Update 1app.py

1app.py

@@ -1,156 +1,431 @@
-from huggingface_hub import InferenceClient
-import gradio as gr
-import random
-import prompts  # Ensure this module is correctly imported
-
-client = InferenceClient(
-    "mistralai/Mixtral-8x7B-Instruct-v0.1"
-)
-
-def format_prompt(message, history):
-    prompt = "<s>"
-    for user_prompt, bot_response in history:
-        prompt += f"[INST] {user_prompt} [/INST]"
-        prompt += f" {bot_response}</s> "
-    prompt += f"[INST] {message} [/INST]"
-    return prompt
-
-agents = [
-    "WEB_DEV",
-    "AI_SYSTEM_PROMPT",
-    "PYTHON_CODE_DEV",
-    "CODE_REVIEW_ASSISTANT",
-    "CONTENT_WRITER_EDITOR",
-    "QUESTION_GENERATOR",
-    "HUGGINGFACE_FILE_DEV",
-]
-
-def generate(
-        prompt, history, agent_name=agents[0], sys_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
-):
-    seed = random.randint(1, 1111111111111111)
-
-    # Ensure correct agent selection
-    agent = prompts.WEB_DEV_SYSTEM_PROMPT
-    if agent_name == "WEB_DEV":
-        agent = prompts.WEB_DEV_SYSTEM_PROMPT
-    elif agent_name == "CODE_REVIEW_ASSISTANT":
-        agent = prompts.CODE_REVIEW_ASSISTANT
-    elif agent_name == "CONTENT_WRITER_EDITOR":
-        agent = prompts.CONTENT_WRITER_EDITOR
-    elif agent_name == "SOCIAL_MEDIA_MANAGER":
-        agent = prompts.SOCIAL_MEDIA_MANAGER        
-    elif agent_name == "AI_SYSTEM_PROMPT":
-        agent = prompts.AI_SYSTEM_PROMPT
-    elif agent_name == "PYTHON_CODE_DEV":
-        agent = prompts.PYTHON_CODE_DEV        
-    elif agent_name == "QUESTION_GENERATOR":
-        agent = prompts.QUESTION_GENERATOR 
-    elif agent_name == "HUGGINGFACE_FILE_DEV":
-        agent = prompts.HUGGINGFACE_FILE_DEV          
-    
-    system_prompt = agent
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-
-    generate_kwargs = dict(
-        temperature=temperature,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        do_sample=True,
-        seed=seed,
+import os
+import subprocess
+import streamlit as st
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import black
+from pylint import lint
+from io import StringIO
+
+# Set Hugging Face repository URL and project root path
+HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/Mistri"
+PROJECT_ROOT = "projects"
+AGENT_DIRECTORY = "agents"
+
+# Global state for session management
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+if 'terminal_history' not in st.session_state:
+    st.session_state.terminal_history = []
+if 'workspace_projects' not in st.session_state:
+    st.session_state.workspace_projects = {}
+if 'available_agents' not in st.session_state:
+    st.session_state.available_agents = []
+if 'current_state' not in st.session_state:
+    st.session_state.current_state = {
+        'toolbox': {},
+        'workspace_chat': {}
+    }
+
+# Define AIAgent class
+class AIAgent:
+    def __init__(self, name, description, skills):
+        self.name = name
+        self.description = description
+        self.skills = skills
+
+    def create_agent_prompt(self):
+        skills_str = '\n'.join([f"* {skill}" for skill in self.skills])
+        agent_prompt = f"""
+As an elite expert developer, my name is {self.name}. I possess a comprehensive understanding of the following areas:
+{skills_str}
+I am confident that I can leverage my expertise to assist you in developing and deploying cutting-edge web applications. Please feel free to ask any questions or present any challenges you may encounter.
+        """
+        return agent_prompt
+
+    def autonomous_build(self, chat_history, workspace_projects):
+        """
+        Autonomous build logic based on chat history and workspace projects.
