Update app.py

app.py

@@ -1,452 +1,310 @@
-from huggingface_hub import InferenceClient, hf_hub_url
-import gradio as gr
-import random
 import os
 import subprocess
-import threading
-import time
-import shutil
-from typing import Dict, Tuple, List
-import json
-from rich import print as rprint
-from rich.panel import Panel
-from rich.progress import track
-from rich.table import Table
-from rich.prompt import Prompt, Confirm
-from rich.markdown import Markdown
-from rich.traceback import install
-install()  # Enable rich tracebacks for easier debugging
-
-# --- Constants ---
-
-API_URL = "https://api-inference.huggingface.co/models/"
-MODEL_NAME = "mistralai/Mixtral-8x7B-Instruct-v0.1"  # Replace with your desired model
-
-# Chat Interface Parameters
-DEFAULT_TEMPERATURE = 0.9
-DEFAULT_MAX_NEW_TOKENS = 2048
-DEFAULT_TOP_P = 0.95
-DEFAULT_REPETITION_PENALTY = 1.2
-
-# Local Server
-LOCAL_HOST_PORT = 7860
-
-# --- Agent Roles ---
-
-agent_roles: Dict[str, Dict[str, bool]] = {
-    "Web Developer": {"description": "A master of front-end and back-end web development.", "active": False},
-    "Prompt Engineer": {"description": "An expert in crafting effective prompts for AI models.", "active": False},
-    "Python Code Developer": {"description": "A skilled Python programmer who can write clean and efficient code.", "active": False},
-    "Hugging Face Hub Expert": {"description": "A specialist in navigating and utilizing the Hugging Face Hub.", "active": False},
-    "AI-Powered Code Assistant": {"description": "An AI assistant that can help with coding tasks and provide code snippets.", "active": False},
-}
-
-# --- Initial Prompt ---
-
-selected_agent = list(agent_roles.keys())[0]
-initial_prompt = f"""
-You are an expert {selected_agent} who responds with complete program coding to client requests. 
-Using available tools, please explain the researched information.
-Please don't answer based solely on what you already know. Always perform a search before providing a response.
-In special cases, such as when the user specifies a page to read, there's no need to search.
-Please read the provided page and answer the user's question accordingly.
-If you find that there's not much information just by looking at the search results page, consider these two options and try them out:
-- Try clicking on the links of the search results to access and read the content of each page.
-- Change your search query and perform a new search.
-Users are extremely busy and not as free as you are.
-Therefore, to save the user's effort, please provide direct answers.
-BAD ANSWER EXAMPLE
-- Please refer to these pages.
-- You can write code referring these pages.
-- Following page will be helpful.
-GOOD ANSWER EXAMPLE
-- This is the complete code:  -- complete code here --
-- The answer of you question is -- answer here --
-Please make sure to list the URLs of the pages you referenced at the end of your answer. (This will allow users to verify your response.)
-Please make sure to answer in the language used by the user. If the user asks in Japanese, please answer in Japanese. If the user asks in Spanish, please answer in Spanish.
-But, you can go ahead and search in English, especially for programming-related questions. PLEASE MAKE SURE TO ALWAYS SEARCH IN ENGLISH FOR THOSE.
-"""
-
-# --- Custom CSS ---
-
-customCSS = """
-#component-7 { 
-  height: 1600px; 
-  flex-grow: 4;
-}
-"""
-
-# --- Functions ---
-
-# Function to toggle the active state of an agent
-def toggle_agent(agent_name: str) -> str:
-    """Toggles the active state of an agent."""
-    global agent_roles
-    agent_roles[agent_name]["active"] = not agent_roles[agent_name]["active"]
-    return f"{agent_name} is now {'active' if agent_roles[agent_name]['active'] else 'inactive'}"
-
-# Function to get the active agent cluster
-def get_agent_cluster() -> Dict[str, bool]:
-    """Returns a dictionary of active agents."""
-    return {agent: agent_roles[agent]["active"] for agent in agent_roles}
-
-# Function to execute code
-def run_code(code: str) -> str:
-    """Executes the provided code and returns the output."""
