import os
import re
import time
import json
import io
import requests

import gradio as gr
import google.generativeai as genai
from google.generativeai import types # Import types for configuration and tools

from huggingface_hub import create_repo, list_models, upload_file, constants
from huggingface_hub.utils import build_hf_headers, get_session, hf_raise_for_status

# Add debugging print to check environment variable immediately
print(f"Attempting to read GOOGLE_API_KEY from environment: {os.environ.get('GOOGLE_API_KEY')}")


# --- Helper functions for Hugging Face integration ---

def show_profile(profile: gr.OAuthProfile | None) -> str:
    """Displays the logged-in Hugging Face profile username."""
    if profile is None:
        return "*Not logged in.*"
    return f"✅ Logged in as **{profile.username}**"

def list_private_models(
    profile: gr.OAuthProfile | None,
    oauth_token: gr.OAuthToken | None
) -> str:
    """Lists private models for the logged-in user (not used in the main workflow, but kept)."""
    if profile is None or oauth_token is None:
        return "Please log in to see your models."
    try:
        models = [
            f"{m.id} ({'private' if m.private else 'public'})"
            for m in list_models(author=profile.username, token=oauth_token.token)
        ]
        return "No models found." if not models else "Models:\n\n" + "\n - ".join(models)
    except Exception as e:
        # Catching generic exception is acceptable for helper functions
        return f"Error listing models: {e}"

def create_space_action(repo_name: str, sdk: str, profile: gr.OAuthProfile, token: gr.OAuthToken):
    """Creates a new Hugging Face Space repository."""
    if not profile or not token:
        raise ValueError("Hugging Face profile or token is missing.")
    repo_id = f"{profile.username}/{repo_name}"
    try:
        create_repo(
            repo_id=repo_id,
            token=token.token,
            exist_ok=True, # Allow creating if it already exists
            repo_type="space",
            space_sdk=sdk
        )
        url    = f"https://huggingface.co/spaces/{repo_id}"
        iframe = f'<iframe src="{url}" width="100%" height="500px"></iframe>'
        return repo_id, iframe
    except Exception as e:
        raise RuntimeError(f"Failed to create Space `{repo_id}`: {e}")

def upload_file_to_space_action(
    file_obj: io.StringIO, # Specify type hint for clarity
    path_in_repo: str,
    repo_id: str,
    profile: gr.OAuthProfile,
    token: gr.OAuthToken
) -> None:
    """Uploads a file to a Hugging Face Space repository."""
    if not (profile and token and repo_id):
        raise ValueError("Hugging Face profile, token, or repo_id is missing.")
    try:
        upload_file(
            path_or_fileobj=file_obj,
            path_in_repo=path_in_repo,
            repo_id=repo_id,
            token=token.token,
            repo_type="space"
        )
    except Exception as e:
        raise RuntimeError(f"Failed to upload `{path_in_repo}` to `{repo_id}`: {e}")

def _fetch_space_logs_level(repo_id: str, level: str, token: str) -> str:
    """Fetches build or run logs for a Space."""
    if not repo_id or not token:
         return f"Cannot fetch {level} logs: repo_id or token missing."
    jwt_url  = f"{constants.ENDPOINT}/api/spaces/{repo_id}/jwt"
    try:
        r = get_session().get(jwt_url, headers=build_hf_headers(token=token))
        hf_raise_for_status(r) # Raise HTTPError for bad responses (4xx or 5xx)
        jwt = r.json()["token"]
        logs_url = f"https://api.hf.space/v1/{repo_id}/logs/{level}"
        lines, count = [], 0
        # Using stream=True is good for potentially large logs
        with get_session().get(logs_url, headers=build_hf_headers(token=jwt), stream=True, timeout=30) as resp:
            hf_raise_for_status(resp)
            for raw in resp.iter_lines():
                if count >= 200: # Limit output lines to prevent UI overload
                    lines.append("... truncated ...")
                    break
                if not raw.startswith(b"data: "): # EventStream protocol expected from HF logs API
                    continue
                payload = raw[len(b"data: "):]
                try:
                    event = json.loads(payload.decode())
                    ts = event.get("timestamp", "")
                    txt = event.get("data", "").strip()
                    if txt:
                        lines.append(f"[{ts}] {txt}")
                        count += 1
                except json.JSONDecodeError:
                    # Skip lines that aren't valid JSON events
                    continue
        return "\n".join(lines) if lines else f"No {level} logs found."
    except Exception as e:
        # Catching generic exception is acceptable for helper functions
        return f"Error fetching {level} logs for `{repo_id}`: {e}"


def get_build_logs_action(repo_id, profile, token):
    """Action to fetch build logs with a small delay."""
    if not (repo_id and profile and token):
        return "⚠️ Cannot fetch build logs: log in and create a Space first."
    # Small delay to allow build process to potentially start on HF side
    time.sleep(5)
    return _fetch_space_logs_level(repo_id, "build", token.token)

def get_container_logs_action(repo_id, profile, token):
    """Action to fetch container logs with a delay."""
    if not (repo_id and profile and token):
        return "⚠️ Cannot fetch container logs: log in and create a Space first."
    # Longer delay to allow container to start after build completes
    time.sleep(10)
    return _fetch_space_logs_level(repo_id, "run", token.token)


# --- Google Gemini integration with model selection and grounding ---

def configure_gemini(api_key: str | None, model_name: str | None) -> str:
    """Configures the Gemini API and checks if the model is accessible."""
    if not api_key:
        return "⚠️ Gemini API key is not set."
    if not model_name:
        return "⚠️ Please select a Gemini model."
    try:
        genai.configure(api_key=api_key)
        # Attempt a simple call to verify credentials and model availability
        # This will raise an exception if the key is invalid or model not found
        genai.GenerativeModel(model_name).generate_content("ping", stream=False)
        # This message indicates the API call *for configuration check* was successful
        return f"✅ Gemini configured successfully with **{model_name}**."
    except Exception as e:
        # This message indicates the API call *for configuration check* failed
        return f"❌ Error configuring Gemini: {e}"

def call_gemini(prompt: str, api_key: str, model_name: str, use_grounding: bool = False) -> str:
    """Calls the Gemini API with a given prompt, optionally using grounding."""
    # This check is crucial - it will raise an error *before* the API call if prereqs aren't met
    if not api_key or not model_name:
        raise ValueError("Gemini API key or model not set.")
    try:
        genai.configure(api_key=api_key)
        model = genai.GenerativeModel(model_name)

