improved Stats on certain days (eg. bitrthday) <br>Privacy <br>User Directed DLC eg. Rockstar Editor with local llm guide")
            gr.HTML("Some ideas - In game websites eg. GTA esp stock markets, news; ")
            gr.HTML("Placeholder for huggingface spaces that can assist - https://huggingface.co/nvidia/Nemotron-4-340B-Instruct (Purpose is supposed to be synthetic data generation), https://huggingface.co/spaces/gokaygokay/Gemma-2-llamacpp ")
            gr.HTML("Placeholder for models small enough to run on cpu here in this space that can assist (9b and under) <br>initial floor for testing can be https://huggingface.co/spaces/Qwen/Qwen2-0.5B-Instruct, https://huggingface.co/spaces/Qwen/Qwen2-1.5b-instruct-demo, https://huggingface.co/spaces/stabilityai/stablelm-2-1_6b-zephyr, https://huggingface.co/spaces/IndexTeam/Index-1.9B, https://huggingface.co/microsoft/Phi-3-mini-4k-instruct")
            with gr.Tab("Diagrams"):
                gr.HTML("Claude 3.5 sonnet is very good with mermaid graphs - can used for maps, situational explanations")
            with gr.Tab("Maths"):
                gr.HTML("https://huggingface.co/spaces/AI-MO/math-olympiad-solver")

        with gr.Tab("Media Understanding"):
            gr.HTML("NPC Response Engines? Camera, Shopkeeper, Companion, Enemies, etc.")
            with gr.Accordion("Media understanding model Spaces/Sites (Click Here to Open)", open=False):
                with gr.Row():
                    linktomediaunderstandingspace = gr.Dropdown(choices=[ "--Weak Audio Understanding = Audio to text, Weak Video Understanding = Video to Image to Image Understanding", "https://skalskip-florence-2-video.hf.space", "https://kingnish-opengpt-4o.hf.space",
                                                                            "--Image Understanding--", "https://qnguyen3-nanollava.hf.space", "https://skalskip-better-florence-2.hf.space", ], 
                                                    label="Choose/Cancel type any .hf.space link here (can also type a link)'", allow_custom_value=True)
                    mediaunderstandingspacebtn = gr.Button("Use the chosen URL to load interface with a media understanding space")
                mediaunderstandingspace = gr.HTML("Mdeia Understanding Space Chosen will load here")
                mediaunderstandingspacebtn.click(display_website, inputs=linktomediaunderstandingspace, outputs=mediaunderstandingspace)


        with gr.Tab("Images"):
            with gr.Accordion("Image Gen or Animation HF Spaces/Sites (Click Here to Open) - Have to download and upload at the the top", open=False):
                # with gr.Tabs("General"):
                with gr.Row():
                    linktoimagegen = gr.Dropdown(choices=["Text-Interleaved", "https://ethanchern-anole.hf.space",
                                                            "--General--", "https://pixart-alpha-pixart-sigma.hf.space", "https://stabilityai-stable-diffusion-3-medium.hf.space", "https://prodia-sdxl-stable-diffusion-xl.hf.space", "https://prodia-fast-stable-diffusion.hf.space", "https://bytedance-hyper-sdxl-1step-t2i.hf.space",  "https://multimodalart-cosxl.hf.space", "https://cagliostrolab-animagine-xl-3-1.hf.space", "https://stabilityai-stable-diffusion.hf.space",
                                                            "--Speed--", "https://radames-real-time-text-to-image-sdxl-lightning.hf.space", "https://ap123-sdxl-lightning.hf.space",
                                                            "--LORA Support--", "https://artificialguybr-artificialguybr-demo-lora.hf.space", "https://artificialguybr-studio-ghibli-lora-sdxl.hf.space", "https://artificialguybr-pixel-art-generator.hf.space", "https://fffiloni-sdxl-control-loras.hf.space", "https://ehristoforu-dalle-3-xl-lora-v2.hf.space",
                                                            "--Image to Image--", "https://lllyasviel-ic-light.hf.space", "https://gparmar-img2img-turbo-sketch.hf.space",
                                                            "--Control of Pose--", "https://instantx-instantid.hf.space", "https://modelscope-transferanything.hf.space", "https://okaris-omni-zero.hf.space"
                                                            "--Control of Shapes--", "https://linoyts-scribble-sdxl-flash.hf.space",
                                                            "--Foreign Language Input--", "https://gokaygokay-kolors.hf.space"], label="Choose/Cancel type any .hf.space link here (can also type a link)'", allow_custom_value=True)
                    imagegenspacebtn = gr.Button("Use the chosen URL to load interface with a image generation model")
            
