Update app.py

app.py

@@ -1,251 +1,354 @@
 import os
+import subprocess
 import streamlit as st
-from huggingface_hub import InferenceClient
-import gradio as gr
-import random
 from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
-import subprocess
+import black
+from pylint import lint
+from io import StringIO
+import openai
+import sys
+
+# Set your OpenAI API key here
+openai.api_key = "YOUR_OPENAI_API_KEY"
+
+HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
+PROJECT_ROOT = "projects"
+AGENT_DIRECTORY = "agents"
+
+# Global state to manage communication between Tool Box and Workspace Chat App
+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': {}
+    }
 
-# --- Agent Definitions ---
 class AIAgent:
-    def __init__(self, name, description, skills, model_name="mistralai/Mixtral-8x7B-Instruct-v0.1"):
+    def __init__(self, name, description, skills):
         self.name = name
         self.description = description
         self.skills = skills
-        self.model_name = model_name
-        self.client = InferenceClient(self.model_name)
 
     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 generate_response(self, prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
-        formatted_prompt = self.format_prompt(prompt, history)
-        stream = self.client.text_generation(formatted_prompt, 
-                                            temperature=temperature,
-                                            max_new_tokens=max_new_tokens,
-                                            top_p=top_p,
-                                            repetition_penalty=repetition_penalty,
-                                            do_sample=True,
-                                            seed=random.randint(1, 1111111111111111),
-                                            stream=True, 
-                                            details=True, 
-                                            return_full_text=False)
-        output = ""
-        for response in stream:
-            output += response.token.text
-            yield output
-        return output
-
-    def format_prompt(self, 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
-
     def autonomous_build(self, chat_history, workspace_projects):
+        """
+        Autonomous build logic that continues based on the state of 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()])
+
+        # Analyze chat history and workspace projects to suggest actions
+        # Example:
+        # - Check if the user has requested to create a new file
+        # - Check if the user has requested to install a package
+        # - Check if the user has requested to run a command
+        # - Check if the user has requested to generate code
+        # - Check if the user has requested to translate code
+        # - Check if the user has requested to summarize text
+        # - Check if the user has requested to analyze sentiment
+
+        # Generate a response based on the analysis
         next_step = "Based on the current state, the next logical step is to implement the main application logic."
+
         return summary, next_step
 
-# --- Agent Definitions ---
-agents = {
-    "WEB_DEV": AIAgent("WEB_DEV", "Web development expert", ["HTML", "CSS", "JavaScript", "Flask", "React"]),
-    "AI_SYSTEM_PROMPT": AIAgent("AI_SYSTEM_PROMPT", "AI system prompt expert", ["Prompt Engineering", "LLM Interaction", "Fine-tuning"]),
-    "PYTHON_CODE_DEV": AIAgent("PYTHON_CODE_DEV", "Python code development expert", ["Python", "Data Structures", "Algorithms", "Libraries"]),
-    "CODE_REVIEW_ASSISTANT": AIAgent("CODE_REVIEW_ASSISTANT", "Code review assistant", ["Code Quality", "Best Practices", "Security"]),
-    "CONTENT_WRITER_EDITOR": AIAgent("CONTENT_WRITER_EDITOR", "Content writer and editor", ["Writing", "Editing", "SEO"]),
-    "QUESTION_GENERATOR": AIAgent("QUESTION_GENERATOR", "Question generator", ["Question Generation", "Knowledge Testing"]),
-    "HUGGINGFACE_FILE_DEV": AIAgent("HUGGINGFACE_FILE_DEV", "Hugging Face file development expert", ["Hugging Face Hub", "Model Training", "Dataset Creation"]),
-}
-
-# --- Streamlit UI ---
-st.title("DevToolKit: AI-Powered Development Environment")
-
-# --- Project Management ---
-st.header("Project Management")
-project_name = st.text_input("Enter project name:")
-if st.button("Create Project"):
-    if project_name not in st.session_state.workspace_projects:
-        st.session_state.workspace_projects[project_name] = {'files': []}
-        st.success(f"Created project: {project_name}")
+def save_agent_to_file(agent):
+    """Saves the agent's prompt to a file."""
+    if not os.path.exists(AGENT_DIRECTORY):
+        os.makedirs(AGENT_DIRECTORY)
+    file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
+    with open(file_path, "w") as file:
+        file.write(agent.create_agent_prompt())
+    st.session_state.available_agents.append(agent.name)
+
+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:
-        st.warning(f"Project {project_name} already exists")
-
-# --- Code Addition ---
-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 ---
-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 ---
-st.subheader("Chat with DevToolKit 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"DevToolKit: {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"DevToolKit: {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_name = st.selectbox("Select an AI agent", list(agents.keys()))
-selected_agent = agents[selected_agent_name]
-agent_chat_input = st.text_area("Enter your message for the agent:")
-if st.button("Send to Agent"):
-    agent_chat_response = selected_agent.generate_response(agent_chat_input, st.session_state.chat_history)
-    st.session_state.chat_history.append((agent_chat_input, agent_chat_response))
-    st.write(f"{selected_agent.name}: {agent_chat_response}")
-
-# --- Automate Build Process ---
-st.subheader("Automate Build Process")
-if st.button("Automate"):
-    summary, next_step = selected_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)
-
-# --- Display current state for debugging ---
-st.sidebar.subheader("Current State")
-st.sidebar.json(st.session_state.current_state)
-
-# --- Gradio Interface ---
-additional_inputs = [
-    gr.Dropdown(label="Agents", choices=list(agents.keys()), value=list(agents.keys())[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=1048*10, minimum=0, maximum=1000*10, step=64, interactive=True, info="The maximum numbers of new tokens"),
-    gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Higher values sample more low-probability tokens"),
-    gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Penalize repeated tokens"),
-]
-
-examples = [
-    ["Create a simple web application using Flask", "WEB_DEV", None, None, None, None, ],
-    ["Generate a Python script to perform a linear regression analysis", "PYTHON_CODE_DEV", None, None, None, None, ],
-    ["Create a Dockerfile for a Node.js application", "AI_SYSTEM_PROMPT", None, None, None, None, ],
-    # Add more examples as needed
-]
-
-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="DevToolKit AI Assistant",
-    examples=examples,
-    concurrency_limit=20,
-).launch(show_api=True)
-
-# --- Helper Functions (Moved to separate file) ---
-def generate(prompt, history, agent_name, sys_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
-    # ... (Implementation in utils.py)
-
-def chat_interface(chat_input):
-    # ... (Implementation in utils.py)
-
-def chat_interface_with_agent(chat_input, agent_name):
-    # ... (Implementation in utils.py)
-
-def terminal_interface(command, project_name):
-    # ... (Implementation in utils.py)
+        return None
 