        # Define tools for grounding if requested.
        # Using genai.types.GoogleSearch() is recommended for Gemini 2.0+
        # and is backwards compatible with 1.5 for retrieval.
        tools_config = [types.Tool(google_search=types.GoogleSearch())] if use_grounding else None

        # Using generate_content and stream=False for simplicity here
        response = model.generate_content(
            prompt,
            stream=False,
            tools=tools_config # Pass the tools configuration
        )
        # Check if response is blocked
        if response.prompt_feedback and response.prompt_feedback.block_reason:
             raise RuntimeError(f"Gemini API call blocked: {response.prompt_feedback.block_reason}")
        if not response.candidates:
             # Check for safety ratings if no candidates are returned but not blocked
             if response.prompt_feedback and response.prompt_feedback.safety_ratings:
                  ratings = "; ".join([f"{r.category}: {r.probability}" for r in response.prompt_feedback.safety_ratings])
                  raise RuntimeError(f"Gemini API call returned no candidates. Safety ratings: {ratings}")
             else:
                  raise RuntimeError("Gemini API call returned no candidates.")


        # If response.candidates is not empty, get the text
        # Using response.text is a convenient way to get text from the first candidate part
        return response.text or "" # Return empty string if no text

    except Exception as e:
        # Re-raising as RuntimeError for the workflow to catch and manage
        raise RuntimeError(f"Gemini API call failed: {e}")


# --- AI workflow logic (State Machine) ---

# Define States for the workflow
STATE_IDLE = "idle"
STATE_AWAITING_REPO_NAME = "awaiting_repo_name"
STATE_CREATING_SPACE = "creating_space"
STATE_GENERATING_CODE = "generating_code"
STATE_UPLOADING_APP_PY = "uploading_app_py"
STATE_GENERATING_REQUIREMENTS = "generating_requirements"
STATE_UPLOADING_REQUIREMENTS = "uploading_requirements"
STATE_GENERATING_README = "generating_readme"
STATE_UPLOADING_README = "uploading_readme"
STATE_CHECKING_LOGS_BUILD = "checking_logs_build"
STATE_CHECKING_LOGS_RUN = "checking_logs_run"
STATE_DEBUGGING_CODE = "debugging_code"
STATE_UPLOADING_FIXED_APP_PY = "uploading_fixed_app_py"
STATE_COMPLETE = "complete"

MAX_DEBUG_ATTEMPTS = 3 # Limit the number of automatic debug attempts

def add_bot_message(history: list[dict], bot_message: str) -> list[dict]:
    """Helper to add a new assistant message to the chatbot history."""
    history.append({"role": "assistant", "content": bot_message})
    return history

# Add an initial welcome message to the chatbot (defined outside Blocks to be called by load chain)
def greet():
    return [{"role": "assistant", "content": "Welcome! Please log in to Hugging Face and provide your Google AI Studio API key to start building Spaces. Once ready, type 'generate me a gradio app called myapp' or 'create' to begin."}]

# Helper function to update send button interactivity based on prereqs
# MODIFIED to return gr.update
# FIX: Added *args, **kwargs to accept any extra arguments passed by Gradio chaining
def check_send_button_ready(profile: gr.OAuthProfile | None, token: gr.OAuthToken | None, api_key: str | None, model_name: str | None, *args, **kwargs) -> gr.update:
    """Checks if HF login and Gemini configuration are complete and returns update for button interactivity."""
    # --- START ENHANCED DEBUGGING LOGS ---
    print("\n--- check_send_button_ready START ---")
    print(f"  Received profile type: {type(profile)}, is None: {profile is None}")
    print(f"  Received token type: {type(token)}, is None: {token is None}")
    # For api_key, print part of the key if not None for verification, be careful with full key
    print(f"  Received api_key is None: {api_key is None}, first 5 chars: {api_key[:5] if api_key else 'N/A'}")
    print(f"  Received model_name: {model_name}")
    # --- END ENHANCED DEBUGGING LOGS ---

    is_logged_in    = profile is not None and token is not None
    is_gemini_ready = api_key is not None and model_name is not None

    # --- CONTINUED DEBUGGING LOGS ---
    print(f"  HF check: {profile is not None} and {token is not None} = {is_logged_in}")
    print(f"  Gemini check: {api_key is not None} and {model_name is not None} = {is_gemini_ready}")
    # --- END CONTINUED DEBUGGING LOGS ---

    is_ready = is_logged_in and is_gemini_ready
    print(f"check_send_button_ready - HF Ready: {is_logged_in}, Gemini Ready: {is_gemini_ready}, Button Ready (boolean): {is_ready}")
    print("--- check_send_button_ready END ---\n")

    return gr.update(interactive=is_ready)


# This is the main generator function for the workflow, triggered by the 'Send' button
# NOTE: This function MUST accept ALL state variables as inputs that it might need to modify or pass through.
# It MUST also yield/return ALL state variables in the same order they appear in the `outputs` list of the `.click()` event.
def ai_workflow_chat(
    message: str,
    history: list[dict],
    hf_profile: gr.OAuthProfile | None,
    hf_token:   gr.OAuthToken   | None,
    gemini_api_key: str         | None,
    gemini_model:   str         | None,
    repo_id_state:  str | None,
    workflow_state: str,
    space_sdk:      str,
    # NOTE: UI component values are passed *by value* to the generator
    preview_html:   str, # Value from iframe HTML
    container_logs: str, # Value from run_txt Textbox
    build_logs:     str, # Value from build_txt Textbox
    debug_attempts_state: int,
    app_description_state: str | None,
    repo_name_state: str | None,
    generated_code_state: str | None,
    use_grounding_state: bool, # Value from use_grounding_checkbox
    # Absorb potential extra args passed by Gradio event listeners (e.g. old value, event data)
    *args,
    **kwargs
) -> tuple[
    list[dict],       # 0: Updated chat history (for chatbot)
    str | None,       # 1: Updated repo_id (for repo_id state)
    str,              # 2: Updated workflow state (for workflow state)
    str,              # 3: Updated iframe HTML (for iframe UI component)
    str,              # 4: Updated container logs (for run_txt UI component)
    str,              # 5: Updated build logs (for build_txt UI component)
    int,              # 6: Updated debug attempts count (for debug_attempts state)
    str | None,       # 7: Updated app description (for app_description state)
    str | None,       # 8: Updated repo name (for repo_name_state state)
    str | None,       # 9: Updated generated code (for generated_code_state state)
    bool,             # 10: Updated use_grounding_state (for use_grounding_state state)
]:
    """
    Generator function to handle the AI workflow state machine.
    Each 'yield' pauses execution and sends values to update Gradio outputs/state.
    """
    # Unpack state variables from Gradio State components passed as inputs
    repo_id = repo_id_state
    state = workflow_state
    attempts = debug_attempts_state
    app_desc = app_description_state
    repo_name = repo_name_state
    generated_code = generated_code_state
    use_grounding = use_grounding_state # Unpack grounding state