+        """
+        summary = "Chat History:\n" + "\n".join([f"User: {u}\nAgent: {a}" for u, a in chat_history])
+        summary += "\n\nWorkspace Projects:\n" + "\n".join([f"{p}: {details}" for p, details in workspace_projects.items()])
+
+        # Use a Hugging Face model for more advanced logic (e.g., a summarization model)
+        summarizer = pipeline("summarization")
+        next_step = summarizer(summary, max_length=50, min_length=25, do_sample=False)[0]['summary_text']
+
+        return summary, next_step
+
+# Function to save an agent's prompt to a file and commit to the Hugging Face repository
+def save_agent_to_file(agent):
+    """Saves the agent's prompt to a file locally and then commits to the Hugging Face repository."""
+    if not os.path.exists(AGENT_DIRECTORY):
+        os.makedirs(AGENT_DIRECTORY)
+    file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
+    config_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}Config.txt")
+    with open(file_path, "w") as file:
+        file.write(agent.create_agent_prompt())
+    with open(config_path, "w") as file:
+        file.write(f"Agent Name: {agent.name}\nDescription: {agent.description}")
+    st.session_state.available_agents.append(agent.name)
+
+    commit_and_push_changes(f"Add agent {agent.name}")
+
+# Function to load an agent's prompt from a file
+def load_agent_prompt(agent_name):
+    """Loads an agent prompt from a file."""
+    file_path = os.path.join(AGENT_DIRECTORY, f"{agent_name}.txt")
+    if os.path.exists(file_path):
+        with open(file_path, "r") as file:
+            agent_prompt = file.read()
+        return agent_prompt
+    else:
+        return None
+
+# Function to create an agent from text input
+def create_agent_from_text(name, text):
+    skills = text.split('\n')
+    agent = AIAgent(name, "AI agent created from text input.", skills)
+    save_agent_to_file(agent)
+    return agent.create_agent_prompt()
+
+# Chat interface using a selected agent
+def chat_interface_with_agent(input_text, agent_name):
+    agent_prompt = load_agent_prompt(agent_name)
+    if agent_prompt is None:
+        return f"Agent {agent_name} not found."
+
+    # Load GPT-2 model
+    model_name = "gpt2"
+    try:
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    except EnvironmentError as e:
+        return f"Error loading model: {e}"
+
+    # Combine agent prompt with user input
+    combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
+
+    # Truncate input text for model length limit
+    max_input_length = 900
+    input_ids = tokenizer.encode(combined_input, return_tensors="pt")
+    if input_ids.shape[1] > max_input_length:
+        input_ids = input_ids[:, :max_input_length]
+
+    # Generate chatbot response
+    outputs = model.generate(
+        input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True, pad_token_id=tokenizer.eos_token_id  # Set pad_token_id to eos_token_id
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Workspace interface for creating projects
+def workspace_interface(project_name):
+    project_path = os.path.join(PROJECT_ROOT, project_name)
+    if not os.path.exists(PROJECT_ROOT):
+        os.makedirs(PROJECT_ROOT)
+    if not os.path.exists(project_path):
+        os.makedirs(project_path)
+        st.session_state.workspace_projects[project_name] = {"files": []}
+        st.session_state.current_state['workspace_chat']['project_name'] = project_name
+        commit_and_push_changes(f"Create project {project_name}")
+        return f"Project {project_name} created successfully."
+    else:
+        return f"Project {project_name} already exists."
+
+# Function to add code to the workspace
+def add_code_to_workspace(project_name, code, file_name):
+    project_path = os.path.join(PROJECT_ROOT, project_name)
+    if os.path.exists(project_path):
+        file_path = os.path.join(project_path, file_name)
+        with open(file_path, "w") as file:
+            file.write(code)
+        st.session_state.workspace_projects[project_name]["files"].append(file_name)
+        st.session_state.current_state['workspace_chat']['added_code'] = {"file_name": file_name, "code": code}
+        commit_and_push_changes(f"Add code to {file_name} in project {project_name}")
+        return f"Code added to {file_name} in project {project_name} successfully."
+    else:
+        return f"Project {project_name} does not exist."
