app.py

import gradio as gr
from transformers import pipeline
from PIL import Image
import torch
import os
import spaces

# --- Configuration & Model Loading ---
# Use the pipeline, which is more robust as seen in the working example
print("Loading MedGemma model via pipeline...")
try:
    pipe = pipeline(
        "image-to-text", # The correct task for this model
        model="google/medgemma-4b-it",
        model_kwargs={"torch_dtype": torch.bfloat16}, # Pass dtype here
        device_map="auto",
        token=os.environ.get("HF_TOKEN")
    )
    model_loaded = True
    print("Model loaded successfully!")
except Exception as e:
    model_loaded = False
    print(f"Error loading model: {e}")


# --- Core Chatbot Function ---
@spaces.GPU
def symptom_checker_chat(user_input, history_for_display, new_image_upload, image_state):
    """
    Manages the conversation using the correct message format derived from the working example.
    """
    if not model_loaded:
        if user_input:
            history_for_display.append((user_input, "Error: The model could not be loaded."))
        return history_for_display, image_state, None, ""

    current_image = new_image_upload if new_image_upload is not None else image_state

    # --- THE CORRECT IMPLEMENTATION ---
    # Build the 'messages' list using the exact format from the working X-ray app.
    messages = []
    
    # Optional: System prompt can be added here if needed, following the same format.
    
    # Process the conversation history
    for user_msg, assistant_msg in history_for_display:
        # For history turns, we assume the image was part of the first turn (handled below).
        # So, all historical messages are just text.
        messages.append({"role": "user", "content": [{"type": "text", "text": user_msg}]})
        if assistant_msg:
            messages.append({"role": "assistant", "content": [{"type": "text", "text": assistant_msg}]})

    # Add the current user turn
    current_user_content = [{"type": "text", "text": user_input}]
    # If there's an image for the conversation, add it to the first user turn's content
    if current_image is not None and not history_for_display: # Only for the very first message
         current_user_content.append({"type": "image"}) # The pipeline handles the image object separately

    messages.append({"role": "user", "content": current_user_content})

    try:
        # Generate analysis using the pipeline. It's much simpler.
        # We pass the image separately if it exists.
        if current_image:
            output = pipe(current_image, prompt=messages, generate_kwargs={"max_new_tokens": 512})
        else:
            # If no image, the pipeline can work with just the prompt
            output = pipe(prompt=messages, generate_kwargs={"max_new_tokens": 512})
        
        # The pipeline's output structure can be complex; we need to extract the final text.
        # It's usually in the last dictionary of the generated list.
        result = output[0]["generated_text"]
        if isinstance(result, list):
            # Find the last text content from the model's response
            clean_response = next((item['text'] for item in reversed(result) if item['type'] == 'text'), "Sorry, I couldn't generate a response.")
        else: # Simpler text-only output
            clean_response = result

    except Exception as e:
        print(f"Caught a critical exception during generation: {e}", flush=True)
        clean_response = (
            "An error occurred during generation. Details:\n\n"
            f"```\n{type(e).__name__}: {e}\n```"
        )

    history_for_display.append((user_input, clean_response))
    return history_for_display, current_image, None, ""

# --- Gradio Interface (Mostly unchanged) ---
with gr.Blocks(theme=gr.themes.Soft(), css="footer {display: none !important}") as demo:
    gr.Markdown(
        """
        # AI Symptom Checker powered by MedGemma
        Describe your symptoms below. For visual symptoms (e.g., a skin rash), upload an image. The AI will analyze the inputs and ask clarifying questions if needed.
        """
    )
    
    image_state = gr.State(None)
    chatbot = gr.Chatbot(label="Conversation", height=500, bubble_full_width=False)
    chat_history = gr.State([])

    with gr.Row():
        image_box = gr.Image(type="pil", label="Upload Image of Symptom (Optional)")

    with gr.Row():
        text_box = gr.Textbox(label="Describe your symptoms...", placeholder="e.g., I have a rash on my arm...", scale=4)
        submit_btn = gr.Button("Send", variant="primary", scale=1)

    def clear_all():
        return [], None, None, ""

    clear_btn = gr.Button("Start New Conversation")
    clear_btn.click(fn=clear_all, outputs=[chat_history, image_state, image_box, text_box], queue=False)
    
    def on_submit(user_input, display_history, new_image, persisted_image):
        if not user_input.strip() and not new_image:
            return display_history, persisted_image, None, ""
        # The display history IS our history state now
        return symptom_checker_chat(user_input, display_history, new_image, persisted_image)

    submit_btn.click(
        fn=on_submit,
        inputs=[text_box, chat_history, image_box, image_state],
        outputs=[chat_history, image_state, image_box, text_box]
    )
    text_box.submit(
        fn=on_submit,
        inputs=[text_box, chat_history, image_box, image_state],
        outputs=[chat_history, image_state, image_box, text_box]
    )

if __name__ == "__main__":
    demo.launch(debug=True)
        # Generate the response
        outputs = model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.7)
        
        # Decode only the newly generated part
        input_token_len = inputs["input_ids"].shape[1]
        generated_tokens = outputs[:, input_token_len:]
        clean_response = processor.decode(generated_tokens[0], skip_special_tokens=True).strip()

    except Exception as e:
        print(f"Caught a critical exception during generation: {e}", flush=True)
        # Display the real error in the UI for easier debugging
        clean_response = (
            "An error occurred during generation. This is the technical details:\n\n"
            f"```\n{type(e).__name__}: {e}\n```"
        )

    # Update the display history
    history_for_display.append((user_input, clean_response))

    # Return all updated values
    return history_for_display, current_image, None, ""

# --- Gradio Interface ---
with gr.Blocks(theme=gr.themes.Soft(), css="footer {display: none !important}") as demo:
    gr.Markdown(
        """
        # AI Symptom Checker powered by MedGemma
        Describe your symptoms below. For visual symptoms (e.g., a skin rash), upload an image. The AI will analyze the inputs and ask clarifying questions if needed.
        """
    )
    
    # State to hold the image across an entire conversation
    image_state = gr.State(None)

    chatbot = gr.Chatbot(label="Conversation", height=500, bubble_full_width=False, avatar_images=("user.png", "bot.png"))
    # The history state will now just be for display, a simple list of (text, text) tuples.
    chat_history = gr.State([])

    with gr.Row():
        image_box = gr.Image(type="pil", label="Upload Image of Symptom (Optional)")

    with gr.Row():
        text_box = gr.Textbox(label="Describe your symptoms...", placeholder="e.g., I have a rash on my arm...", scale=4)
        submit_btn = gr.Button("Send", variant="primary", scale=1)

    # The clear function now resets all three states
    def clear_all():
        return [], None, None, ""

    clear_btn = gr.Button("Start New Conversation")
    clear_btn.click(
        fn=clear_all, 
        outputs=[chat_history, image_state, image_box, text_box], 
        queue=False
    )
    
    # The submit handler function
    def on_submit(user_input, display_history, new_image, persisted_image):
        if not user_input.strip() and not new_image:
            return display_history, persisted_image, None, ""
        return symptom_checker_chat(user_input, display_history, new_image, persisted_image)

    # Wire up the events
    submit_btn.click(
        fn=on_submit,
        inputs=[text_box, chat_history, image_box, image_state],
        outputs=[chat_history, image_state, image_box, text_box]
    )
        
    text_box.submit(
        fn=on_submit,
        inputs=[text_box, chat_history, image_box, image_state],
        outputs=[chat_history, image_state, image_box, text_box]
    )

if __name__ == "__main__":
    demo.launch(debug=True)