    # Keep copies of potentially updated UI elements passed as inputs to update them later
    # These are the *current values* of the UI components as of the button click
    updated_preview = preview_html
    updated_build = build_logs
    updated_run = container_logs

    # Add the user's message to the chat history immediately
    user_message_entry = {"role": "user", "content": message}
    # Add username if logged in (optional, but nice)
    if hf_profile and hf_profile.username:
         user_message_entry["name"] = hf_profile.username
    history.append(user_message_entry)

    # Yield immediately to update the chat UI with the user's message
    # This provides immediate feedback to the user while the AI processes
    # Ensure all state variables and UI outputs are yielded back in the correct order
    yield (history, repo_id, state, updated_preview, updated_run, updated_build,
           attempts, app_desc, repo_name, generated_code, use_grounding)

    try:
        # --- State Machine Logic based on the current 'state' variable ---

        if state == STATE_IDLE:
            # Check workflow prerequisites before starting any workflow actions
            # The Send button should already be disabled if these aren't met, but double-check
            # Note: These checks here are for the *workflow logic*, not the button interactivity logic.
            # The button state is controlled by check_send_button_ready and the .then chains.
            if not (hf_profile and hf_token):
                # This case should ideally not be reachable if the button is correctly disabled
                history = add_bot_message(history, "Workflow paused: Please log in to Hugging Face first.")
                yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                       attempts, app_desc, repo_name, generated_code, use_grounding)
                return # Stop workflow execution for this click

            if not (gemini_api_key and gemini_model):
                 # This case should also ideally not be reachable if the button is correctly disabled
                 history = add_bot_message(history, "Workflow cannot start: Please ensure your Gemini API key is entered and a model is selected.")
                 yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                        attempts, app_desc, repo_name, generated_code, use_grounding)
                 return # Stop workflow execution for this click


            # Look for specific commands in the user's message
            reset_match = "reset" in message.lower()
            # Capture app description AND repo name using regex
            generate_match = re.search(r'generate (?:me )?(?:a|an) (.+) app called (\w+)', message, re.I)
            # Capture repo name for a simple 'create space' command
            create_match = re.search(r'create (?:a|an)? space called (\w+)', message, re.I)

            if reset_match:
                # Reset the workflow state and associated variables
                history = add_bot_message(history, "Workflow reset.")
                # Yield updated history and reset state variables to their initial values
                # Also reset UI outputs to their initial state
                yield (history, None, STATE_IDLE, "<p>No Space created yet.</p>", "", "", 0,
                       None, None, None, False) # Reset use_grounding to default False, and other states to None/default
                # No return needed after yield in this generator pattern; execution for this click ends here.

            elif generate_match:
                # User requested generation with description and name
                new_app_desc = generate_match.group(1).strip() # Capture description part
                new_repo_name = generate_match.group(2).strip() # Capture name part
                history = add_bot_message(history, f"Acknowledged: '{message}'. Starting workflow to create Space `{hf_profile.username}/{new_repo_name}` for a '{new_app_desc}' app.")
                # Update state variables for the next step (creation)
                state = STATE_CREATING_SPACE
                repo_name = new_repo_name
                app_desc = new_app_desc
                # Yield updated history and state variables (pass UI outputs through)
                yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                       attempts, app_desc, repo_name, generated_code, use_grounding)
                # No return needed

            elif create_match:
                 # User requested simple space creation with a name
                 new_repo_name = create_match.group(1).strip()
                 history = add_bot_message(history, f"Acknowledged: '{message}'. Starting workflow to create Space `{hf_profile.username}/{new_repo_name}`.")
                 state = STATE_CREATING_SPACE # Transition state to creation
                 repo_name = new_repo_name # Store the validated repo name
                 # Yield updated history and state variables (pass UI outputs through)
                 yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                        attempts, app_desc, repo_name, generated_code, use_grounding)
                 # No return needed

            elif "create" in message.lower() and not repo_id:
                # User wants to create but didn't specify a name yet
                history = add_bot_message(history, "Okay, what should the Space be called? (e.g., `my-awesome-app`)")
                state = STATE_AWAITING_REPO_NAME # Transition to the state where we wait for the name
                # Yield updated history and state (pass UI outputs through)
                yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                       attempts, app_desc, repo_name, generated_code, use_grounding)
                # No return needed

            else:
                # Command not recognized in IDLE state
                history = add_bot_message(history, "Command not recognized. Try 'generate me a gradio app called myapp', or 'reset'.")
                # Yield updated history and current state (pass UI outputs through)
                yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                       attempts, app_desc, repo_name, generated_code, use_grounding)
                # No return needed


        elif state == STATE_AWAITING_REPO_NAME:
             # User's message is expected to be the repo name
             new_repo_name = message.strip()
             # Basic validation for Hugging Face repo name format
             # Allow letters, numbers, hyphens, underscores, max 100 chars (HF limit check)
             if not new_repo_name or re.search(r'[^a-zA-Z0-9_-]', new_repo_name) or len(new_repo_name) > 100:
                 history = add_bot_message(history, "Invalid name. Please provide a single word/slug for the Space name (letters, numbers, underscores, hyphens only, max 100 chars).")
                 # Stay in AWAITING_REPO_NAME state and yield message (pass UI outputs through)
                 yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                        attempts, app_desc, repo_name, generated_code, use_grounding)
                 # No return needed

             else:
                 history = add_bot_message(history, f"Using Space name `{new_repo_name}`. Creating Space `{hf_profile.username}/{new_repo_name}`...")
                 state = STATE_CREATING_SPACE # Transition state to creation
                 repo_name = new_repo_name # Store the validated repo name
                 # Yield updated history, state, and repo name. UI outputs remain unchanged for now.
                 # The next click will proceed from the STATE_CREATING_SPACE block.
                 yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                        attempts, app_desc, repo_name, generated_code, use_grounding)
                 # No return needed

