app.py

import gradio as gr
import os
import zipfile
import time
import uuid # For unique filenames

# --- LLM/Model Setup ---
from transformers import pipeline as transformers_pipeline # For local list generation
from huggingface_hub import InferenceClient # For prompt refinement via API
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler # For image generation
from gradio_client import Client as GradioClient, handle_file # For 3D generation

# --- Configuration ---
# Consider making these configurable in the UI later
LIST_GENERATION_MODEL = "google/flan-t5-base" # Or another suitable small model
PROMPT_REFINEMENT_MODEL_API = "mistralai/Mixtral-8x7B-Instruct-v0.1" # Or another instruct model via Inference API
IMAGE_GENERATION_MODEL = "stabilityai/stable-diffusion-xl-base-1.0" # Or "runwayml/stable-diffusion-v1-5"
HUNYUAN_SPACE_ID = "tencent/Hunyuan3D-2"
OUTPUT_DIR = "outputs"
MODELS_SUBDIR = "3d_models"
IMAGES_SUBDIR = "image_previews"
ZIP_FILENAME = "3d_collection.zip"

# --- Initialize Clients/Pipelines (can be slow, consider loading on demand if needed) ---

# Use HF Token from Space secrets if available/needed for Inference API
HF_TOKEN = os.environ.get("HUGGINGFACE_TOKEN", None)

# Basic List Generator (local)
try:
    list_generator = transformers_pipeline("text2text-generation", model=LIST_GENERATION_MODEL)
except Exception as e:
    print(f"Warning: Could not load local list generator {LIST_GENERATION_MODEL}: {e}")
    list_generator = None

# Prompt Refiner (API)
try:
    if not HF_TOKEN:
        print("Warning: HUGGINGFACE_TOKEN not set. Inference API calls might be rate-limited or fail.")
    prompt_refiner = InferenceClient(model=PROMPT_REFINEMENT_MODEL_API, token=HF_TOKEN)
except Exception as e:
    print(f"Warning: Could not initialize InferenceClient for {PROMPT_REFINEMENT_MODEL_API}: {e}")
    prompt_refiner = None

# Image Generator (Local with Diffusers - requires GPU on Space for reasonable speed)
# Or consider an Image Gen API service if running on CPU hardware
try:
    # Using XL as an example - adjust based on available hardware
    image_pipeline = StableDiffusionPipeline.from_pretrained(IMAGE_GENERATION_MODEL, torch_dtype=torch.float16, use_safetensors=True)
    # Move to GPU if available (check Space hardware)
    # image_pipeline.to("cuda") # Uncomment if GPU is available
    image_pipeline.scheduler = EulerDiscreteScheduler.from_config(image_pipeline.scheduler.config)
except Exception as e:
    print(f"Warning: Could not load diffusers pipeline {IMAGE_GENERATION_MODEL}. Image generation might fail: {e}")
    image_pipeline = None


# 3D Generator Client
try:
    hunyuan_client = GradioClient(HUNYUAN_SPACE_ID)
except Exception as e:
    print(f"Error initializing GradioClient for {HUNYUAN_SPACE_ID}: {e}")
    hunyuan_client = None

# --- Helper Functions ---

def generate_list_local(theme, count):
    if not list_generator:
        return ["Error: List generator model not loaded."]
    prompt = f"Generate a comma-separated list of {count} distinct types of {theme}."
    try:
        result = list_generator(prompt, max_length=200)[0]['generated_text']
        items = [item.strip() for item in result.split(',') if item.strip()]
        return items[:count] # Ensure we don't exceed the requested count
    except Exception as e:
        print(f"Error generating list: {e}")
        return [f"Error: {e}"]

def refine_prompt_api(item_name):
    if not prompt_refiner:
        return f"A 3D model of a {item_name}" # Fallback basic prompt
    prompt = f"Create a detailed, descriptive prompt for generating a highly realistic image of a single '{item_name}'. Focus on visual details suitable for a text-to-image AI. Only output the prompt itself."
    try:
        refined = prompt_refiner.text_generation(prompt, max_new_tokens=100)
        # Clean up potential API artifacts if necessary
        refined = refined.strip().strip('"')
        return refined
    except Exception as e:
        print(f"Error refining prompt for '{item_name}': {e}")
        # Fallback to a simpler prompt for 3D generation if refinement fails
        return f"A high quality 3D model of a {item_name}"

