updated code ✅✅

app.py

@@ -1,27 +1,40 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load a pre-configured medical analysis pipeline
+# Initialize the model at startup
 analyzer = pipeline(
     "image-to-text",
-    model="Salesforce/blip-image-captioning-base",
-    device="cpu"
+    model="Salesforce/blip-image-captioning-base"
 )
 
-def analyze(image, question):
-    prompt = f"As a radiologist, {question}" if question else "Describe this medical scan professionally"
-    result = analyzer(image, prompt=prompt)
-    return result[0]["generated_text"]
+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)}"
 
-with gr.Blocks() as app:
-    gr.Markdown("# 🩺 Medical Image Analyzer")
-    with gr.Row():
-        with gr.Column():
-            img_input = gr.Image(type="pil", label="Upload Scan")
-            question = gr.Textbox(label="Question (optional)")
-            btn = gr.Button("Analyze")
-        with gr.Column():
-            output = gr.Textbox(label="Diagnosis")
-    btn.click(analyze, [img_input, question], output)
+# 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"
+)
 
-app.launch()
+demo.launch(show_error=True)