Update app.py

app.py

@@ -1,330 +1,348 @@
 import os
 import subprocess
-import random
-from huggingface_hub import InferenceClient
-import gradio as gr
-from safe_search import safe_search
-from i_search import google
-from i_search import i_search as i_s
-from datetime import datetime
-import logging
-import json
-
-now = datetime.now()
-date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
-
-client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
-
-# --- Set up logging ---
-logging.basicConfig(
-    filename="app.log",  # Name of the log file
-    level=logging.INFO,  # Set the logging level (INFO, DEBUG, etc.)
-    format="%(asctime)s - %(levelname)s - %(message)s",
-)
-
-agents = [
-    "WEB_DEV",
-    "AI_SYSTEM_PROMPT",
-    "PYTHON_CODE_DEV"
-]
-
-VERBOSE = True
-MAX_HISTORY = 5
-
-PREFIX = """
-{date_time_str}
-Purpose: {purpose}
-Safe Search: {safe_search}
-"""
-
-LOG_PROMPT = """
-PROMPT: {content}
-"""
-
-LOG_RESPONSE = """
-RESPONSE: {resp}
-"""
-
-COMPRESS_HISTORY_PROMPT = """
-You are a helpful AI assistant. Your task is to compress the following history into a summary that is no longer than 512 tokens.
-History:
-{history}
+import streamlit as st
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, AutoConfig, AutoModel
+
+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': {}
+    }
+
+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 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
+
+def load_hf_token():
+    return 'YOUR_HF_TOKEN'
+
+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:
+        return None
 
-ACTION_PROMPT = """
-You are a helpful AI assistant. You are working on the task: {task}
-Your current history is:
-{history}
-What is your next thought?
-thought: 
-What is your next action?
-action: 
-"""
+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()
 
-TASK_PROMPT = """
-You are a helpful AI assistant. Your current history is:
-{history}
-What is the next task?
-task: 
-"""
+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."
 
-UNDERSTAND_TEST_RESULTS_PROMPT = """
-You are a helpful AI assistant. The test results are:
-{test_results}
-What do you want to know about the test results?
-thought: 
-"""
+    model_name = "Bin12345/AutoCoder_S_6.7B"
+    try:
+        model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=load_hf_token())
+        tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=load_hf_token())
+        generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    except EnvironmentError as e:
+        return f"Error loading model: {e}"
 