def generate_image_local(refined_prompt, output_path):
    if not image_pipeline:
        print("Image generation pipeline not available.")
        # Create a placeholder image or return None
        # Example: from PIL import Image; img = Image.new('RGB', (60, 30), color = 'red'); img.save(output_path); return output_path
        return None
    try:
        # Adjust inference steps/guidance as needed
        image = image_pipeline(refined_prompt, num_inference_steps=25, guidance_scale=7.5).images[0]
        image.save(output_path)
        return output_path
    except Exception as e:
        print(f"Error generating image for prompt '{refined_prompt}': {e}")
        return None

def generate_3d_model_hunyuan(refined_prompt_for_3d, output_dir, item_name_safe):
    if not hunyuan_client:
        print("Hunyuan 3D client not available.")
        return None, "Client not initialized"

    print(f"Requesting 3D model for: {refined_prompt_for_3d}")
    # Use defaults for most parameters initially
    try:
        result_tuple = hunyuan_client.predict(
                caption=refined_prompt_for_3d,
                # Leave image and mv_image inputs as None for text-to-3D
                image=None,
                mv_image_front=None,
                mv_image_back=None,
                mv_image_left=None,
                mv_image_right=None,
                # Default values from API docs (can be overridden)
                steps=30,
                guidance_scale=5,
                seed=1234, # Or use randomize_seed=True
                octree_resolution=256,
                check_box_rembg=True,
                num_chunks=8000,
                randomize_seed=True,
                api_name="/generation_all" # Crucial!
        )

        # --- VERIFICATION NEEDED ---
        # Check the actual return tuple structure. Assuming file path is first or second.
        # Let's try the first element (index 0). If it's None or not a path, try index 1.
        raw_filepath = None
        if result_tuple and len(result_tuple) > 0 and isinstance(result_tuple[0], str):
             raw_filepath = result_tuple[0]
        elif result_tuple and len(result_tuple) > 1 and isinstance(result_tuple[1], str):
             print("Using second element from result tuple for filepath.")
             raw_filepath = result_tuple[1]
        # --- END VERIFICATION NEEDED ---

        if raw_filepath:
            print(f"Job completed. Raw result path: {raw_filepath}")
            os.makedirs(output_dir, exist_ok=True)

            # Download the file using handle_file which manages temp paths etc.
            # handle_file saves with a potentially random name in download_dir
            downloaded_temp_path = handle_file(raw_filepath, download_dir=output_dir)

            if downloaded_temp_path and os.path.exists(downloaded_temp_path):
                # Rename it to something meaningful
                file_ext = os.path.splitext(downloaded_temp_path)[1] # Get extension (.glb, .obj?)
                if not file_ext: file_ext = ".glb" # Assume glb if unknown
                final_path = os.path.join(output_dir, f"{item_name_safe}{file_ext}")
                os.rename(downloaded_temp_path, final_path)
                print(f"Model saved to: {final_path}")
                return final_path, "Success"
            else:
                 error_msg = f"handle_file failed to download or returned invalid path: {downloaded_temp_path}"
                 print(error_msg)
                 return None, error_msg
        else:
            error_msg = f"Job for '{refined_prompt_for_3d}' did not return a valid filepath in expected tuple elements."
            print(error_msg)
            # You might want to inspect the full result_tuple here for debugging
            print(f"Full result tuple: {result_tuple}")
            return None, error_msg

    except Exception as e:
        error_msg = f"Error calling Hunyuan3D API for '{refined_prompt_for_3d}': {e}"
        print(error_msg)
        return None, str(e)

def create_zip(files_to_zip, zip_filepath):
    with zipfile.ZipFile(zip_filepath, 'w') as zf:
        for file_path in files_to_zip:
            if file_path and os.path.exists(file_path):
                zf.write(file_path, os.path.basename(file_path))
    return zip_filepath


# --- Gradio Interface & Logic ---

with gr.Blocks() as demo:
    gr.Markdown("# 3D Asset Collection Generator")
    gr.Markdown("Generate a list based on a theme, refine prompts, preview images, and generate selected 3D models using Hunyuan3D-2.")
    if not HF_TOKEN:
        gr.Warning("Hugging Face Token not found. Prompt refinement quality/rate limits may be affected. Consider adding HUGGINGFACE_TOKEN to Space secrets.")
    if not image_pipeline:
        gr.Warning("Local Image Generation model failed to load. Image previews will be skipped. Check Space hardware/logs.")
    if not hunyuan_client:
        gr.Error("Failed to connect to the Hunyuan3D-2 Space. 3D generation will not work.")


