Create app.py

app.py

@@ -0,0 +1,65 @@
+import gradio as gr
+from main import final_pipeline  # Changed from utils to main
+from PIL import Image
+import torch
+
+CSS = """
+.gradio-container {
+    max-width: 1200px !important;
+}
+"""
+
+def predict(image, question):
+    if image is None:
+        return "Please upload an image! 📸"
+    if not question.strip():
+        return "Please enter a question! ❓"
+    
+    try:
+        result = final_pipeline(image, question)
+        return result
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+with gr.Blocks(css=CSS, title="🩻 Medical VQA - MultiLanguage") as demo:
+    gr.Markdown("""
+    # 🩻 **Medical VQA - MultiLanguage**
+    Upload X-ray, MRI, CT scans and ask questions in **ANY language**!
+    
+    **Supports:** English, Hindi, Tamil, Telugu, Kannada, Malayalam
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(type="pil", label="Upload Medical Image")
+            question_input = gr.Textbox(
+                label="Question (Any Language)", 
+                placeholder="What is shown in this X-ray? / இதில் என்ன உள்ளது?",
+                lines=2
+            )
+            predict_btn = gr.Button("🔍 Analyze Image", variant="primary", size="lg")
+        
+        with gr.Column(scale=1):
+            answer_output = gr.Markdown(label="🤖 AI Answer")
+    
+    predict_btn.click(predict, [image_input, question_input], answer_output)
+    
+    gr.Examples(
+        examples=[
+            ["What is this fracture?", None],
+            ["இதில் எந்த உடல்நலக் குறைபாடு உள்ளது?", None],
+            ["ఈ చిత్రంలో ఏముంది?", None],
+            ["Describe this X-ray", None]
+        ],
+        inputs=[question_input, image_input],
+        outputs=answer_output
+    )
+    
+    gr.Markdown("---")
+    gr.Markdown("""
+    **Powered by:** Custom VQA (ResNet18+LSTM) + BLIP2 + NLLB-200  
+    **Dataset:** VQA-RAD (Medical Images)
+    """)
+
+if __name__ == "__main__":
+    demo.launch()