app.py

import gradio as gr
from transformers import pipeline

# Initialize the model at startup
analyzer = pipeline(
    "image-to-text",
    model="Salesforce/blip-image-captioning-base"
)

def analyze_medical_image(image, question=""):
    """Analyze medical images with optional question"""
    try:
        if image is None:
            return "⚠️ Please upload a medical image"
        
        prompt = (
            f"Question: As a radiologist, {question if question else 'describe any abnormalities in this medical scan'}. "
            "Answer professionally:"
        )
        
        results = analyzer(image, prompt=prompt)
        return results[0]["generated_text"].replace(prompt, "").strip()
    
    except Exception as e:
        return f"❌ Error: {str(e)}"

# Simple Gradio interface
demo = gr.Interface(
    fn=analyze_medical_image,
    inputs=[
        gr.Image(type="pil", label="Upload Medical Scan"),
        gr.Textbox(label="Clinical Question (optional)", placeholder="Describe symptoms...")
    ],
    outputs=gr.Textbox(label="Analysis Report"),
    title="🩺 Medical Image Analyzer",
    description="Upload medical scans (X-rays, CT, MRI) for AI analysis",
    allow_flagging="never"
)

demo.launch(show_error=True)