Update app.py

app.py

@@ -24,8 +24,8 @@ data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
 with open("labels.txt", "r") as file:
     labels = file.read().splitlines()
 
-def classify(image_path):
-    try:
+def classify(image_path, text_input):
+    if text_input == code:
         output = []  # Create an empty list for output
         image_data = np.array(image_path)
         image_data = cv.resize(image_data, (224, 224))
@@ -46,7 +46,7 @@ def classify(image_path):
     
         for i, label in enumerate(labels):
             prediction_value = float(prediction[0][i])
-            rounded_value = round(prediction_value, 2)
+            rounded_value = round prediction_value, 2)
             print(f'{label}: {rounded_value}')
     
             if prediction_value > max_prediction_value:
@@ -78,13 +78,12 @@ def classify(image_path):
             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}"
+    else:
+        return "Unauthorized"
 
 iface = gr.Interface(
     fn=classify,
-    inputs=gr.inputs.Image(),
+    inputs=[gr.inputs.Image(), "text"],
     outputs=gr.outputs.JSON(),  # Output as JSON
     title="Waste Classifier",
     description="Upload an image to classify and get disposal instructions."