        # Note: Each 'elif' block below represents a distinct step in the workflow triggered
        # when the 'state' variable matches its condition on a button click.

        elif state == STATE_CREATING_SPACE:
             # Ensure repo_name is available (it should have been set in the previous step)
             if not repo_name:
                 history = add_bot_message(history, "Internal error: Repo name missing for creation. Resetting.")
                 # Reset relevant states and UI outputs on critical error
                 yield (history, None, STATE_IDLE, "<p>Error creating space.</p>", "", "", 0,
                       None, None, None, use_grounding) # Pass grounding state through
                 # No return needed

             else:
                 try:
                     # Perform the action to create the Space on Hugging Face
                     new_repo_id, iframe_html = create_space_action(repo_name, space_sdk, hf_profile, hf_token)
                     updated_preview = iframe_html # Update the iframe content to show the new space
                     repo_id = new_repo_id # Store the official repo_id
                     history = add_bot_message(history, f"✅ Space `{repo_id}` created. Click 'Send' to generate and upload code.")
                     state = STATE_GENERATING_CODE # Transition to the next state
                     # Yield updated state variables and history, and the new iframe HTML
                     yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                            attempts, app_desc, repo_name, generated_code, use_grounding) # Pass logs and grounding through
                     # No return needed

                 except Exception as e:
                     history = add_bot_message(history, f"❌ Error creating space: {e}. Click 'reset'.")
                     # Yield error message and reset state on failure
                     yield (history, None, STATE_IDLE, "<p>Error creating space.</p>", "", "", 0,
                           None, None, None, use_grounding) # Pass logs and grounding through
                     # No return needed


        elif state == STATE_GENERATING_CODE:
             # Define the prompt for Gemini based on the app description or a default
             prompt_desc = app_desc if app_desc else f'a simple {space_sdk} app'
             prompt = f"""
You are an AI assistant specializing in Hugging Face Spaces using the {space_sdk} SDK.
Generate a full, single-file Python app based on:
'{prompt_desc}'
Ensure the code is runnable as `app.py` in a Hugging Face Space using the `{space_sdk}` SDK. Include necessary imports and setup.
Return **only** the python code block for `app.py`. Do not include any extra text, explanations, or markdown outside the code block.
"""
             try:
                 history = add_bot_message(history, f"🧠 Generating `{prompt_desc}` `{space_sdk}` app (`app.py`) code with Gemini...")
                 if use_grounding:
                     history = add_bot_message(history, "(Using Grounding with Google Search)")
                 # Yield to show message before the potentially time-consuming API call
                 yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                        attempts, app_desc, repo_name, generated_code, use_grounding)

                 # Perform the Gemini API call to generate code, optionally using grounding
                 code = call_gemini(prompt, gemini_api_key, gemini_model, use_grounding=use_grounding)
                 code = code.strip()
                 # Clean up common markdown code block formatting if present
                 if code.startswith("```python"):
                     code = code[len("```python"):].strip()
                 if code.startswith("```"): # Handle generic code blocks too
                      code = code[len("```"):].strip()
                 if code.endswith("```"):
                     code = code[:-len("```")].strip()

                 if not code:
                      raise ValueError("Gemini returned empty code.")

                 history = add_bot_message(history, "✅ `app.py` code generated. Click 'Send' to upload.")
                 state = STATE_UPLOADING_APP_PY # Transition to the upload state
                 generated_code = code # Store the generated code in the state variable for the next step
                 # Yield updated state variables and history (pass UI outputs and other states through)
                 yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                        attempts, app_desc, repo_name, generated_code, use_grounding)
                 # No return needed

             except Exception as e:
                 history = add_bot_message(history, f"❌ Error generating code: {e}. Click 'reset'.")
                 # Yield error message and reset state on failure
                 yield (history, None, STATE_IDLE, updated_preview, updated_run, updated_build, 0,
                       None, None, None, use_grounding)
                 # No return needed


        elif state == STATE_UPLOADING_APP_PY:
             # Retrieve the generated code from the state variable
             code_to_upload = generated_code
             if not code_to_upload:
                  history = add_bot_message(history, "Internal error: No code to upload. Resetting.")
                  yield (history, None, STATE_IDLE, updated_preview, updated_run, updated_build, 0,
                        None, None, None, use_grounding)
                  # No return needed

             else:
                 history = add_bot_message(history, "☁️ Uploading `app.py`...")
                 # Yield to show message before the upload action (pass UI outputs and states through)
                 yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                        attempts, app_desc, repo_name, generated_code, use_grounding)

                 try:
                     # Perform the file upload action
                     upload_file_to_space_action(io.StringIO(code_to_upload), "app.py", repo_id, hf_profile, hf_token)
                     history = add_bot_message(history, "✅ Uploaded `app.py`. Click 'Send' to generate requirements.")
                     state = STATE_GENERATING_REQUIREMENTS # Transition state
                     generated_code = None # Clear the stored code after use to free memory/state space
                     # Yield updated state variables and history (pass UI outputs and other states through)
                     yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                            attempts, app_desc, repo_name, generated_code, use_grounding)
                     # No return needed

                 except Exception as e:
                     history = add_bot_message(history, f"❌ Error uploading `app.py`: {e}. Click 'reset'.")
                     # Yield error message and reset state on failure
                     yield (history, None, STATE_IDLE, updated_preview, updated_run, updated_build, 0,
                           None, None, None, use_grounding)
                     # No return needed


        elif state == STATE_GENERATING_REQUIREMENTS:
             history = add_bot_message(history, "📄 Generating `requirements.txt`...")
             # Yield to show message before generating requirements (pass UI outputs and states through)
             yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                    attempts, app_desc, repo_name, generated_code, use_grounding)

             # Logic to determine required packages based on SDK and keywords in the app description
             reqs_list = ["gradio"] if space_sdk == "gradio" else ["streamlit"]
             # Add essential libraries regardless of description keywords or grounding
             essential_libs = ["google-generativeai", "huggingface_hub"]
             # Only add if Gemini is actually needed for the app (determined by description or if key is present)
             # If we are here, key and model are available based on STATE_IDLE checks
             reqs_list.extend(essential_libs)