+import logging
+import streamlit as st
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, AutoModel, RagRetriever, AutoModelForSeq2SeqLM
+import torch
+from datetime import datetime
+from huggingface_hub import hf_hub_url, cached_download, HfApi
+from dotenv import load_dotenv
+
+# Constants
+HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
+PROJECT_ROOT = "projects"
+AGENT_DIRECTORY = "agents"
+AVAILABLE_CODE_GENERATIVE_MODELS = [
+    "bigcode/starcoder",  # Popular and powerful
+    "Salesforce/codegen-350M-mono",  # Smaller, good for quick tasks
+    "microsoft/CodeGPT-small",  # Smaller, good for quick tasks
+    "google/flan-t5-xl",  # Powerful, good for complex tasks
+    "facebook/bart-large-cnn",  # Good for text-to-code tasks
+]
+
+# Load environment variables
+load_dotenv()
+HF_TOKEN = os.getenv("HUGGING_FACE_API_KEY")
+
+# Initialize logger
+logging.basicConfig(level=logging.INFO)
+
+# 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': {}
+    }
+
+# Load pre-trained RAG retriever
+rag_retriever = RagRetriever.from_pretrained("facebook/rag-token-base")  # Use a Hugging Face RAG model
+
+# Load pre-trained chat model
+chat_model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/DialoGPT-medium")  # Use a Hugging Face chat model
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+
+def process_input(user_input):
+    # Input pipeline: Tokenize and preprocess user input
+    input_ids = tokenizer(user_input, return_tensors="pt").input_ids
+    attention_mask = tokenizer(user_input, return_tensors="pt").attention_mask
+
+    # RAG model: Generate response
+    output = rag_retriever(input_ids, attention_mask=attention_mask)
+    response = output.generator_outputs[0].sequences[0]
+
+    # Chat model: Refine response
+    chat_input = tokenizer(response, return_tensors="pt")
+    chat_input["input_ids"] = chat_input["input_ids"].unsqueeze(0)
+    chat_input["attention_mask"] = chat_input["attention_mask"].unsqueeze(0)
+    output = chat_model(**chat_input)
+    refined_response = output.sequences[0]
+
+    # Output pipeline: Return final response
+    return refined_response
+
+def workspace_interface(project_name):
+    project_path = os.path.join(PROJECT_ROOT, project_name)
+    if os.path.exists(project_path):
+        return f"Project '{project_name}' already exists."
+    else:
+        os.makedirs(project_path)
+        st.session_state.workspace_projects[project_name] = {'files': []}
+        return f"Project '{project_name}' created successfully."
+
+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)
     try:
-        output = subprocess.check_output(
-            ['python', '-c', code],
-            stderr=subprocess.STDOUT,
-            universal_newlines=True,
-        )
-        return output
-    except subprocess.CalledProcessError as e:
-        return f"Error: {e.output}"
-
-# Function to format the prompt
-def format_prompt(message: str, history: list[Tuple[str, str]], agent_roles: list[str]) -> str:
-    """Formats the prompt with the selected agent roles and conversation history."""
-    prompt = f"""
-You are an expert agent cluster, consisting of {', '.join(agent_roles)}. 
-Respond with complete program coding to client requests. 
-Using available tools, please explain the researched information.
-Please don't answer based solely on what you already know. Always perform a search before providing a response.
-In special cases, such as when the user specifies a page to read, there's no need to search.
-Please read the provided page and answer the user's question accordingly.
-If you find that there's not much information just by looking at the search results page, consider these two options and try them out:
-- Try clicking on the links of the search results to access and read the content of each page.
-- Change your search query and perform a new search.
-Users are extremely busy and not as free as you are.
-Therefore, to save the user's effort, please provide direct answers.
-BAD ANSWER EXAMPLE
-- Please refer to these pages.
-- You can write code referring these pages.
-- Following page will be helpful.
-GOOD ANSWER EXAMPLE
-- This is the complete code:  -- complete code here --
-- The answer of you question is -- answer here --
-Please make sure to list the URLs of the pages you referenced at the end of your answer. (This will allow users to verify your response.)
-Please make sure to answer in the language used by the user. If the user asks in Japanese, please answer in Japanese. If the user asks in Spanish, please answer in Spanish.
-But, you can go ahead and search in English, especially for programming-related questions. PLEASE MAKE SURE TO ALWAYS SEARCH IN ENGLISH FOR THOSE.
-"""
-
-    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
-
-# Function to generate a response
-def generate(prompt: str, history: list[Tuple[str, str]], agent_roles: list[str], temperature: float = DEFAULT_TEMPERATURE, max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS, top_p: float = DEFAULT_TOP_P, repetition_penalty: float = DEFAULT_REPETITION_PENALTY) -> str:
-    """Generates a response using the selected agent roles and parameters."""
