Update app.py

app.py

@@ -1,281 +1,456 @@
 import os
 import subprocess
-import streamlit as st
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
-from langchain_community.llms import HuggingFaceHub
-from langchain_community.embeddings import HuggingFaceHubEmbeddings
-from langchain_community.document_loaders import PyPDFLoader
-from langchain_community.vectorstores import FAISS
-from langchain.chains import ConversationalRetrievalChain
-from langchain.chains.question_answering import load_qa_chain
-from llama_cpp import Llama, LlamaCppPythonProvider, LlamaCppAgent
-from llama_cpp.llama_cpp_agent import get_messages_formatter_type, get_context_by_model
-from io import StringIO
-import tempfile
-import importlib
-import time
-import sys
-from flask import Flask
-
-# --- Global Variables ---
-CURRENT_PROJECT = {}  # Store project data (code, packages, etc.)
-MODEL_OPTIONS = {
-    "CodeQwen": "Qwen/CodeQwen1.5-7B-Chat-GGUF",
-    "Codestral": "bartowski/Codestral-22B-v0.1-GGUF",
-    "AutoCoder": "bartowski/AutoCoder-GGUF",
-}
-MODEL_FILENAMES = {
-    "CodeQwen": "codeqwen-1_5-7b-chat-q6_k.gguf",
-    "Codestral": "Codestral-22B-v0.1-Q6_K.gguf",
-    "AutoCoder": "AutoCoder-Q6_K.gguf",
-}
-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': {}
-    }
-
-# --- Load NLP Pipelines ---
-classifier = pipeline("text-classification", model="facebook/bart-large-mnli")
-
-# --- Load the model and tokenizer ---
-model = AutoModelForCausalLM.from_pretrained(
-    "mistralai/Mixtral-8x7B-Instruct-v0.1", 
-    use_auth_token=os.environ.get("huggingface_token")
+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 agent import (
+    ACTION_PROMPT,
+    ADD_PROMPT,
+    COMPRESS_HISTORY_PROMPT,
+    LOG_PROMPT,
+    LOG_RESPONSE,
+    MODIFY_PROMPT,
+    PREFIX,
+    SEARCH_QUERY,
+    READ_PROMPT,
+    TASK_PROMPT,
+    UNDERSTAND_TEST_RESULTS_PROMPT,
 )
-tokenizer = AutoTokenizer.from_pretrained(
-    "mistralai/Mixtral-8x7B-Instruct-v0.1", 
-    use_auth_token=os.environ.get("huggingface_token")
+from utils import parse_action, parse_file_content, read_python_module_structure
+from datetime import datetime
+now = datetime.now()
+date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
+
+client = InferenceClient(
+    "mistralai/Mixtral-8x7B-Instruct-v0.1"
 )
 
-# --- Utility Functions ---
-def install_and_import(package_name):
-    """Installs a package using pip and imports it."""
-    subprocess.check_call(["pip", "install", package_name])
-    return importlib.import_module(package_name)
-
-def extract_package_name(input_str):
-    """Extracts the package name from a PyPI URL or pip command."""
-    if input_str.startswith("https://pypi.org/project/"):
-        return input_str.split("/")[-2]
-    elif input_str.startswith("pip install "):
-        return input_str.split(" ")[2]
-    else:
-        return input_str
-
-def create_interface_from_input(input_str):
-    """Creates a Gradio interface with buttons for functions from a package."""