             # Add common libraries if description suggests they might be needed
             if app_desc:
                 app_desc_lower = app_desc.lower()
                 if "requests" in app_desc_lower or "api" in app_desc_lower:
                      reqs_list.append("requests")
                 # Image processing libraries
                 if "image" in app_desc_lower or "upload" in app_desc_lower or "blur" in app_desc_lower or "vision" in app_desc_lower or "photo" in app_desc_lower:
                     reqs_list.append("Pillow")
                 if "numpy" in app_desc_lower: reqs_list.append("numpy")
                 if "pandas" in app_desc_lower or "dataframe" in app_desc_lower: reqs_list.append("pandas")
                 # Add scikit-image and opencv if image processing is heavily implied
                 if any(lib in app_desc_lower for lib in ["scikit-image", "skimage", "cv2", "opencv"]):
                      reqs_list.extend(["scikit-image", "opencv-python"]) # Note: opencv-python for pip
                 # Add transformers if large models are implied
                 if any(lib in app_desc_lower for lib in ["transformer", "llama", "mistral", "bert", "gpt2"]):
                     reqs_list.append("transformers")
                 # Add torch or tensorflow if deep learning frameworks are implied
                 if any(lib in app_desc_lower for lib in ["torch", "pytorch", "tensorflow", "keras"]):
                     reqs_list.extend(["torch", "tensorflow"]) # Users might need specific versions, but this is a start

             # Use dict.fromkeys to get unique items while preserving insertion order (Python 3.7+)
             reqs_list = list(dict.fromkeys(reqs_list))
             # Sort alphabetically for cleaner requirements.txt
             reqs_list.sort()

             reqs_content = "\n".join(reqs_list) + "\n"

             history = add_bot_message(history, "✅ `requirements.txt` generated. Click 'Send' to upload.")
             state = STATE_UPLOADING_REQUIREMENTS # Transition state
             generated_code = reqs_content # Store requirements content
             # Yield updated state variables and history (pass UI outputs and other states through)
             yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                    attempts, app_desc, repo_name, generated_code, use_grounding)
                     # No return needed


        elif state == STATE_UPLOADING_REQUIREMENTS:
            # Retrieve requirements content from state variable
            reqs_content_to_upload = generated_code
            if not reqs_content_to_upload:
                 history = add_bot_message(history, "Internal error: No requirements content to upload. Resetting.")
                 yield (history, None, STATE_IDLE, updated_preview, updated_run, updated_build, 0,
                        None, None, None, use_grounding)
                 # No return needed

            else:
                 history = add_bot_message(history, "☁️ Uploading `requirements.txt`...")
                 # Yield message before upload (pass UI outputs and states through)
                 yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                        attempts, app_desc, repo_name, generated_code, use_grounding)

                 try:
                     # Perform requirements file upload
                     upload_file_to_space_action(io.StringIO(reqs_content_to_upload), "requirements.txt", repo_id, hf_profile, hf_token)
                     history = add_bot_message(history, "✅ Uploaded `requirements.txt`. Click 'Send' to generate README.")
                     state = STATE_GENERATING_README # Transition state
                     generated_code = None # Clear content after use
                     # Yield updated state variables and history (pass UI outputs and other states through)
                     yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                            attempts, app_desc, repo_name, generated_code, use_grounding)
                     # No return needed

                 except Exception as e:
                     history = add_bot_message(history, f"❌ Error uploading `requirements.txt`: {e}. Click 'reset'.")
                     # Yield error message and reset state on failure
                     yield (history, None, STATE_IDLE, updated_preview, updated_run, updated_build, 0,
                           None, None, None, use_grounding)
                     # No return needed

        elif state == STATE_GENERATING_README:
            history = add_bot_message(history, "📝 Generating `README.md`...")
            # Yield message before generating README (pass UI outputs and states through)
            yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                   attempts, app_desc, repo_name, generated_code, use_grounding)

            # Generate simple README content with Space metadata header
            readme_title = repo_name if repo_name else "My Awesome Space"
            readme_description = app_desc if app_desc else f"This Hugging Face Space hosts an AI-generated {space_sdk} application."

            readme_content = f"""---
title: {readme_title}
emoji: 🚀
colorFrom: blue
colorTo: yellow
sdk: {space_sdk}
app_file: app.py
pinned: false
---

# {readme_title}

{readme_description}

This Space was automatically generated by an AI workflow using Google Gemini and Gradio.
""" # Added Space metadata header and slightly improved content

            history = add_bot_message(history, "✅ `README.md` generated. Click 'Send' to upload.")
            state = STATE_UPLOADING_README # Transition state
            generated_code = readme_content # Store README content
            # Yield updated state variables and history (pass UI outputs and other states through)
            yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                   attempts, app_desc, repo_name, generated_code, use_grounding)
            # No return needed


        elif state == STATE_UPLOADING_README:
            # Retrieve README content from state variable
            readme_content_to_upload = generated_code
            if not readme_content_to_upload:
                 history = add_bot_message(history, "Internal error: No README content to upload. Resetting.")
                 yield (history, None, STATE_IDLE, updated_preview, updated_run, updated_build, 0,
                        None, None, None, use_grounding)
                 # No return needed

            else:
                 history = add_bot_message(history, "☁️ Uploading `README.md`...")
                 # Yield message before upload (pass UI outputs and states through)
                 yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                        attempts, app_desc, repo_name, generated_code, use_grounding)

                 try:
                     # Perform README file upload
                     upload_file_to_space_action(io.StringIO(readme_content_to_upload), "README.md", repo_id, hf_profile, hf_token)
                     history = add_bot_message(history, "✅ Uploaded `README.md`. All files uploaded. Space is now building. Click 'Send' to check build logs.")
                     state = STATE_CHECKING_LOGS_BUILD # Transition to checking build logs
                     generated_code = None # Clear content after use
                     # Yield updated state variables and history (pass UI outputs and other states through)
                     yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                            attempts, app_desc, repo_name, generated_code, use_grounding)
                     # No return needed

                 except Exception as e:
                     history = add_bot_message(history, f"❌ Error uploading `README.md`: {e}. Click 'reset'.")
                     # Yield error message and reset state on failure
                     yield (history, None, STATE_IDLE, updated_preview, updated_run, updated_build, 0,
                           None, None, None, use_grounding)
                     # No return needed

        elif state == STATE_CHECKING_LOGS_BUILD:
             history = add_bot_message(history, "🔍 Fetching build logs...")
             # Yield message before fetching logs (which includes a delay) (pass UI outputs and states through)
             yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                    attempts, app_desc, repo_name, generated_code, use_grounding)