-    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=random.randint(0, 10**7),
-    )
-
-    formatted_prompt = format_prompt(prompt, history, agent_roles)
-
-    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
-    return output
-
-# Function to handle user input and generate responses
-def chat_interface(message: str, history: list[Tuple[str, str]], agent_cluster: Dict[str, bool], temperature: float, max_new_tokens: int, top_p: float, repetition_penalty: float) -> Tuple[str, str]:
-    """Handles user input and generates responses."""
-    rprint(f"[bold blue]User:[/bold blue] {message}")  # Log user message
-    if message.startswith("python"): 
-        # User entered code, execute it 
-        code = message[9:-3] 
-        output = run_code(code) 
-        rprint(f"[bold green]Code Output:[/bold green] {output}")  # Log code output
-        return (message, output) 
+        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}'."
+    except Exception as e:
+        logging.error(f"Error adding code: {file_name}: {e}")
+        return f"Error adding code: {file_name}"
+
+def run_code(command, project_name=None):
+    if project_name:
+        project_path = os.path.join(PROJECT_ROOT, project_name)
+        result = subprocess.run(command, shell=True, capture_output=True, text=True, cwd=project_path)
     else:
-        # User entered a normal message, generate a response
-        active_agents = [agent for agent, is_active in agent_cluster.items() if is_active]
-        response = generate(message, history, active_agents, temperature, max_new_tokens, top_p, repetition_penalty)
-        rprint(f"[bold purple]Agent Response:[/bold purple] {response}")  # Log agent response
-        return (message, response)
-
-# Function to create a new web app instance
-def create_web_app(app_name: str, code: str) -> None:
-    """Creates a new web app instance with the given name and code."""
-    # Create a new directory for the app
-    os.makedirs(app_name, exist_ok=True)
-
-    # Create the app.py file
-    with open(os.path.join(app_name, 'app.py'), 'w') as f:
-        f.write(code)
-
-    # Create the requirements.txt file
-    with open(os.path.join(app_name, 'requirements.txt'), 'w') as f:
-        f.write("gradio\nhuggingface_hub")
-
-    # Print a success message
-    print(f"Web app '{app_name}' created successfully!")
-
-# Function to handle the "Create Web App" button click
-def create_web_app_button_click(code: str) -> str:
-    """Handles the "Create Web App" button click."""
-    # Get the app name from the user
-    app_name = gr.Textbox.get().strip()
-
-    # Validate the app name
-    if not app_name:
-        return "Please enter a valid app name."
-
-    # Create the web app instance
-    create_web_app(app_name, code)
-
-    # Return a success message
-    return f"Web app '{app_name}' created successfully!"
-
-# Function to handle the "Deploy" button click
-def deploy_button_click(app_name: str, code: str) -> str:
-    """Handles the "Deploy" button click."""
-    # Get the app name from the user
-    app_name = gr.Textbox.get().strip()
-
-    # Validate the app name
-    if not app_name:
-        return "Please enter a valid app name."
-
-    # Get Hugging Face token
-    hf_token = gr.Textbox.get("hf_token").strip()
-
-    # Validate Hugging Face token
-    if not hf_token:
-        return "Please enter a valid Hugging Face token."
-
-    # Create a new directory for the app
-    os.makedirs(app_name, exist_ok=True)
-
-    # Copy the code to the app directory
-    with open(os.path.join(app_name, 'app.py'), 'w') as f:
-        f.write(code)
-
-    # Create the requirements.txt file
-    with open(os.path.join(app_name, 'requirements.txt'), 'w') as f:
-        f.write("gradio\nhuggingface_hub")
-
-    # Deploy the app to Hugging Face Spaces
+        result = subprocess.run(command, shell=True, capture_output=True, text=True)
+    return result.stdout
+
+def display_chat_history(history):
+    chat_history = ""
+    for user_input, response in history:
+        chat_history += f"User: {user_input}\nAgent: {response}\n\n"
+    return chat_history
+
+def display_workspace_projects(projects):
+    workspace_projects = ""
+    for project, details in projects.items():
+        workspace_projects += f"Project: {project}\nFiles:\n"
+        for file in details['files']:
+            workspace_projects += f"  - {file}\n"
+    return workspace_projects
+
+def download_models():
+    for model in AVAILABLE_CODE_GENERATIVE_MODELS:
+        try:
+            cached_model = cached_download(model)
+            logging.info(f"Downloaded model '{model}' successfully.")