                imagegenspace = gr.HTML("Image Space Chosen will load here")
                imagegenspacebtn.click(display_website, inputs=linktoimagegen, outputs=imagegenspace)
            
            linkstobecollectednoembed = "https://artgan-diffusion-api.hf.space", "https://multimodalart-stable-cascade.hf.space", "https://google-sdxl.hf.space", "https://visionmaze-magic-me.hf.space", "https://segmind-segmind-stable-diffusion.hf.space", "https://simianluo-latent-consistency-model.hf.space",
            gr.HTML("Concept Art, UI elements, Static/3D Characters, Environments and Objects")
            gr.HTML("Image Caption =  https://huggingface.co/spaces/microsoft/Promptist, https://huggingface.co/spaces/gokaygokay/SD3-Long-Captioner, https://huggingface.co/spaces/gokaygokay/Florence-2,  ")
            gr.HTML("Images Generation Portraits = https://huggingface.co/spaces/okaris/omni-zero")
            gr.HTML("Images Generation General (3rd Party) =  https://www.craiyon.com/")
            gr.HTML("Images Generation Posters with text - https://huggingface.co/spaces/GlyphByT5/Glyph-SDXL-v2")
            gr.HTML("Placeholder for huggingface spaces that can assist - https://huggingface.co/spaces/EPFL-VILAB/4M, https://huggingface.co/spaces/EPFL-VILAB/MultiMAE ")
            gr.HTML("SVG Generation = Coding models / SOTA LLM ")
            gr.HTML("Images Generation - Upscaling - https://huggingface.co/spaces/gokaygokay/Tile-Upscaler")
            gr.HTML("Vision Models for descriptions <br> https://huggingface.co/spaces/gokaygokay/Florence-2 <br>https://huggingface.co/spaces/vilarin/VL-Chatbox - glm 4v 9b <br>")
            gr.HTML("Upscalers (save data transfer costs? highly detailed characters?) - https://huggingface.co/spaces/gokaygokay/AuraSR")
            gr.HTML("Placeholder for huggingface spaces that can assist ")
            gr.HTML("Placeholder for models small enough to run on cpu here in this space that can assist")

        with gr.Tab("Video"):
            with gr.Accordion("Video Spaces/Sites (Click Here to Open)", open=False):
                with gr.Row():
                    linktovideogenspace = gr.Dropdown(choices=["--Genral--", "https://kadirnar-open-sora.hf.space",
                                                               "--Talking Portrait--", "https://fffiloni-tts-hallo-talking-portrait.hf.space",  
                                                             "--Gif / ImgtoImg based video--", "https://wangfuyun-animatelcm-svd.hf.space", "https://bytedance-animatediff-lightning.hf.space", "https://wangfuyun-animatelcm.hf.space", "https://guoyww-animatediff.hf.space",], 
                                                    label="Choose/Cancel type any .hf.space link here (can also type a link)'", allow_custom_value=True)
                    videogenspacebtn = gr.Button("Use the chosen URL to load interface with video generation")
                videogenspace = gr.HTML("Video Space Chosen will load here")
                videogenspacebtn.click(display_website, inputs=linktovideogenspace, outputs=videogenspace)