+
+# Terminal interface with optional project context
+def terminal_interface(command, project_name=None):
+    if project_name:
+        project_path = os.path.join(PROJECT_ROOT, project_name)
+        if not os.path.exists(project_path):
+            return f"Project {project_name} does not exist."
+        result = subprocess.run(command, cwd=project_path, shell=True, capture_output=True, text=True)
+    else:
+        result = subprocess.run(command, shell=True, capture_output=True, text=True)
+    if result.returncode == 0:
+        st.session_state.current_state['toolbox']['terminal_output'] = result.stdout
+        return result.stdout
+    else:
+        st.session_state.current_state['toolbox']['terminal_output'] = result.stderr
+        return result.stderr
+
+# Code editor interface for formatting and linting
+def code_editor_interface(code):
+    try:
+        formatted_code = black.format_str(code, mode=black.FileMode())
+    except black.NothingChanged:
+        formatted_code = code
+    result = StringIO()
+    sys.stdout = result
+    sys.stderr = result
+    (pylint_stdout, pylint_stderr) = lint.py_run(code, return_std=True)
+    sys.stdout = sys.stdout
+    sys.stderr = sys.stderr
+    lint_message = pylint_stdout.getvalue() + pylint_stderr.getvalue()
+    st.session_state.current_state['toolbox']['formatted_code'] = formatted_code
+    st.session_state.current_state['toolbox']['lint_message'] = lint_message
+    return formatted_code, lint_message
+
+# Function to summarize text using a summarization pipeline
+def summarize_text(text):
+    summarizer = pipeline("summarization")
+    summary = summarizer(text, max_length=50, min_length=25, do_sample=False)
+    st.session_state.current_state['toolbox']['summary'] = summary[0]['summary_text']
+    return summary[0]['summary_text']
+
+# Function to perform sentiment analysis using a sentiment analysis pipeline
+def sentiment_analysis(text):
+    analyzer = pipeline("sentiment-analysis")
+    sentiment = analyzer(text)
+    st.session_state.current_state['toolbox']['sentiment'] = sentiment[0]
+    return sentiment[0]
+
+# Function to translate code using the OpenAI API
+def translate_code(code, input_language, output_language):
+    # Define a dictionary to map programming languages to their corresponding file extensions
+    language_extensions = {
+        # ignore the specific languages right now, and continue to EOF
+    }
+
+    # Add code to handle edge cases such as invalid input and unsupported programming languages
+    if input_language not in language_extensions:
+        raise ValueError(f"Invalid input language: {input_language}")
+    if output_language not in language_extensions:
+        raise ValueError(f"Invalid output language: {output_language}")
+
+    # Use the dictionary to map the input and output languages to their corresponding file extensions
+    input_extension = language_extensions[input_language]
+    output_extension = language_extensions[output_language]
+
+    # Translate the code using the OpenAI API
+    prompt = f"Translate this code from {input_language} to {output_language}:\n\n{code}"
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[
+            {"role": "system", "content": "You are an expert software developer."},
+            {"role": "user", "content": prompt}
+        ]
     )
+    translated_code = response.choices[0].message['content'].strip()
+
+    # Return the translated code
+    translated_code = response.choices[0].message['content'].strip()
+    st.session_state.current_state['toolbox']['translated_code'] = translated_code
+    return translated_code
+
+# Function to generate code based on a code idea using the OpenAI API
+def generate_code(code_idea):
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[
+            {"role": "system", "content": "You are an expert software developer."},
+            {"role": "user", "content": f"Generate a Python code snippet for the following idea:\n\n{code_idea}"}
+        ]
+    )
+    generated_code = response.choices[0].message['content'].strip()
+    st.session_state.current_state['toolbox']['generated_code'] = generated_code
+    return generated_code
+
+# Function to commit and push changes to the Hugging Face repository
+def commit_and_push_changes(commit_message):
+    """Commits and pushes changes to the Hugging Face repository."""