    # State to hold intermediate results
    # Using gr.State is good for simple values, for complex lists/dicts might need alternatives or careful handling
    list_items_state = gr.State([])
    refined_prompts_state = gr.State({}) # Dict: {item_name: refined_prompt}
    image_paths_state = gr.State({}) # Dict: {item_name: image_path}
    selected_items_state = gr.State([]) # List of item_names selected by user
    generated_3d_files_state = gr.State([]) # List of paths to successfully generated models


    with gr.Row():
        theme_input = gr.Textbox(label="Theme", placeholder="e.g., reptiles, kitchen appliances, medieval weapons")
        count_input = gr.Number(label="Number of Items", value=5, minimum=1, step=1)

    generate_list_button = gr.Button("1. Generate List & Refine Prompts")
    list_output_display = gr.Markdown("List will appear here...") # Or use gr.DataFrame

    generate_images_button = gr.Button("2. Generate Image Previews", visible=False) # Hidden initially
    # Use Gallery for display, Dataset for selection tracking
    image_gallery = gr.Gallery(label="Image Previews", visible=False, elem_id="image_gallery")
    # Dataset to hold data for selection (item_name, image_path, refined_prompt)
    selection_data = gr.Dataset(components=[gr.Textbox(visible=False), gr.Textbox(visible=False), gr.Textbox(visible=False)], # item, img_path, prompt
                                 headers=["Item Name", "Image", "Prompt"],
                                 label="Select Items for 3D Generation",
                                 visible=False)


    generate_3d_button = gr.Button("3. Generate 3D Models for Selected Items", visible=False) # Hidden initially
    status_output = gr.Markdown("") # For progress updates
    final_zip_output = gr.File(label="Download 3D Model Collection (ZIP)", visible=False)


    # --- Event Logic ---

    def run_list_and_refine(theme, count):
        if not theme:
            return {list_output_display: "Please enter a theme.", generate_images_button: gr.Button(visible=False)}

        # Ensure output dirs exist
        os.makedirs(os.path.join(OUTPUT_DIR, IMAGES_SUBDIR), exist_ok=True)
        os.makedirs(os.path.join(OUTPUT_DIR, MODELS_SUBDIR), exist_ok=True)


        gr.Info("Generating list...")
        items = generate_list_local(theme, int(count))
        if not items or "Error:" in items[0]:
            return {list_output_display: f"Failed to generate list: {items[0] if items else 'Unknown error'}",
                    generate_images_button: gr.Button(visible=False)}

        list_items_state.value = items # Save items to state

        gr.Info("Refining prompts via API...")
        refined_prompts = {}
        output_md = "### Generated List & Refined Prompts:\n\n"
        for item in items:
            refined = refine_prompt_api(item)
            refined_prompts[item] = refined
            output_md += f"*   **{item}:** {refined}\n"

        refined_prompts_state.value = refined_prompts # Save refined prompts

        # Enable next step
        return {
            list_output_display: output_md,
            generate_images_button: gr.Button(visible=True) # Show image gen button
        }

    generate_list_button.click(
        fn=run_list_and_refine,
        inputs=[theme_input, count_input],
        outputs=[list_output_display, generate_images_button, list_items_state, refined_prompts_state] # Update state too
    )


    def run_image_generation(items, refined_prompts_dict):
        if not image_pipeline:
             # Skip image generation if pipeline not loaded
             gr.Warning("Image pipeline not loaded. Skipping image previews.")
             # Prepare data for selection without images
             selection_samples = [[item, "N/A", refined_prompts_dict.get(item, "")] for item in items]
             image_paths_state.value = {} # Clear image paths
             return {
                 image_gallery: gr.Gallery(visible=False),
                 selection_data: gr.Dataset(samples=selection_samples, visible=True),
                 generate_3d_button: gr.Button(visible=True) # Allow proceeding without previews
             }

        gr.Info("Generating image previews... (this may take a while)")
        image_paths = {}
        gallery_images = []
        selection_samples = [] # For the Dataset component

        img_dir = os.path.join(OUTPUT_DIR, IMAGES_SUBDIR)

        for item in items:
            refined_prompt = refined_prompts_dict.get(item, f"Image of {item}") # Get refined prompt
            safe_item_name = "".join(c if c.isalnum() else "_" for c in item)
            img_filename = f"{safe_item_name}_{uuid.uuid4()}.png"
            img_path = os.path.join(img_dir, img_filename)

            generated_path = generate_image_local(refined_prompt, img_path)

            if generated_path:
                image_paths[item] = generated_path
                gallery_images.append(generated_path)
                selection_samples.append([item, generated_path, refined_prompt])
            else:
                # Handle image generation failure - maybe add placeholder info
                selection_samples.append([item, "Failed", refined_prompt])
                # Optionally add a placeholder to gallery_images too

        image_paths_state.value = image_paths # Save image paths

        # Show gallery and selection dataset
        return {
            image_gallery: gr.Gallery(value=gallery_images, visible=True),
            selection_data: gr.Dataset(samples=selection_samples, visible=True),
            generate_3d_button: gr.Button(visible=True) # Show 3D gen button
        }

    generate_images_button.click(
        fn=run_image_generation,
        inputs=[list_items_state, refined_prompts_state],
        outputs=[image_gallery, selection_data, generate_3d_button, image_paths_state] # Update state
    )