            gr.HTML("Cutscenes, Tutorials, Trailers")
            gr.HTML("Portrait Video eg. Solo Taking NPC - https://huggingface.co/spaces/fffiloni/tts-hallo-talking-portrait (Image + Audio and combination)  https://huggingface.co/spaces/KwaiVGI/LivePortrait (Non verbal communication eg. in a library, when running from a pursuer)")
            gr.HTML("Placeholder for huggingface spaces that can assist - https://huggingface.co/spaces/KingNish/Instant-Video, https://huggingface.co/spaces/multimodalart/stable-video-diffusion, https://huggingface.co/spaces/multimodalart/stable-video-diffusion")
            gr.HTML("Placeholder for models small enough to run on cpu here in this space that can assist")
            gr.HTML("3rd Party / Closed Source - https://runwayml.com/ <br>")
            with gr.Tab("Animations (for lower resource use)"):
                gr.HTML("Characters, Environments, Objects")
                gr.HTML("Placeholder for huggingface spaces that can assist - image as 3d object in video https://huggingface.co/spaces/ashawkey/LGM")
                gr.HTML("Placeholder for models small enough to run on cpu here in this space that can assist")

        with gr.Tab("Audio"):
            with gr.Accordion("Audio Spaces/Sites (Click Here to Open)", open=False):
                with gr.Row():
                    linktoaudiiogenspace = gr.Dropdown(choices=["General", "https://artificialguybr-stable-audio-open-zero.hf.space", "",
                                                                "--Talking Portrait--","https://fffiloni-tts-hallo-talking-portrait.hf.space"], 
                                                    label="Choose/Cancel type any .hf.space link here (can also type a link)'", allow_custom_value=True)
                    audiiogenspacebtn = gr.Button("Use the chosen URL to load interface with audio generation")
                audiiogenspace = gr.HTML("Audio Space Chosen will load here")
                audiiogenspacebtn.click(display_website, inputs=linktoaudiiogenspace, outputs=audiiogenspace)
            gr.HTML("Music - Background, Interactive, Cutscene, Menu <br>Sound Effects - Environment, character, action (environmental triggered by user eg. gun), UI <br>Speech - Dialouge, narration, voiceover <br>The new render function means the Config can be made and iframe/api functions can be ordered as neccessary based on the part of the config that needs it to streamline workflows based on current state of config ")
            gr.HTML("Placeholder for huggingface spaces that can assist")
            gr.HTML("Audio Sound Effects - https://huggingface.co/spaces/artificialguybr/Stable-Audio-Open-Zero")
            gr.HTML("Voices - Voice clone eg. actors part of your project - https://huggingface.co/spaces/tonyassi/voice-clone")
            gr.HTML("Placeholder for models small enough to run on cpu here in this space that can assist")
            gr.HTML("3rd Party / Closed Source - https://suno.com/ <br>https://www.udio.com/")

        with gr.Tab("3D"):
            with gr.Accordion("3D Model Spaces/Sites (Click Here to Open)", open=False):
                with gr.Row():
                    linktoThreedModel = gr.Dropdown(choices=["https://wuvin-unique3d.hf.space", "https://stabilityai-triposr.hf.space", "https://hysts-shap-e.hf.space", "https://tencentarc-instantmesh.hf.space", "https://ashawkey-lgm.hf.space", "https://dylanebert-lgm-mini.hf.space", "https://dylanebert-splat-to-mesh.hf.space", "https://dylanebert-multi-view-diffusion.hf.space"], label="Choose/Cancel type any .hf.space link here (can also type a link)'", allow_custom_value=True)
                    ThreedModelspacebtn = gr.Button("Use the chosen URL to load interface with a 3D model")
                ThreedModelspace = gr.HTML("3D Space Chosen will load here")
                ThreedModelspacebtn.click(display_website, inputs=linktoThreedModel, outputs=ThreedModelspace)
            gr.HTML("Characters, Environments, Objects")
            gr.HTML("Placeholder for huggingface spaces that can assist - https://huggingface.co/spaces/dylanebert/3d-arena")
            gr.HTML("Closed Source - https://www.meshy.ai/")

        with gr.Tab("Fonts"):
            gr.HTML("Style of whole game, or locations, or characters")
            gr.HTML("Placeholder for huggingface spaces that can assist - there was a space that could make letter into pictures based on the prompt but I cant find it now")
            gr.HTML("Placeholder for models small enough to run on cpu here in this space that can assist")
        