+    commands = [
+        "git add .",
+        f"git commit -m '{commit_message}'",
+        "git push"
+    ]
+    for command in commands:
+        result = subprocess.run(command, shell=True, capture_output=True, text=True)
+        if result.returncode != 0:
+            st.error(f"Error executing command '{command}': {result.stderr}")
+            break
+
+# Streamlit App
+st.title("AI Agent Creator")
+
+# Sidebar navigation
+st.sidebar.title("Navigation")
+app_mode = st.sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"])
+
+# AI Agent Creator
+if app_mode == "AI Agent Creator":
+    st.header("Create an AI Agent from Text")
+
+    st.subheader("From Text")
+    agent_name = st.text_input("Enter agent name:")
+    text_input = st.text_area("Enter skills (one per line):")
+    if st.button("Create Agent"):
+        agent_prompt = create_agent_from_text(agent_name, text_input)
+        st.success(f"Agent '{agent_name}' created and saved successfully.")
+        st.session_state.available_agents.append(agent_name)
+
+# Tool Box
+elif app_mode == "Tool Box":
+    st.header("AI-Powered Tools")
+
+    # Chat Interface
+    st.subheader("Chat with CodeCraft")
+    chat_input = st.text_area("Enter your message:")
+    if st.button("Send"):
+        if chat_input.startswith("@"):
+            agent_name = chat_input.split(" ")[0][1:]  # Extract agent_name from @agent_name
+            chat_input = " ".join(chat_input.split(" ")[1:])  # Remove agent_name from input
+            chat_response = chat_interface_with_agent(chat_input, agent_name)
+        else:
+            chat_response = chat_interface(chat_input)
+        st.session_state.chat_history.append((chat_input, chat_response))
+        st.write(f"CodeCraft: {chat_response}")
+
+    # Terminal Interface
+    st.subheader("Terminal")
+    terminal_input = st.text_input("Enter a command:")
+    if st.button("Run"):
+        terminal_output = terminal_interface(terminal_input)
+        st.session_state.terminal_history.append((terminal_input, terminal_output))
+        st.code(terminal_output, language="bash")
+
+    # Code Editor Interface
+    st.subheader("Code Editor")
+    code_editor = st.text_area("Write your code:", height=300)
+    if st.button("Format & Lint"):
+        formatted_code, lint_message = code_editor_interface(code_editor)
+        st.code(formatted_code, language="python")
+        st.info(lint_message)
+
+    # Text Summarization Tool
+    st.subheader("Summarize Text")
+    text_to_summarize = st.text_area("Enter text to summarize:")
+    if st.button("Summarize"):
+        summary = summarize_text(text_to_summarize)
+        st.write(f"Summary: {summary}")
+
+    # Sentiment Analysis Tool
+    st.subheader("Sentiment Analysis")
+    sentiment_text = st.text_area("Enter text for sentiment analysis:")
+    if st.button("Analyze Sentiment"):
+        sentiment = sentiment_analysis(sentiment_text)
+        st.write(f"Sentiment: {sentiment}")
+
+    # Text Translation Tool (Code Translation)
+    st.subheader("Translate Code")
+    code_to_translate = st.text_area("Enter code to translate:")
+    source_language = st.text_input("Enter source language (e.g. 'Python'):")
+    target_language = st.text_input("Enter target language (e.g. 'JavaScript'):")
+    if st.button("Translate Code"):
+        # Use a Hugging Face translation model
+        translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es") 
+        translated_code = translator(code_to_translate, target_lang=target_language)[0]['translation_text']
+        st.code(translated_code, language=target_language.lower())
+
+    # Code Generation
+    st.subheader("Code Generation")
+    code_idea = st.text_input("Enter your code idea:")
+    if st.button("Generate Code"):
+        # Use a Hugging Face code generation model
+        generator = pipeline("text-generation", model="bigscience/T0_3B") 
+        generated_code = generator(code_idea, max_length=100, num_return_sequences=1, do_sample=True)[0]['generated_text']