+############################################
+
+
+VERBOSE = True
+MAX_HISTORY = 100
+#MODEL = "gpt-3.5-turbo"  # "gpt-4"
+
+
+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
+
+
+
+def run_gpt(
+    prompt_template,
+    stop_tokens,
+    max_tokens,
+    purpose,
+    **prompt_kwargs,
+):
+    seed = random.randint(1,1111111111111111)
+    print (seed)
+    generate_kwargs = dict(
+        temperature=1.0,
+        max_new_tokens=2096,
+        top_p=0.99,
+        repetition_penalty=1.0,
+        do_sample=True,
+        seed=seed,
+    )
+
+    
+    content = PREFIX.format(
+        date_time_str=date_time_str,
+        purpose=purpose,
+        safe_search=safe_search,
+    ) + prompt_template.format(**prompt_kwargs)
+    if VERBOSE:
+        print(LOG_PROMPT.format(content))
+    
+    
+    #formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
+    #formatted_prompt = format_prompt(f'{content}', history)
+
+    stream = client.text_generation(content, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    resp = ""
+    for response in stream:
+        resp += response.token.text
+
+    if VERBOSE:
+        print(LOG_RESPONSE.format(resp))
+    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,
+    )
+    history = "observation: {}\n".format(resp)
+    return history
+    
+def call_search(purpose, task, history, directory, action_input):
+    print("CALLING SEARCH")
     try:
-        package_name = extract_package_name(input_str)
-        module = install_and_import(package_name)
-
-        # Handle Flask application context if needed
-        if 'flask' in sys.modules or 'flask_restful' in sys.modules:
-            app = Flask(__name__)
-            with app.app_context():
-                functions = [getattr(module, name) for name in dir(module) if callable(getattr(module, name))]
+        
+        if "http" in action_input:
+            if "<" in action_input:
+                action_input = action_input.strip("<")
+            if ">" in action_input:
+                action_input = action_input.strip(">")
+            
+            response = i_s(action_input)
+            #response = google(search_return)
+            print(response)
+            history += "observation: search result is: {}\n".format(response)
         else:
-            functions = [getattr(module, name) for name in dir(module) if callable(getattr(module, name))]
+            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):
+    resp = run_gpt(
+        ACTION_PROMPT,
+        stop_tokens=["observation:", "task:", "action:","thought:"],
+        max_tokens=2096,
+        purpose=purpose,
+        task=task,
+        history=history,
+    )
+    lines = resp.strip().strip("\n").split("\n")
+    for line in lines:
+        if line == "":
+            continue
+        if line.startswith("thought: "):
+            history += "{}\n".format(line)
+        elif line.startswith("action: "):
+            
+            action_name, action_input = parse_action(line)
+            print (f'ACTION_NAME :: {action_name}')
+            print (f'ACTION_INPUT :: {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)
+            #history += "observation: the following command did not produce any useful output: '{}', I need to check the commands syntax, or use a different command\n".format(line)
+            
+            #return action_name, action_input, history, task
+            #assert False, "unknown action: {}".format(line)
+    return "MAIN", None, history, task
+
+
+def call_set_task(purpose, task, history, directory, 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):
+    task = "END"
+    return "COMPLETE", "COMPLETE", history, task
+
+NAME_TO_FUNC = {
+    "MAIN": call_main,
+    "UPDATE-TASK": call_set_task,
+    "SEARCH": call_search,
+    "COMPLETE": end_fn,
 
-        function_list = [(func.__name__, func) for func in functions if not func.__name__.startswith("_")]
-        return function_list, f"Interface for `{package_name}` created."
+}
 
+def run_action(purpose, task, history, directory, action_name, action_input):
+    print(f'action_name::{action_name}')
+    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:
+            if VERBOSE:
+                print("COMPRESSING HISTORY")
+            history = compress_history(purpose, task, history, directory)
+        if not action_name in NAME_TO_FUNC:
+            action_name="MAIN"
+        if action_name == "" or action_name == None:
+            action_name="MAIN"
+        assert action_name in NAME_TO_FUNC
+    
+        print("RUN: ", action_name, action_input)
+        return NAME_TO_FUNC[action_name](purpose, task, history, directory, action_input)
     except Exception as e:
-        return [], str(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"
 
-def execute_pip_command(command, add_message):
-    """Executes a pip command and streams the output."""
-    process = subprocess.Popen(command.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+        return "MAIN", None, history, task
+
+def run(purpose,history):
+    
+    #print(purpose)
+    #print(hist)
+    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:
-        output = process.stdout.readline()
-        if output == '' and process.poll() is not None:
-            break
-        if output:
-            # Corrected line: Combine the f-string parts
-            add_message("System", f"'"{output.strip()}")
-            time.sleep(0.1)  # Simulate delay for more realistic streaming
-    rc = process.poll()
-    return rc
-def generate_text(input_text):
-    """Generates text using the loaded language model."""
-    inputs = tokenizer(input_text, return_tensors="pt")
-    output = model.generate(**inputs, max_length=500, num_return_sequences=1)
-    return tokenizer.decode(output[0], skip_special_tokens=True)
-# --- AI Agent Functions ---
-def analyze_user_intent(user_input):
-    """Classifies the user's intent based on their input."""
-    classification = classifier(user_input)
-    return classification[0]['label']
-def generate_mini_app_ideas(theme):
-    """Generates mini-app ideas based on the user's theme."""
-    if theme.lower() == "productivity":
-        return [
-            "Idea-to-Codebase Generator",
-            "Automated GitHub Repo Manager",
-            "AI-Powered IDE"
-        ]
-    elif theme.lower() == "creativity":
-        return [
-            "Brainstorming Assistant",
-            "Mood Board Generator",
-            "Writing Assistant"
-        ]
-    elif theme.lower() == "well-being":
-        return [
-            "Meditation Guide",
-            "Mood Tracker",
-            "Sleep Tracker"
-        ]
-    else:
-        return ["No matching mini-apps found. Try a different theme."]