        with gr.Tab("Shaders and related"):
            gr.HTML("Any output that is not understood by the common person can be used as special effects eg. depth map filters on images etc.")
            gr.HTML("Post-processing Effects, material effects, Particle systems, visual feedback")
            gr.HTML("Visual Effects - eg. explosion can turn all items white for a moment, losing conciousness blurs whole screen")
            gr.HTML("Placeholder for huggingface spaces that can assist - https://huggingface.co/spaces/EPFL-VILAB/4M, https://huggingface.co/spaces/EPFL-VILAB/MultiMAE ")
            gr.HTML("Placeholder for models small enough to run on cpu here in this space that can assist")

        with gr.Tab("Demos on Social Media for inspiration"):
            gr.HTML("Social media that shows possiblities")
            gr.HTML("https://x.com/blizaine")

    with gr.Tab("Basic Game Engine Mechanics"):
        gr.HTML("Placeholder for explanations of Player and Game Session")

    with gr.Tab("LLM play testing"):
        gr.HTML("LLM can read the contents in full and give critiques but they can also play the game if you make a api interface - gradio allows this in the form of gradio client but you can also reroute the user inputs to function calling")

    with gr.Tab("Custom JS Config Creator"):
        gr.HTML("-- Incomplete -- Companion Space for zerogpu / client api workflow planning for a way to send a zip to the Basic Game Engine at the bottom of https://huggingface.co/spaces/KwabsHug/TestSvelteStatic (Also to test how much can be done majority on cpu)")
        with gr.Tab("Simple Config Creator"):
            inventory_items = gr.State([])
            skills_items = gr.State([])
            objectives_items = gr.State([])
            targets_items = gr.State([])

            with gr.Tabs():
                with gr.TabItem("Inventory"):
                    inventory_type = gr.Textbox(label="Type")
                    inventory_name = gr.Textbox(label="Name")
                    inventory_description = gr.Textbox(label="Description")
                    add_inventory = gr.Button("Add Inventory Item")
                    inventory_textbox = gr.JSON(label="Inventory Items", value=[])

                with gr.TabItem("Skills"):
                    skills_branch = gr.Textbox(label="Branch")
                    skills_name = gr.Textbox(label="Name")
                    skills_learned = gr.Dropdown(choices=["True", "False"], label="Learned")
                    add_skill_button = gr.Button("Add Skill")
                    skills_textbox = gr.JSON(label="Skills", value=[])

                with gr.TabItem("Objectives"):
                    objectives_id = gr.Textbox(label="ID")
                    objectives_name = gr.Textbox(label="Name")
                    objectives_complete = gr.Dropdown(choices=["True", "False"], label="Complete")
                    add_objective_button = gr.Button("Add Objective")
                    objectives_textbox = gr.JSON(label="Objectives", value=[])

                with gr.TabItem("Targets"):
                    targets_name = gr.Textbox(label="Name")
                    targets_x = gr.Textbox(label="X Coordinate")
                    targets_y = gr.Textbox(label="Y Coordinate")
                    targets_collisionType = gr.Textbox(label="Collision Type")
                    targets_collisiontext = gr.Textbox(label="Collision Text")
                    add_target_button = gr.Button("Add Target")
                    targets_textbox = gr.JSON(label="Targets", value=[])

                with gr.TabItem("Placeholders for Modal Target"):
                    gr.HTML("Placeholder")

                with gr.TabItem("Placeholders for State Machine Modal Target"):
                    gr.HTML("Placeholder")

                with gr.TabItem("Placeholders for Background"):
                    gr.HTML("Placeholder")

            config_output = gr.JSON(label="Updated Configuration")

            @gr.render(inputs=[inventory_items, skills_items, objectives_items, targets_items]) #, outputs=config_output)
            def aggregate_config(inventory, skills, objectives, targets):
                config = default_config.copy()
                config['inventory'] = inventory
                config['skills'] = skills
                config['objectives'] = objectives
                config['targets'] = targets
                return config

            add_inventory.click(add_inventory_item, inputs=[inventory_items, inventory_type, inventory_name, inventory_description], outputs=inventory_textbox)
            add_inventory.click(aggregate_config, inputs=[inventory_items, skills_items, objectives_items, targets_items], outputs=config_output)

            add_skill_button.click(add_skill, inputs=[skills_items, skills_branch, skills_name, skills_learned], outputs=skills_textbox)
            add_skill_button.click(aggregate_config, inputs=[inventory_items, skills_items, objectives_items, targets_items], outputs=config_output)