             # Fetch build logs from HF Space
             build_logs_text = get_build_logs_action(repo_id, hf_profile, hf_token)
             updated_build = build_logs_text # Update the logs display variable

             # Simple check for common error indicators in logs (case-insensitive)
             if "error" in updated_build.lower() or "exception" in updated_build.lower() or "build failed" in updated_build.lower():
                  history = add_bot_message(history, "⚠️ Build logs indicate potential issues. Please inspect above. Click 'Send' to check container logs (app might still start despite build warnings).")
                  state = STATE_CHECKING_LOGS_RUN # Transition even on build error, to see if container starts
                  # Yield updated state, logs, and variables
                  yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                         attempts, app_desc, repo_name, generated_code, use_grounding)
                  # No return needed

             else:
                  history = add_bot_message(history, "✅ Build logs fetched. Click 'Send' to check container logs.")
                  state = STATE_CHECKING_LOGS_RUN # Transition to next log check
                  # Yield updated state, logs, and variables
                  yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                         attempts, app_desc, repo_name, generated_code, use_grounding)
                  # No return needed


        elif state == STATE_CHECKING_LOGS_RUN:
             history = add_bot_message(history, "🔍 Fetching container logs...")
             # Yield message before fetching logs (includes a delay) (pass UI outputs and states through)
             yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                    attempts, app_desc, repo_name, generated_code, use_grounding)

             # Fetch container logs from HF Space
             container_logs_text = get_container_logs_action(repo_id, hf_profile, hf_token)
             updated_run = container_logs_text # Update the logs display variable

             # Check for errors in run logs and if we have debug attempts left
             if ("error" in updated_run.lower() or "exception" in updated_run.lower()) and attempts < MAX_DEBUG_ATTEMPTS:
                  attempts += 1 # Increment debug attempts counter
                  history = add_bot_message(history, f"❌ Errors detected in container logs. Attempting debug fix #{attempts}/{MAX_DEBUG_ATTEMPTS}. Click 'Send' to proceed.")
                  state = STATE_DEBUGGING_CODE # Transition to the debugging state
                  # Yield updated state, logs, attempts, and variables
                  yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                         attempts, app_desc, repo_name, generated_code, use_grounding)
                  # No return needed

             elif ("error" in updated_run.lower() or "exception" in updated_run.lower()) and attempts >= MAX_DEBUG_ATTEMPTS:
                  # Max debug attempts reached
                  history = add_bot_message(history, f"❌ Errors detected in container logs. Max debug attempts ({MAX_DEBUG_ATTEMPTS}) reached. Please inspect logs manually or click 'reset'.")
                  state = STATE_COMPLETE # Workflow ends on failure after attempts
                  # Yield updated state, logs, attempts, and variables
                  yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                         attempts, app_desc, repo_name, generated_code, use_grounding)
                  # No return needed

             else:
                  # No significant errors found in logs, assume success
                  history = add_bot_message(history, "✅ App appears to be running successfully! Check the iframe above. Click 'reset' to start a new project.")
                  state = STATE_COMPLETE # Workflow ends on success
                  # Yield updated state, logs, attempts, and variables
                  yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                         attempts, app_desc, repo_name, generated_code, use_grounding)
                  # No return needed


        elif state == STATE_DEBUGGING_CODE:
             history = add_bot_message(history, f"🧠 Calling Gemini to generate fix based on logs...")
             if use_grounding:
                  history = add_bot_message(history, "(Using Grounding with Google Search)")
             # Yield message before Gemini API call (pass UI outputs and states through)
             yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                    attempts, app_desc, repo_name, generated_code, use_grounding)

             # Construct prompt for Gemini including the container logs
             debug_prompt = f"""
You are debugging a {space_sdk} Space. The goal is to fix the code in `app.py` based on the container logs provided.

Here are the container logs:
Use code with caution.
Python
{updated_run}
Generate the *complete, fixed* content for `app.py` based on these logs.
Return **only** the python code block for app.py. Do not include any extra text, explanations, or markdown outside the code block.
"""
             try:
                  # Call Gemini to generate the corrected code, optionally using grounding
                  # Note: Grounding might be less effective for debugging based *only* on logs,
                  # but we include the option as requested.
                  fix_code = call_gemini(debug_prompt, gemini_api_key, gemini_model, use_grounding=use_grounding)
                  fix_code = fix_code.strip()
                  # Clean up potential markdown formatting
                  if fix_code.startswith("```python"):
                      fix_code = fix_code[len("```python"):].strip()
                  if fix_code.startswith("```"):
                       fix_code = fix_code[len("```"):].strip()
                  if fix_code.endswith("```"):
                      fix_code = fix_code[:-len("```")].strip()

                  if not fix_code:
                     raise ValueError("Gemini returned empty fix code.")

                  history = add_bot_message(history, "✅ Fix code generated. Click 'Send' to upload.")
                  state = STATE_UPLOADING_FIXED_APP_PY # Transition to the upload state for the fix
                  generated_code = fix_code # Store the generated fix code
                  # Yield updated state, code, and variables (pass UI outputs and states through)
                  yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                         attempts, app_desc, repo_name, generated_code, use_grounding)
                  # No return needed

             except Exception as e:
                  history = add_bot_message(history, f"❌ Error generating debug code: {e}. Click 'reset'.")
                  # Yield error message and reset state on failure
                  yield (history, None, STATE_IDLE, updated_preview, updated_run, updated_build, 0,
                         None, None, None, use_grounding)
                  # No return needed

        elif state == STATE_UPLOADING_FIXED_APP_PY:
             # Retrieve the fixed code from the state variable
             fixed_code_to_upload = generated_code
             if not fixed_code_to_upload:
                  history = add_bot_message(history, "Internal error: No fixed code available to upload. Resetting.")
                  yield (history, None, STATE_IDLE, updated_preview, updated_run, updated_build, 0,
                        None, None, None, use_grounding)
                  # No return needed

