Update app.py

app.py

@@ -26,59 +26,58 @@ with open("labels.txt", "r") as file:
 
 def classify(image_path):
     try:
-        output = []
-        for i in output:
-            image_data = np.array(image_path)
-            image_data = cv.resize(image_data, (224, 224))
-            image_array = np.asarray(image_data)
-            normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
-            data[0] = normalized_image_array
+        output = []  # Create an empty list for output
+        image_data = np.array(image_path)
+        image_data = cv.resize(image_data, (224, 224))
+        image_array = np.asarray(image_data)
+        normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+        data[0] = normalized_image_array
     
-            # Load the model within the classify function
-            import tensorflow as tf
-            model = tf.keras.models.load_model('keras_model.h5')
+        # Load the model within the classify function
+        import tensorflow as tf
+        model = tf.keras.models.load_model('keras_model.h5')
     
-            prediction = model.predict(data)
-            
-            max_label_index = None
-            max_prediction_value = -1
-    
-            print('Prediction')
+        prediction = model.predict(data)
+        
+        max_label_index = None
+        max_prediction_value = -1
+
+        print('Prediction')
     
-            for i, label in enumerate(labels):
-                prediction_value = float(prediction[0][i])
-                rounded_value = round(prediction_value, 2)
-                print(f'{label}: {rounded_value}')
+        for i, label in enumerate(labels):
+            prediction_value = float(prediction[0][i])
+            rounded_value = round(prediction_value, 2)
+            print(f'{label}: {rounded_value}')
     
-                if prediction_value > max_prediction_value:
-                    max_label_index = i
-                    max_prediction_value = prediction_value  # Update max_prediction_value
+            if prediction_value > max_prediction_value:
+                max_label_index = i
+                max_prediction_value = prediction_value  # Update max_prediction_value
     
-            if max_label_index is not None:
-                max_label = labels[max_label_index].split(' ', 1)[1]
-                print(f'Maximum Prediction: {max_label} with a value of {round(max_prediction_value, 2)}')
+        if max_label_index is not None:
+            max_label = labels[max_label_index].split(' ', 1)[1]
+            print(f'Maximum Prediction: {max_label} with a value of {round(max_prediction_value, 2)}')
     
-                time.sleep(1)
-                print("\nWays to dispose of this waste: " + max_label)
-                payload = [
-                    {"role": "system", "content": "You are a helpful assistant."},
-                    {"role": "user", "content": "Give me the steps to dispose of this waste in bullet points (5 max): " + max_label}
-                ]
+            time.sleep(1)
+            print("\nWays to dispose of this waste: " + max_label)
+            payload = [
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": "Give me the steps to dispose of this waste in bullet points (5 max): " + max_label}
+            ]
     
-                response = requests.post(host, json={
-                    "messages": payload,
-                    "model": model_llm,
-                    "temperature": 0.5,
-                    "presence_penalty": 0,
-                    "frequency_penalty": 0,
-                    "top_p": 1
-                }).json()
+            response = requests.post(host, json={
+                "messages": payload,
+                "model": model_llm,
+                "temperature": 0.5,
+                "presence_penalty": 0,
+                "frequency_penalty": 0,
+                "top_p": 1
+            }).json()
 
-                reply = response["choices"][0]["message"]["content"]
+            reply = response["choices"][0]["message"]["content"]
 
-                output.append({"type": max_label, "prediction_value": round(max_prediction_value, 2), "content": reply})
-    
-            return output
+            output.append({"type": max_label, "prediction_value": round(max_prediction_value, 2), "content": reply})
+        
+        return output  # Return the populated output list
 
     except Exception as e:
         return f"An error occurred: {e}"
@@ -86,7 +85,7 @@ def classify(image_path):
 iface = gr.Interface(
     fn=classify,
     inputs=gr.inputs.Image(),
-    outputs="text",
+    outputs=gr.outputs.JSON(),  # Output as JSON
     title="Waste Classifier",
     description="Upload an image to classify and get disposal instructions."
 )