+        st.code(generated_code, language="python")
+
+    # Display Preset Commands
+    st.subheader("Preset Commands")
+    preset_commands = {
+        "Create a new project": "create_project('project_name')",
+        "Add code to workspace": "add_code_to_workspace('project_name', 'code', 'file_name')",
+        "Run terminal command": "terminal_interface('command', 'project_name')",
+        "Generate code": "generate_code('code_idea')",
+        "Summarize text": "summarize_text('text')",
+        "Analyze sentiment": "sentiment_analysis('text')",
+        "Translate code": "translate_code('code', 'source_language', 'target_language')",
+    }
+    for command_name, command in preset_commands.items():
+        st.write(f"{command_name}: `{command}`")
+
+# Workspace Chat App
+elif app_mode == "Workspace Chat App":
+    st.header("Workspace Chat App")
+
+    # Project Workspace Creation
+    st.subheader("Create a New Project")
+    project_name = st.text_input("Enter project name:")
+    if st.button("Create Project"):
+        workspace_status = workspace_interface(project_name)
+        st.success(workspace_status)
+
+    # Add Code to Workspace
+    st.subheader("Add Code to Workspace")
+    code_to_add = st.text_area("Enter code to add to workspace:")
+    file_name = st.text_input("Enter file name (e.g. 'app.py'):")
+    if st.button("Add Code"):
+        add_code_status = add_code_to_workspace(project_name, code_to_add, file_name)
+        st.success(add_code_status)
+
+    # Terminal Interface with Project Context
+    st.subheader("Terminal (Workspace Context)")
+    terminal_input = st.text_input("Enter a command within the workspace:")
+    if st.button("Run Command"):
+        terminal_output = terminal_interface(terminal_input, project_name)
+        st.code(terminal_output, language="bash")
+
+    # Chat Interface for Guidance
+    st.subheader("Chat with CodeCraft for Guidance")
+    chat_input = st.text_area("Enter your message for guidance:")
+    if st.button("Get Guidance"):
+        chat_response = chat_interface(chat_input)
+        st.session_state.chat_history.append((chat_input, chat_response))
+        st.write(f"CodeCraft: {chat_response}")
+
+    # Display Chat History
+    st.subheader("Chat History")
+    for user_input, response in st.session_state.chat_history:
+        st.write(f"User: {user_input}")
+        st.write(f"CodeCraft: {response}")
+
+    # Display Terminal History
+    st.subheader("Terminal History")
+    for command, output in st.session_state.terminal_history:
+        st.write(f"Command: {command}")
+        st.code(output, language="bash")
+
+    # Display Projects and Files
+    st.subheader("Workspace Projects")
+    for project, details in st.session_state.workspace_projects.items():
+        st.write(f"Project: {project}")
+        for file in details['files']:
+            st.write(f"  - {file}")
+
+    # Chat with AI Agents
+    st.subheader("Chat with AI Agents")
+    selected_agent = st.selectbox("Select an AI agent", st.session_state.available_agents)
+    agent_chat_input = st.text_area("Enter your message for the agent:")
+    if st.button("Send to Agent"):
+        agent_chat_response = chat_interface_with_agent(agent_chat_input, selected_agent)
+        st.session_state.chat_history.append((agent_chat_input, agent_chat_response))
+        st.write(f"{selected_agent}: {agent_chat_response}")
+
+    # Automate Build Process
+    st.subheader("Automate Build Process")
+    if st.button("Automate"):
+        agent = AIAgent(selected_agent, "", [])  # Load the agent without skills for now
+        summary, next_step = agent.autonomous_build(st.session_state.chat_history, st.session_state.workspace_projects)
+        st.write("Autonomous Build Summary:")
+        st.write(summary)
+        st.write("Next Step:")
+        st.write(next_step)
 
-    formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