             else:
                  history = add_bot_message(history, "☁️ Uploading fixed `app.py`...")
                  # Yield message before upload (pass UI outputs and states through)
                  yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                         attempts, app_desc, repo_name, generated_code, use_grounding)

                  try:
                      # Perform the upload of the fixed app.py
                      upload_file_to_space_action(io.StringIO(fixed_code_to_upload), "app.py", repo_id, hf_profile, hf_token)
                      history = add_bot_message(history, "✅ Fixed `app.py` uploaded. Space will rebuild. Click 'Send' to check logs again.")
                      state = STATE_CHECKING_LOGS_RUN # Go back to checking run logs after uploading the fix
                      generated_code = None # Clear code after use
                      # Yield updated state, code, and variables (pass UI outputs and states through)
                      yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                             attempts, app_desc, repo_name, generated_code, use_grounding)
                      # No return needed

                  except Exception as e:
                      history = add_bot_message(history, f"❌ Error uploading fixed `app.py`: {e}. Click 'reset'.")
                      # Yield error message and reset state on failure
                      yield (history, None, STATE_IDLE, updated_preview, updated_run, updated_build, 0,
                            None, None, None, use_grounding)
                      # No return needed

        elif state == STATE_COMPLETE:
             # If in the complete state, the workflow is finished for this project.
             # Subsequent clicks just add user messages; we simply yield the current state.
             yield (history, repo_id, state, updated_preview, updated_run, updated_build,
                    attempts, app_desc, repo_name, generated_code, use_grounding)
             # No return needed


    except Exception as e:
        # This catches any unexpected errors that occur within any state's logic
        error_message = f"Workflow step failed unexpectedly ({state}): {e}. Click 'Send' to re-attempt this step or 'reset'."
        history = add_bot_message(history, error_message)
        print(f"Critical Error in state {state}: {e}") # Log the error for debugging purposes
        # Yield an error state and reset essential workflow variables on critical failure
        yield (history, None, STATE_IDLE, updated_preview, updated_run, updated_build, 0,
               None, None, None, use_grounding) # Include use_grounding
        # No return needed after yield


# --- Build the Gradio UI ---

with gr.Blocks(title="AI-Powered HF Space App Builder") as ai_builder_tab:
    # Gradio State variables - these persist their values across user interactions (clicks)
    # Define these first as they might be used in default values for components
    hf_profile   = gr.State(None)
    hf_token     = gr.State(None)
    # FIX: Initialize gemini_key state from env var on load
    gemini_key   = gr.State(os.environ.get("GOOGLE_API_KEY"))
    gemini_model = gr.State("gemini-1.5-flash") # Default selected model
    repo_id      = gr.State(None) # Stores the ID of the created Space
    workflow     = gr.State(STATE_IDLE) # Stores the current state of the AI workflow
    sdk_state    = gr.State("gradio") # Stores the selected Space SDK (Gradio or Streamlit)
    debug_attempts = gr.State(0) # Counter for how many debugging attempts have been made
    app_description = gr.State(None) # Stores the user's initial description of the desired app
    repo_name_state = gr.State(None) # Stores the chosen repository name for the Space
    generated_code_state = gr.State(None) # Temporary storage for generated file content (app.py, reqs, README)
    # New State variable for grounding checkbox
    use_grounding_state = gr.State(False)

    with gr.Row():
        # Sidebar column for inputs and status displays
        with gr.Column(scale=1, min_width=300):
            gr.Markdown("## Hugging Face Login")
            # Define login_status before it's used in login_btn.click outputs
            login_status = gr.Markdown("*Not logged in.*")
            # Hugging Face Login Button
            login_btn    = gr.LoginButton(variant="huggingface")

            gr.Markdown("## Google AI Studio / Gemini")
            # Define gemini_input and gemini_status before they are used in change handlers
            gemini_input  = gr.Textbox(
                label="API Key",
                type="password", # Hides input for security
                interactive=True,
                value=os.environ.get("GOOGLE_API_KEY"), # Pre-fill if GOOGLE_API_KEY env var is set
                info="Get your key from Google AI Studio"
            )
            gemini_status = gr.Markdown("") # Display Gemini configuration status

            # Define model_selector before it's used in its change handler
            model_selector = gr.Radio(
                choices=[
                    ("Gemini 1.5 Flash", "gemini-1.5-flash"),
                    ("Gemini 1.5 Pro", "gemini-1.5-pro"),
                    ("Gemini 1.0 Pro", "gemini-1.0-pro"),
                ],
                value="gemini-1.5-flash", # Default selection
                label="Select model",
                interactive=True
            )

            # Define grounding checkbox before its change handler
            use_grounding_checkbox = gr.Checkbox(
                label="Enable Grounding with Google Search",
                value=False, # Default to off
                interactive=True,
                info="Use Google Search results to inform Gemini's response (may improve factuality)."
            )

            gr.Markdown("## Space SDK")
            # Define sdk_selector before its change handler
            sdk_selector = gr.Radio(choices=["gradio","streamlit"], value="gradio", label="Template SDK", interactive=True)

            gr.Markdown("## Workflow Status")
            # Define status_text and repo_id_text before they are used in change handlers
            status_text = gr.Textbox(label="Current State", value=STATE_IDLE, interactive=False)
            repo_id_text = gr.Textbox(label="Current Space ID", value="None", interactive=False)


        # Main content area column
        with gr.Column(scale=3):
            # Define chatbot, user_input, send_btn before send_btn.click
            chatbot    = gr.Chatbot(type='messages', label="AI Workflow Chat")
            user_input = gr.Textbox(placeholder="Type your message…", interactive=True)
            # Define send_btn before its click handler
            # Initial interactive state will be handled by the load event chain
            send_btn   = gr.Button("Send") # Starts disabled by default (interactive=False)


            # Define iframe, build_txt, run_txt before they are used in send_btn.click inputs/outputs
            # These are UI components, NOT State variables
            iframe    = gr.HTML("<p>No Space created yet.</p>") # HTML element for the Space iframe
            build_txt = gr.Textbox(label="Build Logs", lines=10, interactive=False, value="", max_lines=20) # Set max_lines for scrollability
            run_txt   = gr.Textbox(label="Container Logs", lines=10, interactive=False, value="", max_lines=20) # Set max_lines for scrollability


