Update app.py

app.py

@@ -12,8 +12,6 @@ import json
 try:
     model = load_model('wound_classifier_model_googlenet.h5')
     print("✅ Model loaded successfully")
-    print(f"ℹ️ Input shape: {model.input_shape}")
-    print(f"ℹ️ Output shape: {model.output_shape}")
 except Exception as e:
     raise RuntimeError(f"❌ Model loading failed: {str(e)}")
 
@@ -116,21 +114,51 @@ def predict(image):
         return {f"🚨 Error": str(e)}, ""
 
 # ================== GRADIO INTERFACE ==================
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil", label="Upload Wound Image"),
-    outputs=[
-        gr.Label(label="Top Predictions", num_top_classes=3),
-        gr.Textbox(label="Treatment Guidelines", lines=8)
-    ],
-    title="AI Wound Classification System",
-    description="Identifies 18 wound types and provides treatment recommendations",
-    allow_flagging="never",
-    examples=[
-        f for f in ["abrasion.jpg", "burn.png", "bruise.png", "chicken-pox.png", "cut.png"] 
-        if os.path.exists(f)
-    ]
-)
+def create_interface():
+    with gr.Blocks(title="AI Wound Classifier") as demo:
+        gr.Markdown("# 🩹 AI-Powered Wound Classification System")
+        gr.Markdown("Upload a wound image or take a photo using your camera")
+
+        with gr.Tabs():
+            with gr.TabItem("📤 Upload Image"):
+                file_input = gr.Image(type="pil", label="Upload Wound Image")
+            
+            with gr.TabItem("📷 Use Camera"):
+                webcam_input = gr.Image(
+                    source="webcam", 
+                    streaming=False,
+                    mirror_webcam=False,
+                    label="Take Wound Photo",
+                    type="pil"
+                )
+        
+        with gr.Row():
+            submit_btn = gr.Button("Analyze Now", variant="primary")
+        
+        with gr.Row():
+            output_label = gr.Label(label="Top Predictions", num_top_classes=3)
+            output_guidelines = gr.Textbox(label="Treatment Guidelines", lines=8)
+
+        # Connect both input methods to same processing
+        submit_btn.click(
+            fn=predict,
+            inputs=[file_input],
+            outputs=[output_label, output_guidelines]
+        )
+        
+        webcam_input.change(
+            fn=predict,
+            inputs=[webcam_input],
+            outputs=[output_label, output_guidelines]
+        )
+
+    return demo
 
 if __name__ == "__main__":
-    iface.launch(server_name="0.0.0.0", server_port=7860)
+    iface = create_interface()
+    iface.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        enable_queue=True,
+        share=False  # Set to True if you want a public link
+    )