-    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
-    output = ""
-
-    for response in stream:
-        output += response.token.text
-        yield output
-
-additional_inputs = [
-    gr.Dropdown(
-        label="Agents",
-        choices=[s for s in agents],
-        value=agents[0],
-        interactive=True,
-    ),
-    gr.Textbox(
-        label="System Prompt",
-        max_lines=1,
-        interactive=True,
-    ),
-    gr.Slider(
-        label="Temperature",
-        value=0.9,
-        minimum=0.0,
-        maximum=1.0,
-        step=0.05,
-        interactive=True,
-        info="Higher values produce more diverse outputs",
-    ),
-    gr.Slider(
-        label="Max new tokens",
-        value=256,
-        minimum=0,
-        maximum=1000,
-        step=64,
-        interactive=True,
-        info="The maximum numbers of new tokens",
-    ),
-    gr.Slider(
-        label="Top-p (nucleus sampling)",
-        value=0.95,
-        minimum=0.0,
-        maximum=1,
-        step=0.05,
-        interactive=True,
-        info="Higher values sample more low-probability tokens",
-    ),
-    gr.Slider(
-        label="Repetition penalty",
-        value=1.0,
-        minimum=1.0,
-        maximum=2.0,
-        step=0.05,
-        interactive=True,
-        info="Penalize repeated tokens",
-    ),
-]
-
-examples = [
-    ["Create a simple web application using Flask", agents[0], "", 0.9, 256, 0.95, 1.0],
-    ["Generate a Python script to perform a linear regression analysis", agents[2], "", 0.9, 256, 0.95, 1.0],
-    ["Create a Dockerfile for a Node.js application", agents[1], "", 0.9, 256, 0.95, 1.0],
-    ["Write a shell script to automate the deployment of a web application to a server", agents[3], "", 0.9, 256, 0.95, 1.0],
-    ["Generate a SQL query to retrieve the top 10 most popular products by sales", agents[4], "", 0.9, 256, 0.95, 1.0],
-    ["Write a Python script to generate a random password with a given length and complexity", agents[2], "", 0.9, 256, 0.95, 1.0],
-    ["Create a simple game in Unity using C#", agents[0], "", 0.9, 256, 0.95, 1.0],
-    ["Generate a Java program to implement a binary search algorithm", agents[2], "", 0.9, 256, 0.95, 1.0],
-    ["Write a shell script to monitor the CPU usage of a server", agents[1], "", 0.9, 256, 0.95, 1.0],
-    ["Create a simple web application using React and Node.js", agents[0], "", 0.9, 256, 0.95, 1.0],
-    ["Generate a Python script to perform a sentiment analysis on a given text", agents[2], "", 0.9, 256, 0.95, 1.0],
-    ["Write a shell script to automate the backup of a MySQL database", agents[1], "", 0.9, 256, 0.95, 1.0],
-    ["Create a simple game in Unreal Engine using C++", agents[3], "", 0.9, 256, 0.95, 1.0],
-    ["Generate a Java program to implement a bubble sort algorithm", agents[2], "", 0.9, 256, 0.95, 1.0],
-    ["Write a shell script to monitor the memory usage of a server", agents[1], "", 0.9, 256, 0.95, 1.0],
-    ["Create a simple web application using Angular and Node.js", agents[0], "", 0.9, 256, 0.95, 1.0],
-    ["Generate a Python script to perform a text classification on a given dataset", agents[2], "", 0.9, 256, 0.95, 1.0],
-    ["Write a shell script to automate the installation of a software package on a server", agents[1], "", 0.9, 256, 0.95, 1.0],
-    ["Create a simple game in Godot using GDScript", agents[3], "", 0.9, 256, 0.95, 1.0],
-    ["Generate a Java program to implement a merge sort algorithm", agents[2], "", 0.9, 256, 0.95, 1.0],
-    ["Write a shell script to automate the cleanup of temporary files on a server", agents[1], "", 0.9, 256, 0.95, 1.0],
-]
-
-gr.ChatInterface(
-    fn=generate,
-    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
-    additional_inputs=additional_inputs,
-    title="Mixtral 46.7B",
-    examples=examples,
-    concurrency_limit=20,
-).launch(show_api=False)
+# Display current state for debugging
+st.sidebar.subheader("Current State")
+st.sidebar.json(st.session_state.current_state)