    # Handler for when user makes selections in the Dataset
    # Note: Gradio's Dataset selection handling might require specific event listeners
    # or potentially using gr.CheckboxGroup or similar if Dataset selection is tricky.
    # For simplicity here, we assume we can get the selected indices/items.
    # A common pattern is to add a hidden Textbox updated by JS on selection,
    # or use the Dataset's 'select' event if available and robust.
    # Let's simulate getting selected *items* (requires correct component setup).
    # This part might need refinement based on Gradio version/behavior.

    # We'll trigger 3D generation directly from the button click for now,
    # assuming the selection_data component holds the necessary info and selection state.

    def run_3d_generation(selection_evt: gr.SelectData, all_items_data):
        if not hunyuan_client:
            return {status_output: "Hunyuan3D client not initialized. Cannot generate.", final_zip_output: gr.File(visible=False)}

        selected_indices = selection_evt.index if selection_evt else []
        if not selected_indices:
            return {status_output: "Please select items from the table above before generating 3D models.", final_zip_output: gr.File(visible=False)}

        # Extract selected items based on indices from the *current* data in the dataset
        selected_items_info = [all_items_data[i] for i in selected_indices] # Each item is [name, img_path, prompt]

        generated_files = []
        status_messages = ["### 3D Generation Status:\n"]

        model_dir = os.path.join(OUTPUT_DIR, MODELS_SUBDIR)

        total_selected = len(selected_items_info)
        for i, (item_name, _, refined_prompt) in enumerate(selected_items_info):
            current_status = f"({i+1}/{total_selected}) Generating model for: **{item_name}**..."
            print(current_status)
            status_messages.append(f"*   {current_status}")
            # Update UI status progressively
            yield {status_output: "\n".join(status_messages), final_zip_output: gr.File(visible=False)}

            # Adapt prompt slightly for 3D if desired, or use the image prompt directly
            prompt_for_3d = refined_prompt # Or customize: f"A high quality 3D model of {item_name}, {refined_prompt}"

            item_name_safe = "".join(c if c.isalnum() else "_" for c in item_name)

            # --- Retry Logic Placeholder ---
            max_retries = 1 # Example: allow 1 retry
            attempts = 0
            model_path = None
            last_error = "Unknown error"

            while attempts <= max_retries:
                attempts += 1
                if attempts > 1:
                     status_messages.append(f"    *   Retrying ({attempts-1}/{max_retries})...")
                     yield {status_output: "\n".join(status_messages)}
                     time.sleep(2) # Brief pause before retry

                model_path, msg = generate_3d_model_hunyuan(prompt_for_3d, model_dir, item_name_safe)
                last_error = msg
                if model_path:
                    generated_files.append(model_path)
                    status_messages.append(f"    *   Success! Model saved.")
                    break # Exit retry loop on success
                else:
                    status_messages.append(f"    *   Attempt {attempts} failed: {msg}")

            if not model_path:
                 status_messages.append(f"    *   **Failed** after {attempts} attempt(s). Last error: {last_error}")
            # --- End Retry Logic ---

            # Update UI status after each item
            yield {status_output: "\n".join(status_messages)}


        if generated_files:
            status_messages.append("\nCreating ZIP archive...")
            yield {status_output: "\n".join(status_messages)}
            zip_path = os.path.join(OUTPUT_DIR, ZIP_FILENAME)
            final_zip = create_zip(generated_files, zip_path)
            status_messages.append(f"\n**Collection ready!** Download '{ZIP_FILENAME}' below.")
            generated_3d_files_state.value = generated_files # Store final paths
            return {status_output: "\n".join(status_messages), final_zip_output: gr.File(value=final_zip, visible=True)}
        else:
            status_messages.append("\nNo 3D models were successfully generated.")
            return {status_output: "\n".join(status_messages), final_zip_output: gr.File(visible=False)}


    # Link the button click to the generator function
    # The 'select' event on Dataset provides selection info (gr.SelectData)
    # We pass both the selection event data and the full dataset content
    generate_3d_button.click(
        fn=run_3d_generation,
        inputs=[selection_data, selection_data], # Pass dataset twice: once for select event, once for full data access
        outputs=[status_output, final_zip_output, generated_3d_files_state] # Update state
    )


# Launch the Gradio app
demo.queue().launch(debug=True) # Enable queue for longer processes, debug for detailed errors