+        except Exception as e:
+            logging.error(f"Error downloading model '{model}': {e}")
+
+def deploy_space_to_hf(project_name, hf_token):
+    repository_name = f"my-awesome-space_{datetime.now().timestamp()}"
+    files = get_built_space_files()
+    commit_response = deploy_to_git(project_name, repository_name, files)
+    if commit_response:
+        publish_space(repository_name, hf_token)
+        return f"Space '{repository_name}' deployed successfully."
+    else:
+        return "Failed to commit changes to Space."
+
+def get_built_space_files():
+    projects = st.session_state.workspace_projects
+    files = []
+    for project in projects.values():
+        for file in project['files']:
+            file_path = os.path.join(PROJECT_ROOT, project['project_name'], file)
+            with open(file_path, "rb") as file:
+                files.append(file.read())
+    return files
+
+def deploy_to_git(project_name, repository_name, files):
+    project_path = os.path.join(PROJECT_ROOT, project_name)
+    git_repo_url = hf_hub_url(repository_name)
+    git = subprocess.Popen(["git", "init"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=project_path)
+    git.communicate()
+
+    git = subprocess.Popen(["git", "add", "-A"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=project_path)
+    git.communicate()
+
+    for file in files:
+        filename = "temp.txt"
+        with open("temp.txt", "wb") as temp_file:
+            temp_file.write(file)
+            git = subprocess.Popen(["git", "add", filename], stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=project_path)
+            git.communicate()
+            os.remove("temp.txt")
+
+    git = subprocess.Popen(["git", "commit", "-m", "Initial commit"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=project_path)
+    git.communicate()
+
+    return git.returncode == 0
+
+def publish_space(repository_name, hf_token):
+    api = HfApi(token=hf_token)
+    api.create_model(repository_name, files=[], push_to_hub=True)
+
+def handle_autonomous_build():
+    if not st.session_state.workspace_projects or not st.session_state.available_agents:
+        st.error("No projects or agents available to build.")
+        return
+
+    project_name = st.session_state.workspace_projects.keys()[0]
+    selected_agent = st.session_state.available_agents[0]
+    code_idea = st.session_state.current_state["workspace_chat"]["user_input"]
+    code_generative_model = next((model for model in AVAILABLE_CODE_GENERATIVE_MODELS if model in st.session_state.current_state["toolbox"]["selected_models"]), None)
+
+    if not code_generative_model:
+        st.error("No code-generative model selected.")
+        return
+
+    logging.info(f"Building project '{project_name}' with agent '{selected_agent}' and model '{code_generative_model}'.")
+
     try:
-        subprocess.run(
-            ['huggingface-cli', 'login', '--token', hf_token],
-            check=True,
-        )
-        subprocess.run(
-            ['huggingface-cli', 'space', 'create', app_name, '--repo_type', 'spaces', '--private', '--branch', 'main'],
-            check=True,
-        )
-        subprocess.run(
-            ['git', 'init'],
-            cwd=app_name,
-            check=True,
-        )
-        subprocess.run(
-            ['git', 'add', '.'],
-            cwd=app_name,
-            check=True,
-        )
-        subprocess.run(
-            ['git', 'commit', '-m', 'Initial commit'],
-            cwd=app_name,
-            check=True,
-        )
-        subprocess.run(
-            ['git', 'remote', 'add', 'origin', hf_hub_url(username='your_username', repo_id=app_name)],
-            cwd=app_name,
-            check=True,
-        )
-        subprocess.run(
-            ['git', 'push', '-u', 'origin', 'main'],
-            cwd=app_name,
-            check=True,
-        )
-        return f"Web app '{app_name}' deployed successfully to Hugging Face Spaces!"
-    except subprocess.CalledProcessError as e:
-        return f"Error: {e}"
-
-# Function to handle the "Local Host" button click
-def local_host_button_click(app_name: str, code: str) -> str:
-    """Handles the "Local Host" button click."""
-    # Get the app name from the user
-    app_name = gr.Textbox.get().strip()
-
-    # Validate the app name
-    if not app_name:
-        return "Please enter a valid app name."
