Update app.py

app.py

@@ -26,53 +26,59 @@ with open("labels.txt", "r") as file:
 
 def classify(image_path):
     try:
-        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')
-
-        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}')
-
-            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)}')
-
-            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()
-
-            return response["choices"][0]["message"]["content"]
+        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
+    
+            # 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')
+    
+            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 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}
+                ]
+    
+                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"]
+
+                output.append("type": max_label, "prediction_value": {round(max_prediction_value, 2)}, "content": reply)
+    
+            return output
 
     except Exception as e:
         return f"An error occurred: {e}"