-def generate_app_code(app_name, app_description, model_name, history):
-    """Generates code for the selected mini-app using the specified GGUF model."""
-    prompt = f"'"Write a Python script for a {app_description} named {app_name} using Gradio and Streamlit:"
-    agent = get_agent(model_name)
-    generated_code = agent.chat(prompt, history)
-    return generated_code
-def execute_terminal_command(command):
-    """Executes a terminal command and returns the output."""
-    try:
-        result = subprocess.check_output(command, shell=True, stderr=subprocess.STDOUT, universal_newlines=True)
-        return result.strip(), None
-    except subprocess.CalledProcessError as e:
-        return e.output.strip(), str(e)
-def install_package(package_name):
-    """Installs a package using pip."""
-    output, error = execute_terminal_command(f"'"pip install {package_name}")
-    if error:
-        return f"'"Error installing package: {error}"
-    else:
-        return f"'"Package `{package_name}` installed successfully."
-def get_project_data():
-    """Returns the current project data."""
-    return CURRENT_PROJECT
-def update_project_data(key, value):
-    """Updates the project data."""
-    CURRENT_PROJECT[key] = value
-def handle_chat(input_text, history):
-    """Handles user input in the chat interface."""
-    def add_message(sender, message):
-        history.append((sender, message))
-    add_message("User", input_text)
-    if input_text.startswith("pip install ") or input_text.startswith("https://pypi.org/project/"):
-        package_name = extract_package_name(input_text)
-        add_message("System", f"'"Installing `{package_name}`...")
-        result = install_package(package_name)
-        add_message("System", result)
-        update_project_data("packages", CURRENT_PROJECT.get("packages", []) + [package_name])
-        return history, dynamic_functions
-    # --- AI Agent Interaction ---
-    if USER_INTENT is None:
-        add_message("System", analyze_user_intent(input_text))
-        add_message("System", "What kind of mini-app do you have in mind?")
-    elif not MINI_APPS:
-        add_message("System", "Here are some ideas:")
-        for idea in generate_mini_app_ideas(input_text):
-            add_message("System", f"'"-{idea}")
-        add_message("System", "Which one would you like to build?")
-    elif CURRENT_APP["name"] is None:
-        selected_app = input_text
-        app_description = next((app for app in MINI_APPS if selected_app in app), None)
-        if app_description:
-            add_message("System", f"'"Generating code for {app_description}...")
-            code = generate_app_code(selected_app, app_description, "CodeQwen", history)  # Use CodeQwen by default
-            add_message("System", f"'"
-python\n{code}\n
-add_message("System", "Code generated! What else can I do for you?")
-            update_project_data("code", code)
-            update_project_data("app_name", selected_app)
-            update_project_data("app_description", app_description)
-        else:
-            add_message("System", "Please choose from the provided mini-app ideas.")
-    else:
-        add_message("System", "You already have an app in progress. Do you want to start over?")
-    return history, dynamic_functions
-# --- Prebuilt Tools ---
-def generate_code_tool(input_text, history):
-    """Prebuilt tool for code generation."""
-    code = generate_app_code("MyTool", "A tool to do something", "CodeQwen", history)  # Use CodeQwen by default
-    return f"'" python {code}"
-def analyze_code_tool(input_text, history):
-    """Prebuilt tool for code analysis."""
-    agent = get_agent("Codestral")
-    analysis = agent.chat(input_text, history)
-    return analysis
-# --- Streamlit Interface ---
-st.title("AI4ME: Your Personal AI App Workshop")
-st.markdown("## Let's build your dream app together! 🤖")
-# --- Hugging Face Token Input ---
-huggingface_token = st.text_input("Enter your Hugging Face Token", type="password", key="huggingface_token")
-os.environ["huggingface_token"] = huggingface_token
-# --- Chat Interface ---
-chat_history = []
-chat_input = st.text_input("Tell me your idea...", key="chat_input")
-if chat_input:
-    chat_history, dynamic_functions = handle_chat(chat_input, chat_history)
-    for sender, message in chat_history:
-        st.markdown(f"**{sender}:** {message}")
-# --- Code Execution and Deployment ---
-if CURRENT_APP["code"]:
-    st.markdown("## Your App Code:")
-    code_area = st.text_area("Your App Code", value=CURRENT_APP["code"], key="code_area")
-    st.markdown("## Deploy Your App (Coming Soon!)")
-    # Add deployment functionality here using Streamlit's deployment features.
-    # For example, you could use Streamlit's `st.button` to trigger deployment.