            add_objective_button.click(add_objective, inputs=[objectives_items, objectives_id, objectives_name, objectives_complete], outputs=objectives_textbox)
            add_objective_button.click(aggregate_config, inputs=[inventory_items, skills_items, objectives_items, targets_items], outputs=config_output)

            add_target_button.click(add_target, inputs=[targets_items, targets_name, targets_x, targets_y, targets_collisionType, targets_collisiontext], outputs=targets_textbox)
            add_target_button.click(aggregate_config, inputs=[inventory_items, skills_items, objectives_items, targets_items], outputs=config_output)
        
        with gr.Tab("Advanced Config Creator"):
            gr.HTML("Config with More than text and images")
                    
    with gr.Tab("LLM/Robotics as custom controllers Considerations"):
        gr.HTML("Controls changed the scope of the game eg. mouse vs keyboard vs console controller vs remote vs touch screen <br>LLM can be vision/surveilance based controler (eg. MGS/GTA camera gauged by an actual camera in real life) or it can be a companion (offline/off console game progrssion ideas)")
        gr.HTML("Robotics - https://github.com/OpenTeleVision/TeleVision https://www.stereolabs.com/")

    with gr.Tab("Other Considerations"):
        with gr.Tab("General"):
            gr.HTML("Experiment for https://huggingface.co/spaces/ysharma/open-interpreter/blob/main/app.py inplementation with gradio client api")
            gr.HTML("Some conderations for future integration: https://huggingface.co/spaces/dylanebert/3d-arena, https://github.com/fudan-generative-vision/hallo")

            gr.HTML("Useful Spaces and links: https://huggingface.co/spaces/artificialguybr/Stable-Audio-Open-Zero https://huggingface.co/spaces/stabilityai/TripoSR https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD https://huggingface.co/spaces/multimodalart/face-to-all https://huggingface.co/spaces/facebook/MusicGen https://huggingface.co/spaces/Doubiiu/tooncrafter")
            
            gr.HTML("langchain docs as awareness for alot of the integration use cases and providers that are possible - https://python.langchain.com/v0.2/docs/integrations/tools/")

            gr.HTML("https://huggingface.co/spaces/linoyts/scribble-sdxl-flash as map planner")

            gr.HTML("---------------------------------------Gameplay Ideas-------------------------------")
            gr.HTML("https://huggingface.co/spaces/Lin-Chen/ShareCaptioner-Video - game use example police questions a event with multiple eye witnesses needs to give as close to the caption description to win")
        with gr.Tab("State management through huggingface?"):
            gr.HTML("Huggingface as the login provider? - https://huggingface.co/docs/hub/en/spaces-oauth https://huggingface.co/docs/hub/en/oauth, persistent storage - https://huggingface.co/docs/hub/en/spaces-storage")
        with gr.Tab("Finetuning options"):
            gr.HTML("Price - https://openpipe.ai/pricing")
        with gr.Tab("Backend and/or Hosting?"):
            gr.HTML("Prototyping and freemium <br>free api <br>HF Pro subscription")
            gr.HTML("GPU (Data privacy) = No Rate limits? - https://replicate.com/pricing, https://lambdalabs.com/service/gpu-cloud https://huggingface.co/pricing#endpoints https://tensordock.com/cloud-gpus")
            gr.HTML("Speed - Groq, SambaNova, https://www.etched.com/announcing-etched ")
            gr.HTML("Price - Coding - https://aider.chat/docs/leaderboards/ -  https://www.deepseek.com/ 0.3 per million - is this per token or chinese character as that means converting code to chinese if possible can save api cost?")
    with gr.Tab("Asset loading test"):
        gr.HTML("SDXL (linoyts/scribble-sdxl-flash), SVD and Stable Audio used for the test assets (For commercial use need a licence)")
        with gr.Row():
            gr.Image(value="testmedia/Flash scribble SDXL - random squiggles as roads.webp")
            gr.Video(value="testmedia/SVD - random squiggles as roads video 004484.mp4")
            gr.Audio(value="testmedia/Stable Audio - Raindrops, output.wav")
        gr.HTML(TestmedialoadinHTML) # imported from relatively_constant_variables

demo.queue().launch()