-def format_prompt(message, history, max_history_turns=2):
-    prompt = " "
-    # Keep only the last 'max_history_turns' turns
-    for user_prompt, bot_response in history[-max_history_turns:]:
-        prompt += f"[INST] {user_prompt} [/INST] {bot_response} "
-    prompt += f"[INST] {message} [/INST] "
-    return prompt
-
-def run_gpt(
-    prompt_template,
-    stop_tokens,
-    max_tokens,
-    purpose,
-    **prompt_kwargs,
-):
-    seed = random.randint(1, 1111111111111111)
-    logging.info(f"Seed: {seed}")  # Log the seed
-
-    content = PREFIX.format(
-        date_time_str=date_time_str,
-        purpose=purpose,
-        safe_search=safe_search,
-    ) + prompt_template.format(**prompt_kwargs)
-    if VERBOSE:
-        logging.info(LOG_PROMPT.format(content))  # Log the prompt
-
-    resp = client.text_generation(content, max_new_tokens=max_tokens, stop_sequences=stop_tokens, temperature=0.7, top_p=0.8, repetition_penalty=1.5)
-    if VERBOSE:
-        logging.info(LOG_RESPONSE.format(resp))  # Log the response
-    return resp
-
-def generate(
-    prompt,
-    history,
-    agent_name=agents[0],
-    sys_prompt="",
-    temperature=0.7,
-    max_new_tokens=2048,
-    top_p=0.8,
-    repetition_penalty=1.5,
-):
-    content = PREFIX.format(
-        date_time_str=date_time_str,
-        purpose=purpose,
-        safe_search=safe_search,
-    ) + prompt
-    if VERBOSE:
-        logging.info(LOG_PROMPT.format(content))  # Log the prompt
-
-    stream = client.text_generation(content, stream=True, details=True, return_full_text=False, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty, max_new_tokens=max_new_tokens)
-    resp = ""
-    for response in stream:
-        resp += response.token.text
-
-    if VERBOSE:
-        logging.info(LOG_RESPONSE.format(resp))  # Log the response
-    return resp
-
-def compress_history(purpose, task, history, directory):
-    resp = run_gpt(
-        COMPRESS_HISTORY_PROMPT,
-        stop_tokens=["observation:", "task:", "action:", "thought:"],
-        max_tokens=512,
-        purpose=purpose,
-        task=task,
-        history=history,
+    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
     )
-    history = "observation: {}\n".format(resp)
-    return history
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    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 call_search(purpose, task, history, directory, action_input):
-    logging.info(f"CALLING SEARCH: {action_input}")
+# Code editor interface
+def code_editor_interface(code):
     try:
-        if "http" in action_input:
-            action_input = action_input.strip("<>").strip()
-            response = i_s(action_input)
-            logging.info(f"Search Result: {response}")
-            history += "observation: search result is: {}\n".format(response)
-        else:
-            history += "observation: I need to provide a valid URL to 'action: SEARCH action_input=https://URL'\n"
-    except Exception as e:
-        history += "observation: {}\n".format(e)
-    return "MAIN", None, history, task
-
-def call_main(purpose, task, history, directory, action_input):
-    logging.info(f"CALLING MAIN: {action_input}")
-    resp = run_gpt(
-        ACTION_PROMPT,
-        stop_tokens=["observation:", "task:", "action:", "thought:"],
-        max_tokens=32000,
-        purpose=purpose,
-        task=task,
-        history=history,
-    )
-    lines = resp.strip().split("\n")
-    for line in lines:
-        if line == "":
-            continue
-        if line.startswith("thought: "):
-            history += "{}\n".format(line)
-            logging.info(f"Thought: {line}")
-        elif line.startswith("action: "):
-            action_name, action_input = parse_action(line)
-            logging.info(f"Action: {action_name} - {action_input}")
-            history += "{}\n".format(line)
-            if "COMPLETE" in action_name or "COMPLETE" in action_input:
-                task = "END"
-                return action_name, action_input, history, task
-            else:
-                return action_name, action_input, history, task
-        else:
-            history += "{}\n".format(line)
-            logging.info(f"Other Output: {line}")
-    return "MAIN", None, history, task
-
-def call_set_task(purpose, task, history, directory, action_input):
-    logging.info(f"CALLING SET_TASK: {action_input}")
-    task = run_gpt(
-        TASK_PROMPT,
-        stop_tokens=[],
-        max_tokens=64,
-        purpose=purpose,
-        task=task,
-        history=history,
-    ).strip("\n")
-    history += "observation: task has been updated to: {}\n".format(task)
-    return "MAIN", None, history, task
-
-def end_fn(purpose, task, history, directory, action_input):
-    logging.info(f"CALLING END_FN: {action_input}")
-    task = "END"
-    return "COMPLETE", "COMPLETE", history, task
-
-NAME_TO_FUNC = {
-    "MAIN": call_main,
-    "UPDATE-TASK": call_set_task,
-    "SEARCH": call_search,
-    "COMPLETE": end_fn,
-}
-
-def run_action(purpose, task, history, directory, action_name, action_input):
-    logging.info(f"RUNNING ACTION: {action_name} - {action_input}")
-    try:
-        if "RESPONSE" in action_name or "COMPLETE" in action_name:
-            action_name = "COMPLETE"
-            task = "END"
-            return action_name, "COMPLETE", history, task
-
-        # compress the history when it is long
-        if len(history.split("\n")) > MAX_HISTORY:
-            logging.info("COMPRESSING HISTORY")
-            history = compress_history(purpose, task, history, directory)
-        if action_name not in NAME_TO_FUNC:
-            action_name = "MAIN"
-        if action_name == "" or action_name is None:
-            action_name = "MAIN"
-        assert action_name in NAME_TO_FUNC
-
-        logging.info(f"RUN: {action_name} - {action_input}")
-        return NAME_TO_FUNC[action_name](purpose, task, history, directory, action_input)
-    except Exception as e:
-        history += "observation: the previous command did not produce any useful output, I need to check the commands syntax, or use a different command\n"
-        logging.error(f"Error in run_action: {e}")
-        return "MAIN", None, history, task
-
-def run(purpose, history):
-    task = None
-    directory = "./"
-    if history:
-        history = str(history).strip("[]")
-    if not history:
-        history = ""
-    
-    action_name = "UPDATE-TASK" if task is None else "MAIN"
-    action_input = None
-    while True:
-        logging.info(f"---")
-        logging.info(f"Purpose: {purpose}")
-        logging.info(f"Task: {task}")
-        logging.info(f"---")
-        logging.info(f"History: {history}")
-        logging.info(f"---")
-
-        action_name, action_input, history, task = run_action(
-            purpose,
-            task,
-            history,
-            directory,
-            action_name,
-            action_input,
-        )
-        yield (history)
-        if task == "END":
-            return (history)
-
-def parse_action(line):
-    """Parse the action line to get the action name and input."""
-    parts = line.split(":", 1)
-    if len(parts) == 2:
-        action_name = parts[0].replace("action", "").strip()
-        action_input = parts[1].strip()
+        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", model="t5-base", use_auth_token=load_hf_token())
+    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", model="cardiffnlp/twitter-roberta-base-sentiment", use_auth_token=load_hf_token())
+    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", use_auth_token=load_hf_token())  # 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', use_auth_token=load_hf_token())
+    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", use_auth_token=load_hf_token())
+    response = chatbot(input_text, max_length=50, num_return_sequences=1)[0]['generated_text']
+    return response
+
+# 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:
-        action_name = parts[0].replace("action", "").strip()
-        action_input = ""
-    return action_name, action_input
-
-def main():
-    with gr.Blocks() as demo:
-        gr.Markdown("## FragMixt")
-        gr.Markdown("### Agents w/ Agents")
-
-        # Chat Interface
-        chatbot = gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel")
-        
-        # Input Components
-        message = gr.Textbox(label="Enter your message", placeholder="Ask me anything!")
-        purpose = gr.Textbox(label="Purpose", placeholder="What is the purpose of this interaction?")
-        agent_name = gr.Dropdown(label="Agents", choices=[s for s in agents], value=agents[0], interactive=True)
-        sys_prompt = gr.Textbox(label="System Prompt", max_lines=1, interactive=True)
-        temperature = 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")
-        max_new_tokens = gr.Slider(label="Max new tokens", value=1048*10, minimum=0, maximum=1048*10, step=64, interactive=True, info="The maximum numbers of new tokens")
-        top_p = 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")
-        repetition_penalty = gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Penalize repeated tokens")
-
-        # Button to submit the message
-        submit_button = gr.Button(value="Send")
-
-        # Project Explorer Tab
-        with gr.Tab("Project Explorer"):
-            project_path = gr.Textbox(label="Project Path", placeholder="/home/user/app/current_project")
-            explore_button = gr.Button(value="Explore")
-            project_output = gr.Textbox(label="File Tree", lines=20)
-
-        # Chat App Logic Tab
-        with gr.Tab("Chat App"):
-            history = gr.State([])
-            examples = [
-                ["What is the purpose of this AI agent?", "I am designed to assist with no-code development tasks."],
-                ["Can you help me generate a Python function to calculate the factorial of a number?", "Sure! Here is a Python function to calculate the factorial of a number:"],
-            ]
-
-        def chat(purpose, message, agent_name, sys_prompt, temperature, max_new_tokens, top_p, repetition_penalty, history):
-            prompt = format_prompt(message, history)
-            response = generate(prompt, history, agent_name, sys_prompt, temperature, max_new_tokens, top_p, repetition_penalty)
-            history.append((message, response))
-            return history, history
-
-        submit_button.click(chat, inputs=[purpose, message, agent_name, sys_prompt, temperature, max_new_tokens, top_p, repetition_penalty, history], outputs=[chatbot, history])
-
-    demo.launch()
-
-if __name__ == "__main__":
-    main()
+        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:
+        file.write(code)
+    st.session_state.workspace_projects[project_name]['files'].append(file_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)