-    # --- Code Execution ---
-    st.markdown("## Run Your App:")
-    if st.button("Execute Code"):
-        try:
-            # Use Hugging Face's text-generation pipeline for code execution
-            inputs = tokenizer(code_area, return_tensors="pt")
-            output = model.generate(**inputs, max_length=500, num_return_sequences=1)
-            output = tokenizer.decode(output[0], skip_special_tokens=True)
-            st.success(f"'"Code executed successfully!{output}")
-        except Exception as e:
-            st.error(f"'"Error executing code: {e}")
-    # --- Code Editing ---
-    st.markdown("## Edit Your Code:")
-    if st.button("Edit Code"):
-        try:
-            # Use Hugging Face's text-generation pipeline for code editing
-            prompt = f"'"Improve the following Python code: python {code_area}"
-inputs = tokenizer(prompt, return_tensors="pt")
-            output = model.generate(**inputs, max_length=500, num_return_sequences=1)
-            edited_code = tokenizer.decode(output[0], skip_special_tokens=True).split("
-python\n")[1].split("\n
-st.success(f"'"Code edited successfully!\n{edited_code}")            
-            update_project_data("code", edited_code)
-            code_area.value = edited_code
-        except Exception as e:
-            st.error(f"'"Error editing code: {e}")
-# --- Prebuilt Tools ---
-st.markdown("## Prebuilt Tools:")
-with st.expander("Generate Code"):
-    code_input = st.text_area("Enter your code request:", key="code_input")
-    if st.button("Generate"):
-        code_output = generate_code_tool(code_input, chat_history)
-        st.markdown(code_output)
-with st.expander("Analyze Code"):
-    code_input = st.text_area("Enter your code:", key="analyze_code_input")
-    if st.button("Analyze"):
-        analysis_output = analyze_code_tool(code_input, chat_history)
-        st.markdown(analysis_output)
-# --- Additional Features ---
-# Add features like:
-# - Code editing
-# - Integration with external APIs
-# - Advanced AI agents for more complex tasks
-# - User account management
+        print("")
+        print("")
+        print("---")
+        print("purpose:", purpose)
+        print("task:", task)
+        print("---")
+        print(history)
+        print("---")
+
+        action_name, action_input, history, task = run_action(
+            purpose,
+            task,
+            history,
+            directory,
+            action_name,
+            action_input,
+        )
+        yield (history)
+        #yield ("",[(purpose,history)])
+        if task == "END":
+            return (history)
+            #return ("", [(purpose,history)])
+
+
+
+################################################
+
+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"
+]
+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)
+
+    agent=prompts.WEB_DEV
+    if agent_name == "WEB_DEV":
+        agent = prompts.WEB_DEV
+    if agent_name == "AI_SYSTEM_PROMPT":
+        agent = prompts.AI_SYSTEM_PROMPT
+    if agent_name == "PYTHON_CODE_DEV":
+        agent = prompts.PYTHON_CODE_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,
+    )
+
+    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
+    return 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=1048*10,
+        minimum=0,
+        maximum=1048*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 basic Python web app using Flask.", None, None, None, None, None, ],
+    ["Build a simple Streamlit app to display a data visualization.", None, None, None, None, None, ],
+    ["I need a Gradio interface for a machine learning model that takes an image as input and outputs a classification.", None, None, None, None, None, ],
+    ["Generate a Python script to scrape data from a website.", None, None, None, None, None, ],
+    ["I'm building a React app. How can I use Axios to make API calls?", None, None, None, None, None, ],
+    ["Write a Python function to read data from a CSV file.", None, None, None, None, None, ],
+    ["I want to deploy my Flask app to Heroku.", None, None, None, None, None, ],
+    ["Explain the difference between Git and GitHub.", None, None, None, None, None, ],
+    ["How can I use Docker to containerize my Python app?", None, None, None, None, None, ],
+    ["I need a simple API endpoint for my web app using Flask.", None, None, None, None, None, ],
+    ["Create a function in Python to calculate the factorial of a number.", None, None, None, None, None, ],
+]
+
+'''
+gr.ChatInterface(
+fn=run,
+chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
+title="Mixtral 46.7B\nMicro-Agent\nInternet Search <br> development test",
+examples=examples,
+concurrency_limit=20,
+with gr.Blocks() as ifacea:
+    gr.HTML("""TEST""")
+ifacea.launch()
+).launch()
+with gr.Blocks() as iface:
+    #chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
+    chatbot=gr.Chatbot()
+    msg = gr.Textbox()
+    with gr.Row():
+        submit_b = gr.Button()
+        clear = gr.ClearButton([msg, chatbot])
+    submit_b.click(run, [msg,chatbot],[msg,chatbot])
+    msg.submit(run, [msg, chatbot], [msg, chatbot])
+iface.launch()
+'''
+gr.ChatInterface(
+    fn=run,
+    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
+    title="Mixtral 46.7B\nMicro-Agent\nInternet Search <br> development test",
+    examples=examples,
+    concurrency_limit=20,
+).launch(show_api=False)
+
+
+Implementation of Next Steps:
+
+Terminal Integration:
+
+Install Libraries: Install either streamlit-terminal or gradio-terminal depending on your chosen framework.