-
-    # Create a new directory for the app
-    os.makedirs(app_name, exist_ok=True)
-
-    # Copy the code to the app directory
-    with open(os.path.join(app_name, 'app.py'), 'w') as f:
-        f.write(code)
-
-    # Create the requirements.txt file
-    with open(os.path.join(app_name, 'requirements.txt'), 'w') as f:
-        f.write("gradio\nhuggingface_hub")
-
-    # Start the local server
-    os.chdir(app_name)
-    subprocess.Popen(['gradio', 'run', 'app.py', '--share', '--server_port', str(LOCAL_HOST_PORT)])
-
-    # Return a success message
-    return f"Web app '{app_name}' running locally on port {LOCAL_HOST_PORT}!"
-
-# Function to handle the "Ship" button click
-def ship_button_click(app_name: str, code: str) -> str:
-    """Handles the "Ship" button click."""
-    # Get the app name from the user
-    app_name = gr.Textbox.get().strip()
-
-    # Validate the app name
-    if not app_name:
-        return "Please enter a valid app name."
-
-    # Ship the web app instance
-    # ... (Implement shipping logic here)
-
-    # Return a success message
-    return f"Web app '{app_name}' shipped successfully!"
-
-# --- Gradio Interface ---
-
-with gr.Blocks(theme='ParityError/Interstellar') as demo:
-    # --- Agent Selection ---
-    with gr.Row():
-        for agent_name, agent_data in agent_roles.items():
-            button = gr.Button(agent_name, variant="secondary")
-            textbox = gr.Textbox(agent_data["description"], interactive=False)
-            button.click(toggle_agent, inputs=[button], outputs=[textbox])
-
-    # --- Chat Interface ---
-    with gr.Row():
-        chatbot = gr.Chatbot()
-        chat_interface_input = gr.Textbox(label="Enter your message", placeholder="Ask me anything!")
-        chat_interface_output = gr.Textbox(label="Response", interactive=False)
-
-        # Parameters
-        temperature_slider = gr.Slider(
-            label="Temperature",
-            value=DEFAULT_TEMPERATURE,
-            minimum=0.0,
-            maximum=1.0,
-            step=0.05,
-            interactive=True,
-            info="Higher values generate more diverse outputs",
-        )
-        max_new_tokens_slider = gr.Slider(
-            label="Maximum New Tokens",
-            value=DEFAULT_MAX_NEW_TOKENS,
-            minimum=64,
-            maximum=4096,
-            step=64,
-            interactive=True,
-            info="The maximum number of new tokens",
-        )
-        top_p_slider = gr.Slider(
-            label="Top-p (Nucleus Sampling)",
-            value=DEFAULT_TOP_P,
-            minimum=0.0,
-            maximum=1,
-            step=0.05,
-            interactive=True,
-            info="Higher values sample more low-probability tokens",
-        )
-        repetition_penalty_slider = gr.Slider(
-            label="Repetition Penalty",
-            value=DEFAULT_REPETITION_PENALTY,
-            minimum=1.0,
-            maximum=2.0,
-            step=0.05,
-            interactive=True,
-            info="Penalize repeated tokens",
-        )
-
-        # Submit Button
-        submit_button = gr.Button("Submit")
-
-        # Chat Interface Logic
-        submit_button.click(
-            chat_interface,
-            inputs=[
-                chat_interface_input,
-                chatbot,
-                get_agent_cluster,
-                temperature_slider,
-                max_new_tokens_slider,
-                top_p_slider,
-                repetition_penalty_slider,
-            ],
-            outputs=[
-                chatbot,
-                chat_interface_output,
-            ],
-        )
-
-    # --- Web App Creation ---
-    with gr.Row():
-        app_name_input = gr.Textbox(label="App Name", placeholder="Enter your app name")
-        code_output = gr.Textbox(label="Code", interactive=False)
-        create_web_app_button = gr.Button("Create Web App")
-        deploy_button = gr.Button("Deploy")
-        local_host_button = gr.Button("Local Host")
-        ship_button = gr.Button("Ship")
-        hf_token_input = gr.Textbox(label="Hugging Face Token", placeholder="Enter your Hugging Face token")
-
-        # Web App Creation Logic
-        create_web_app_button.click(
-            create_web_app_button_click,
-            inputs=[code_output],
-            outputs=[gr.Textbox(label="Status", interactive=False)],
-        )
-
-        # Deploy the web app
-        deploy_button.click(
-            deploy_button_click,
-            inputs=[app_name_input, code_output, hf_token_input],
-            outputs=[gr.Textbox(label="Status", interactive=False)],
-        )
-
-        # Local host the web app
-        local_host_button.click(
-            local_host_button_click,
-            inputs=[app_name_input, code_output],
-            outputs=[gr.Textbox(label="Status", interactive=False)],
-        )
-
-        # Ship the web app
-        ship_button.click(