-def add_code_to_workspace(project_name, code, file_name):
-    # ... (Implementation in utils.py)
-2. requirements.txt (Dependencies)
+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()
 
-streamlit
-huggingface_hub
-gradio
-transformers
-subprocess
-3. utils.py (Helper Functions)
+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."
 
-import os
-import subprocess
-import streamlit as st
-
-def generate(prompt, history, agent_name, sys_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
-    seed = random.randint(1, 1111111111111111)
-    agent = agents[agent_name]
-    system_prompt = agent.create_agent_prompt() if sys_prompt is None else sys_prompt
-
-    generate_kwargs = dict(
-        temperature=float(temperature),
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        do_sample=True,
-        seed=seed,
+    model_name = "Bin12345/AutoCoder_S_6.7B"
+    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}"
+
+    combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
+    
+    input_ids = tokenizer.encode(combined_input, return_tensors="pt")
+    max_input_length = 900
+    if input_ids.shape[1] > max_input_length:
+        input_ids = input_ids[:, :max_input_length]
+
+    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
     )
-
-    formatted_prompt = agent.format_prompt(f"{system_prompt}, {prompt}", history)
-    stream = agent.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
-    return output
-
-def chat_interface(chat_input):
-    response = generate(chat_input, st.session_state.chat_history)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return response
 
-def chat_interface_with_agent(chat_input, agent_name):
-    agent_prompt = agents[agent_name].create_agent_prompt()
-    response = generate(chat_input, st.session_state.chat_history, agent_name=agent_name, sys_prompt=agent_prompt)
-    return response
+# Terminal interface
+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, shell=True, capture_output=True, text=True, cwd=project_path)
+    else:
+        result = subprocess.run(command, shell=True, capture_output=True, text=True)
+    return result.stdout
 
-def terminal_interface(command, project_name):
+# Code editor interface
+def code_editor_interface(code):
     try:
-        result = subprocess.run(command, shell=True, capture_output=True, text=True, cwd=project_name)
-        return result.stdout if result.returncode == 0 else result.stderr
-    except Exception as e:
-        return str(e)
+        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()
+
+    return formatted_code, lint_message
+
+# Text summarization tool
+def summarize_text(text):
+    summarizer = pipeline("summarization")
+    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
+    return summary[0]['summary_text']
+
+# Sentiment analysis tool
+def sentiment_analysis(text):
+    analyzer = pipeline("sentiment-analysis")
+    result = analyzer(text)
+    return result[0]['label']
+
+# Text translation tool (code translation)
+def translate_code(code, source_language, target_language):
+    # Use a Hugging Face translation model instead of OpenAI
+    translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")  # Example: English to Spanish
+    translated_code = translator(code, target_lang=target_language)[0]['translation_text']
+    return translated_code
+
+def generate_code(code_idea):
+    # Use a Hugging Face code generation model instead of OpenAI
+    generator = pipeline('text-generation', model='bigcode/starcoder')
+    generated_code = generator(code_idea, max_length=1000, num_return_sequences=1)[0]['generated_text']
+    return generated_code
+
+def chat_interface(input_text):
+    """Handles general chat interactions with the user."""
+    # Use a Hugging Face chatbot model or your own logic
+    chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium")
+    response = chatbot(input_text, max_length=50, num_return_sequences=1)[0]['generated_text']
+    return response
 
-def add_code_to_workspace(project_name, code, file_name):
-    project_path = os.path.join(os.getcwd(), project_name)
+# Workspace interface
+def workspace_interface(project_name):
+    project_path = os.path.join(PROJECT_ROOT, project_name)
     if not os.path.exists(project_path):
         os.makedirs(project_path)
+        st.session_state.workspace_projects[project_name] = {'files': []}
+        return f"Project '{project_name}' created successfully."
+    else:
+        return f"Project '{project_name}' already exists."
+
+# Add code to workspace
+def add_code_to_workspace(project_name, code, file_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."
+    
     file_path = os.path.join(project_path, file_name)
-    with open(file_path, 'w') as file:
+    with open(file_path, "w") as file:
         file.write(code)
-    if project_name not in st.session_state.workspace_projects:
-        st.session_state.workspace_projects[project_name] = {'files': []}
     st.session_state.workspace_projects[project_name]['files'].append(file_name)
-    return f"Added {file_name} to {project_name}"
+    return f"Code added to '{file_name}' in project '{project_name}'."
+
+# 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"])
+
+if app_mode == "AI Agent Creator":
+    # 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)
+
+elif app_mode == "Tool Box":
+    # 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"):
+        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"):
+        translated_code = translate_code(code_to_translate, source_language, target_language)
+        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"):
+        generated_code = generate_code(code_idea)
+        st.code(generated_code, language="python")
+
+elif app_mode == "Workspace Chat App":
+    # 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)