+Integrate the Terminal: Use the library's functions to embed a terminal component within your Streamlit or Gradio app.
+Capture Input: Capture the user's input from the terminal and pass it to your command execution function.
+Display Output: Display the output of the terminal commands, including both standard output and errors.
+Code Generation:
+
+LLM Selection: Choose a Hugging Face Transformer model that is suitable for code generation (e.g., google/flan-t5-xl, Salesforce/codet5-base, microsoft/CodeGPT-small).
+Prompt Engineering: Develop effective prompts for the LLM to generate code based on natural language instructions.
+Code Translation Function: Create a function that takes natural language input, passes it to the LLM with the appropriate prompt, and then returns the generated code.
+Code Correction: You can explore ways to automatically correct code errors, perhaps using a combination of syntax checking and LLM assistance.
+Workspace Explorer:
+
+Streamlit or Gradio Filesystem Access: Use Streamlit's st.file_uploader or Gradio's gr.File component to allow users to upload files.
+File Management: Implement functions to create, edit, and delete files and directories within the workspace.
+Display Files: Use Streamlit's st.code or Gradio's gr.File component to display the contents of files in the workspace.
+Directory Structure: Display the directory structure of the workspace using a tree-like representation.
+Dependency Management:
+
+Package Installation: Create a function that takes a package name as input, installs it using pip, and updates the requirements.txt file.
+Workspace Population: Develop a function to create files and directories in the workspace based on installed packages.
+Application Build and Launch:
+
+Build Logic: Develop a function to build the web app based on the user's code and dependencies.
+Launch Functionality: Implement a mechanism to launch the built app.
+Error Correction: Identify and correct errors during the build and launch process.
+Automated Assistance: Provide automated assistance during the build and launch process, with a gradient slider to adjust the level of user override.
+
+Recommendations, Enhancements, Optimizations, and Workflow:
+
+1. LLM Selection for Code Generation:
+    *  **Google/Flan-T5-XL:** Excellent for code generation, particularly for Python.
+    *  **Salesforce/CodeT5-Base:**  Strong for code generation, with a focus on code summarization and translation.
+    *  **Microsoft/CodeGPT-Small:**  A smaller model that is suitable for code generation tasks, especially if you have limited computational resources.
+
+2. Prompt Engineering for Code Generation:
+    *  **Contextual Prompts:** Provide the LLM with as much context as possible, including the desired programming language, libraries, and any specific requirements.
+    *  **Code Snippets:**  If possible, include code snippets as part of the prompt to guide the LLM's code generation.
+    *  **Iterative Refinement:**  Use iterative prompting to refine the generated code.  Start with a basic prompt and then provide feedback to the LLM to improve the code.
+
+3. Workspace Exploration:
+    *   **Tree-Like View:**  Use a tree-like representation to display the workspace's directory structure.
+    *   **Search Functionality:** Implement a search bar to allow users to quickly find specific files or directories.
+    *   **Code Highlighting:**  Provide code highlighting for files in the workspace to improve readability.
+
+4. Dependency Management:
+    *   **Virtual Environments:** Use virtual environments to isolate project dependencies and prevent conflicts.
+    *   **Automatic Updates:**  Implement a mechanism to automatically update dependencies when new versions are available.
+    *   **Dependency Locking:**  Use tools like `pip-tools` or `poetry` to lock dependencies to specific versions, ensuring consistent builds.
+
+5. Application Build and Launch:
+    *   **Build Tool Integration:**  Consider integrating a build tool like `poetry` or `pipenv` into your workflow to automate the build process.
+    *   **Containerization:** Containerize the app using Docker to ensure consistent deployments across different environments.
+    *   **Deployment Automation:**  Explore tools like `Heroku`, `AWS Elastic Beanstalk`, or `Google App Engine` to automate the deployment process.
+
+6. Automated Assistance:
+    *   **Error Detection and Correction:**  Implement a system that can detect common coding errors and suggest corrections.
+    *   **Code Completion:**  Use an LLM to provide code completion suggestions as the user types.