Update app.py

app.py

@@ -11,9 +11,9 @@ import json
 # ================== MODEL LOADING ==================
 try:
     model = load_model('wound_classifier_model_googlenet.h5')
-    print("\u2705 Model loaded successfully")
+    print("✅ Model loaded successfully")
 except Exception as e:
-    raise RuntimeError(f"\u274C Model loading failed: {str(e)}")
+    raise RuntimeError(f"❌ Model loading failed: {str(e)}")
 
 # ================== CLASS LABELS ==================
 CLASS_LABELS = [
@@ -37,14 +37,14 @@ def preprocess_image(image, target_size=(224, 224)):
     """Process and validate input images"""
     try:
         if not image:
-            raise ValueError("\ud83d\udea8 No image provided")
+            raise ValueError("🚨 No image provided")
         
         image = image.convert("RGB").resize(target_size)
         array = np.array(image) / 255.0
-        print(f"\ud83c\udfa8 Image processed: Shape {array.shape}")
+        print(f"🖼️ Image processed: Shape {array.shape}")
         return array
     except Exception as e:
-        raise RuntimeError(f"\ud83c\udfa8 Image processing failed: {str(e)}")
+        raise RuntimeError(f"🖼️ Image processing failed: {str(e)}")
 
 # ================== MEDICAL GUIDELINES ==================
 def get_medical_guidelines(wound_type):
@@ -62,7 +62,7 @@ def get_medical_guidelines(wound_type):
     Use clear, simple language without markdown."""
     
     try:
-        print(f"\ud83d\udcf1 Sending API request for {wound_type}...")
+        print(f"📡 Sending API request for {wound_type}...")
         response = requests.post(
             OPENROUTER_API_URL,
             headers=headers,
@@ -76,15 +76,15 @@ def get_medical_guidelines(wound_type):
         
         response.raise_for_status()
         result = response.json()
-        print("\ud83d\udee0\ufe0f Raw API response:", json.dumps(result, indent=2))
+        print("🔧 Raw API response:", json.dumps(result, indent=2))
         
         if not result.get("choices"):
-            return "\u26a0\ufe0f API response format unexpected"
+            return "⚠️ API response format unexpected"
             
         return result["choices"][0]["message"]["content"]
         
     except Exception as e:
-        return f"\u26a0\ufe0f Guidelines unavailable: {str(e)}"
+        return f"⚠️ Guidelines unavailable: {str(e)}"
 
 # ================== MAIN PREDICTION ==================
 def predict(image):
@@ -111,44 +111,26 @@ def predict(image):
         return results, guidelines
         
     except Exception as e:
-        return {f"\ud83d\udea8 Error": str(e)}, ""
+        return {f"🚨 Error": str(e)}, ""
 
 # ================== GRADIO INTERFACE ==================
 def create_interface():
     with gr.Blocks(title="AI Wound Classifier") as demo:
-        gr.Markdown("# \ud83e\ude79 AI-Powered Wound Classification System")
+        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("\ud83d\udcc4 Upload Image"):
-                file_input = gr.Image(type="pil", label="Upload Wound Image")
-            
-            with gr.TabItem("\ud83d\udcf7 Use Camera"):
-                webcam_input = gr.Video(
-                    mirror_webcam=False,
-                    label="Take Wound Photo"
-                )
-        
-        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)
+        file_input = gr.Image(type="pil", label="Upload Wound Image")
+        submit_btn = gr.Button("Analyze Now", variant="primary")
+        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
+        # Connect input to 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__":
@@ -156,6 +138,5 @@ if __name__ == "__main__":
     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
+        share=True  # Set to False if you do not want a public link
     )