            # --- Define Event Handlers and Chains AFTER all components are defined ---

            # Define the inputs used for checking prerequisites
            send_button_interactive_binding_inputs = [hf_profile, hf_token, gemini_key, gemini_model]

            # Handle login button click: Update profile/token state -> Check prereqs and update button interactivity
            # LoginButton outputs a tuple (OAuthProfile, OAuthToken) on success
            login_btn.click(
                 lambda x: (x[0], x[1]),
                 inputs=[login_btn],
                 outputs=[hf_profile, hf_token] # Update HF State variables
            ).then( # Chain the next action after state is updated
                 # Call the update function and bind its output to the button component
                 check_send_button_ready,
                 inputs=send_button_interactive_binding_inputs,
                 outputs=[send_btn] # Update button interactivity using gr.update return value
            )

            # Handle Gemini Key Input change: Update key state -> Configure Gemini status -> Check prereqs and update button interactivity
            gemini_input.change(
                lambda k: k, inputs=[gemini_input], outputs=[gemini_key] # Update gemini_key state
            ).then(
                configure_gemini, inputs=[gemini_key, gemini_model], outputs=[gemini_status] # Update Gemini status
            ).then(
                # Call the update function and bind its output to the button component
                check_send_button_ready,
                inputs=send_button_interactive_binding_inputs,
                outputs=[send_btn] # Update button interactivity using gr.update return value
            )

            # Handle Gemini Model Selector change: Update model state -> Configure Gemini status -> Check prereqs and update button interactivity
            model_selector.change(
                lambda m: m, inputs=[model_selector], outputs=[gemini_model] # Update gemini_model state
            ).then(
                configure_gemini, inputs=[gemini_key, gemini_model], outputs=[gemini_status] # Update Gemini status
            ).then(
                # Call the update function and bind its output to the button component
                check_send_button_ready,
                inputs=send_button_interactive_binding_inputs,
                outputs=[send_btn] # Update button interactivity using gr.update return value
            )

            # Handle Grounding checkbox change: update grounding state
            use_grounding_checkbox.change(
                lambda v: v, inputs=use_grounding_checkbox, outputs=use_grounding_state
            )

            # Handle SDK selector change: update sdk state
            sdk_selector.change(
                lambda s: s, inputs=sdk_selector, outputs=sdk_state
            )

            # Link Workflow State variable change to UI status display
            workflow.change(lambda s: s, inputs=workflow, outputs=status_text)

            # Link Repo ID State variable change to UI status display
            repo_id.change(lambda r: r if r else "None", inputs=repo_id, outputs=repo_id_text)


            # The main event handler for the Send button
            # This .click() event triggers the ai_workflow_chat generator function
            # Inputs are read from UI components AND State variables
            # Outputs are updated by the values yielded from the generator
            send_btn.click(
                ai_workflow_chat, # The generator function to run
                inputs=[
                    user_input, chatbot, # UI component inputs (message, current chat history)
                    hf_profile, hf_token, # HF State variables
                    gemini_key, gemini_model, # Gemini State variables
                    repo_id, workflow, sdk_state, # Workflow State variables
                    # UI component inputs whose *current values* are needed by the generator
                    # These are NOT State variables with the same names
                    iframe, run_txt, build_txt,
                    debug_attempts, app_description, repo_name_state, generated_code_state, # Other State variables
                    use_grounding_state # Add the new grounding state input
                ],
                outputs=[
                    chatbot, # Update Chatbot with new messages
                    repo_id, workflow, # Update workflow State variables
                    iframe, run_txt, build_txt, # Update UI component outputs
                    debug_attempts, app_description, repo_name_state, generated_code_state, # Update other State variables
                    use_grounding_state # Update the grounding state output (generators must yield/return all state they modify/pass through)
                ]
            ).success( # Chain a .success() event to run *after* the .click() handler completes without error
                 # Clear the user input textbox after the message is sent and processed
                 lambda: gr.update(value=""),
                 inputs=None,
                 outputs=user_input # Update the user input textbox
            )


    # --- Initial Load Event Chain (Defined INSIDE gr.Blocks, AFTER components and initial bindings) ---
    # This chain runs once when the app loads
    ai_builder_tab.load(
        # Action 1: Show profile (loads cached login if available), does NOT need inputs
        show_profile,
        inputs=None,
        outputs=login_status # Update login status markdown
    ).then(
        # Action 2: Configure Gemini using the initial state values (from env var if set)
        # This uses the *initial* value of gemini_key and gemini_model state variables
        configure_gemini,
        inputs=[gemini_key, gemini_model],
        outputs=[gemini_status] # Update Gemini status display
    ).then(
        # Action 3: Check prereqs and update send button interactivity based on initial states
        # This uses the *initial* values of hf_profile, hf_token, gemini_key, and gemini_model states
        check_send_button_ready, # Use the updated helper function
        inputs=send_button_interactive_binding_inputs,
        outputs=[send_btn] # Update button interactivity using gr.update return value
    ).then(
        # Action 4: Add the initial welcome message to the chatbot
        greet,
        inputs=None,
        outputs=chatbot
    )


# The main workflow function and other helper functions are correctly defined OUTSIDE the gr.Blocks context
# because they operate on the *values* passed to them by Gradio event triggers, not the UI component objects themselves.


if __name__ == "__main__":
    # Optional: Configure retries for huggingface_hub requests to make them more robust
    # from requests.adapters import HTTPAdapter
    # from urllib3.util.retry import Retry
    # retry_strategy = Retry(total=5, backoff_factor=1, status_forcelist=[429, 500, 502, 503, 504]) # Define retry strategy for specific HTTP codes
    # adapter = HTTPAdapter(max_retries=retry_strategy)
    # session = get_session() # Get the session object used internally by huggingface_hub
    # session.mount("http://", adapter)
    # session.mount("https://", adapter)

    # Optional: Configure Gradio settings using environment variables
    # Set max upload size (e.g., 100MB) for files like app.py
    os.environ["GRADIO_MAX_FILE_SIZE"] = "100MB"
    # Optional: Set a local temporary directory for Gradio uploads
    os.environ["GRADIO_TEMP_DIR"] = "./tmp"
    os.makedirs(os.environ["GRADIO_TEMP_DIR"], exist_ok=True) # Ensure the directory exists

    # Launch the Gradio UI
    # The Gradio launch call blocks execution.
    ai_builder_tab.launch()