app.py

import os
import gc
import torch
import gradio as ui
import spaces  # ADDED: Hugging Face Spaces SDK for ZeroGPU support
from transformers import pipeline, TextIteratorStreamer
from threading import Thread

# 1. Initialize Pipeline
MODEL_ID = "Xerv-AI/tarn"

print("Loading tarn architecture into memory...")

# FIX: Replaced 'torch_dtype' with 'dtype' to clear the transformers deprecation warning.
pipe = pipeline(
    "image-text-to-text", 
    model=MODEL_ID, 
    model_kwargs={
        "dtype": torch.float16, 
        "device_map": "auto"
    }
)
print("tarn is fully initialized.")

# 2. Define the Inference Logic
@spaces.GPU  # ADDED: Required by HF Spaces to allocate ZeroGPU dynamically
def process_chat(message, history):
    """
    Handles incoming messages (both images and text), formats them into 
    the proper Qwen 3.5 VL structure, and yields streamed tokens.
    """
    # Force clean any leftover GPU allocation trash before processing
    gc.collect()
    torch.cuda.empty_cache()

    # Reconstruct history into Hugging Face chat template format
    formatted_messages = []
    
    # Process previous conversation turns
    for user_turn, assistant_turn in history:
        if user_turn:
            # Check if user input in history was a file/image dict or text
            if isinstance(user_turn, dict) or (isinstance(user_turn, tuple) and len(user_turn) == 1):
                img_path = user_turn[0] if isinstance(user_turn, tuple) else user_turn.get("path")
                formatted_messages.append({"role": "user", "content": [{"type": "image", "url": img_path}]})
            else:
                formatted_messages.append({"role": "user", "content": [{"type": "text", "text": str(user_turn)}]})
        if assistant_turn:
            formatted_messages.append({"role": "assistant", "content": [{"type": "text", "text": str(assistant_turn)}]})

    # Append current incoming turn
    current_content = []
    
    # Check if the user uploaded an image along with text
    if message["files"]:
        for file_info in message["files"]:
            # Gradio 4+ passes files as dicts or named objects
            file_path = file_info if isinstance(file_info, str) else file_info.get("path")
            current_content.append({"type": "image", "url": file_path})
            
    if message["text"]:
        current_content.append({"type": "text", "text": message["text"]})
        
    # Guard clause if user sent nothing
    if not current_content:
        yield ""
        return

    formatted_messages.append({"role": "user", "content": current_content})

    # Initialize a non-blocking compilation streamer 
    streamer = TextIteratorStreamer(pipe.tokenizer, skip_prompt=True, skip_special_tokens=True)

    # Inject VRAM patch configurations directly into generator parameters
    generate_kwargs = {
        "text": formatted_messages,
        "max_new_tokens": 1024,
        "min_pixels": 256 * 28 * 28,
        "max_pixels": 512 * 28 * 28,
        "generate_kwargs": {"streamer": streamer}
    }

    # Execute text generation on a separate background thread to keep UI interactive
    thread = Thread(target=pipe, kwargs=generate_kwargs)
    thread.start()

    # Yield generated tokens back to UI as they arrive
    partial_text = ""
    for new_token in streamer:
        partial_text += new_token
        yield partial_text

# 3. Build Web Layout using Custom Calm Styling Themes
custom_theme = ui.themes.Soft(
    primary_hue="orange",    # Brings in that subtle "tangy" vibe softly
    neutral_hue="slate",     # Keeps the interface looking professional and clean
    font=[ui.themes.GoogleFont("Source Sans Pro"), "Arial", "sans-serif"]
)

# FIX: Removed `theme=custom_theme` from Blocks (moved to launch).
with ui.Blocks(title="tarn Demo Space") as demo:
    ui.Markdown(
        """
        # 🌌 tarn Multimodal Reasoner
        ### Fine-tuned for deep visual context analysis and structured step-by-step logic.
        *Built and maintained by **Xerv-AI**.*
        """
    )
    
    # Core multi-turn Gradio Chatbot interface
    chatbot_interface = ui.ChatInterface(
        fn=process_chat,
        chatbot=ui.Chatbot(
            height=550, 
            placeholder="📸 Upload an image or ask a structural reasoning question...",
            # FIX: Removed `show_copy_button=True` as it is removed in newer Gradio versions.
            avatar_images=(None, "https://huggingface.co/front/assets/huggingface/logos/huggingface-logo-dark.svg")
        ),
        multimodal=True, # Explicitly turns on the combined image upload + text input array
        textbox=ui.MultimodalTextbox(
            placeholder="Type your question here... (Click the paperclip icon to pin an image)",
            file_types=["image"],
            scale=7
        )
    )

if __name__ == "__main__":
    # FIX: Passed the theme to the launch method to satisfy Gradio 6.0 requirements.
    demo.queue().launch(theme=custom_theme)