-            ship_button_click,
-            inputs=[app_name_input, code_output],
-            outputs=[gr.Textbox(label="Status", interactive=False)],
-        )
-
-    # --- Connect Chat Output to Code Output ---
-    chat_interface_output.change(
-        lambda x: x,
-        inputs=[chat_interface_output],
-        outputs=[code_output],
-    )
-
-    # --- Initialize Hugging Face Client ---
-    client = InferenceClient(repo_id=MODEL_NAME, token=os.environ.get("HF_TOKEN"))
-
-    # --- Launch Gradio ---
-    demo.queue().launch(debug=True)
+        # TODO: Add code to run the build process here
+        # This could include generating code, running it, and updating the workspace projects
+        # The build process should also update the UI with the build summary and next steps
+        summary, next_step = build_project(project_name, selected_agent, code_idea, code_generative_model)
+        st.write(f"Build summary: {summary}")
+        st.write(f"Next step: {next_step}")
+
+        if next_step == "Deploy to Hugging Face Hub":
+            deploy_response = deploy_space_to_hf(project_name, HF_TOKEN)
+            st.write(deploy_response)
+    except Exception as e:
+        logging.error(f"Error during build process: {e}")
+        st.error("Error during build process.")
+
+def build_project(project_name, agent, code_idea, code_generative_model):
+    # TODO: Add code to build the project here
+    # This could include generating code, running it, and updating the workspace projects
+    # The build process should also return a summary and next step
+    summary = "Project built successfully."
+    next_step = ""
+    return summary, next_step
+
+def main():
+    # Initialize the 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"):
+            skills = text_input.split('\n')
+            try:
+                agent = AIAgent(agent_name, "AI agent created from text input", skills)
+                st.session_state.available_agents.append(agent_name)
+                st.success(f"Agent '{agent_name}' created and saved successfully.")
+            except Exception as e:
+                st.error(f"Error creating agent: {e}")
+
+    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"):
+            response = process_input(chat_input)
+            st.session_state.chat_history.append((chat_input, response))
+            st.write(f"CodeCraft: {response}")
+
+        # Terminal Interface
+        st.subheader("Terminal")
+        terminal_input = st.text_input("Enter a command:")
+        if st.button("Run"):
+            output = run_code(terminal_input)
+            st.session_state.terminal_history.append((terminal_input, output))
+            st.code(output, language="bash")
+
+        # Project Management
+        st.subheader("Project Management")
+        project_name_input = st.text_input("Enter Project Name:")
+        if st.button("Create Project"):
+            status = workspace_interface(project_name_input)
+            st.write(status)
+
+        code_to_add = st.text_area("Enter Code to Add to Workspace:", height=150)
+        file_name_input = st.text_input("Enter File Name (e.g., 'app.py'):")
+        if st.button("Add Code"):
+            status = add_code_to_workspace(project_name_input, code_to_add, file_name_input)
+            st.write(status)
+
+        # Display Chat History
+        st.subheader("Chat History")
+        chat_history = display_chat_history(st.session_state.chat_history)
+        st.text_area("Chat History", value=chat_history, height=200)
+
+        # Display Workspace Projects
+        st.subheader("Workspace Projects")
+        workspace_projects = display_workspace_projects(st.session_state.workspace_projects)
+        st.text_area("Workspace Projects", value=workspace_projects, height=200)
+
+        # Download and deploy models
+        if st.button("Download and Deploy Models"):
+            download_models()
+            st.info("Models downloaded and deployed.")
+
+    elif app_mode == "Workspace Chat App":
+        # Workspace Chat App
+        st.header("Workspace Chat App")
+
+        # Chat Interface 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"):
+            response = process_input(agent_chat_input)
+            st.session_state.chat_history.append((agent_chat_input, response))
+            st.write(f"{selected_agent}: {response}")
+
+        # Code Generation
+        st.subheader("Code Generation")
+        code_idea = st.text_input("Enter your code idea:")
+        selected_model = st.selectbox("Select a code-generative model", AVAILABLE_CODE_GENERATIVE_MODELS)
+        if st.button("Generate Code"):
+            generated_code = run_code(code_idea)
+            st.code(generated_code, language="python")
+
+        # Autonomous build process
+        if st.button("Automate Build Process"):
+            handle_autonomous_build()
+
+if __name__ == "__main__":
+    main()