app.py

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

# --- Initialize the Model Pipeline ---
print("Loading MedGemma model...")
try:
    pipe = pipeline(
        "image-text-to-text",
        model="google/medgemma-4b-it",
        torch_dtype=torch.bfloat16,
        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 Analysis Function ---
@spaces.GPU()
def analyze_symptoms(symptom_image, symptoms_text):
    """
    Analyzes user's symptoms using a corrected prompt-building logic.
    """
    if not model_loaded:
        return "Error: The AI model could not be loaded. Please check the Space logs."

    # Standardize input to avoid issues with None or whitespace
    symptoms_text = symptoms_text.strip() if symptoms_text else ""

    if symptom_image is None and not symptoms_text:
        return "Please describe your symptoms or upload an image for analysis."

    try:
        # --- REVISED PROMPT LOGIC ---
        # Build the prompt dynamically based on provided inputs.
        # This is much clearer and less error-prone.
        prompt_parts = [
            "You are an expert, empathetic AI medical assistant. Analyze the potential medical condition based on the following information.",
            "Provide a list of possible conditions, your reasoning, and a clear, actionable next-steps plan.",
            "Start your analysis by describing the user-provided information (text and/or image)."
        ]
        
        # This is the actual user input that the model will process.
        # It's better to pass it directly instead of wrapping it in another instruction.
        user_input_for_model = []

        if symptoms_text:
            user_input_for_model.append({"type": "text", "text": symptoms_text})
        
        if symptom_image:
            # The pipeline expects an image object. PIL Image is correct.
            user_input_for_model.append({"type": "image", "image": symptom_image})
            
        # The system prompt sets the context and instructions for the AI.
        system_prompt = " ".join(prompt_parts)

        messages = [
            {
                "role": "system",
                "content": [{"type": "text", "text": system_prompt}]
            },
            {
                "role": "user",
                "content": user_input_for_model
            }
        ]
        
        print("Generating pipeline output...")
        output = pipe(messages, max_new_tokens=512, do_sample=True, temperature=0.7)
        # The output format is a list containing the full conversation history.
        # The last message in the list is the AI's response.
        print("Pipeline Output:", output)

        # Make the output processing more robust
        generated = output[0]["generated_text"]
        if isinstance(generated, list) and generated:
            # If output is a list of dicts, take the content from the last one
            result = generated[-1].get('content', str(generated))
        elif isinstance(generated, str):
            # If output is just a string
            result = generated
        else:
            # Failsafe for any other unexpected format
            result = str(generated)

        disclaimer = "\n\n***Disclaimer: I am an AI assistant and not a medical professional. This is not a diagnosis. Please consult a doctor for any health concerns.***"
        
        return result + disclaimer

    except Exception as e:
        print(f"An exception occurred during analysis: {type(e).__name__}: {e}")
        return f"Error during analysis: {str(e)}"

# --- Create the Gradio Interface (No changes needed here) ---
with gr.Blocks(theme=gr.themes.Soft(), title="AI Symptom Analyzer") as demo:
    gr.HTML("""
        <div style="text-align: center; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 2rem; border-radius: 10px; margin-bottom: 2rem;">
            <h1>🩺 AI Symptom Analyzer</h1>
            <p>Advanced symptom analysis powered by Google's MedGemma AI</p>
        </div>
    """)
    gr.HTML("""
        <div style="background-color: #fff3cd; border: 1px solid #ffeaa7; border-radius: 8px; padding: 1rem; margin: 1rem 0; color: #856404;">
            <strong>⚠️ Medical Disclaimer:</strong> This AI tool is for informational purposes only and is not a substitute for professional medical diagnosis or treatment.
        </div>
    """)
    
    with gr.Row(equal_height=True):
        with gr.Column(scale=1):
            gr.Markdown("### 1. Describe Your Symptoms")
            symptoms_input = gr.Textbox(
                label="Symptoms",
                placeholder="e.g., 'I have a rash on my arm that is red and itchy...'", lines=5)
            gr.Markdown("### 2. Upload an Image (Optional)")
            image_input = gr.Image(label="Symptom Image", type="pil", height=300)
            with gr.Row():
                clear_btn = gr.Button("🗑️ Clear All", variant="secondary")
                analyze_btn = gr.Button("🔍 Analyze Symptoms", variant="primary", size="lg")
                
        with gr.Column(scale=1):
            gr.Markdown("### 📊 Analysis Report")
            output_text = gr.Textbox(
                label="AI Analysis", lines=25, show_copy_button=True, placeholder="Analysis results will appear here...")

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

    analyze_btn.click(fn=analyze_symptoms, inputs=[image_input, symptoms_input], outputs=output_text)
    clear_btn.click(fn=clear_all, outputs=[image_input, symptoms_input, output_text])

if __name__ == "__main__":
    print("Starting Gradio interface...")
    demo.launch(debug=True)