Update app.py

app.py

@@ -103,7 +103,7 @@ def process_video(video_path, true_label=None):
             cnn_frame = cnn_frame.reshape(1, 28, 28, 1)
             # Predict with cnn_model
             cnn_pred = cnn_model.predict(cnn_frame)
-            cnn_label = np.argmax(cnn_pred)
+            cnn_label = (cnn_pred > 0.5).astype(int)
             if cnn_label == 0:
                 cnn_class0 += 1
             else:
@@ -115,7 +115,7 @@ def process_video(video_path, true_label=None):
             q_frame = process_frame(frame)
             # Predict with qcnn_model
             qcnn_pred = qcnn_model.predict(q_frame)
-            qcnn_label = np.argmax(qcnn_pred)
+            qcnn_label = (qcnn_pred > 0.5).astype(int)
             if qcnn_label == 0:
                 qcnn_class0 += 1
             else:
@@ -141,8 +141,8 @@ def process_video(video_path, true_label=None):
         result = f"CNN Model Accuracy: {cnn_accuracy:.2f}%\n"
         result += f"QCNN Model Accuracy: {qcnn_accuracy:.2f}%"
     else:
-        result = f"CNN Model Predictions:\nOriginal: {cnn_class0_percent:.2f}%\nFake: {cnn_class1_percent:.2f}%\n"
-        result += f"QCNN Model Predictions:\nOriginal: {qcnn_class0_percent:.2f}%\nFake: {qcnn_class1_percent:.2f}%"
+        result = f"CNN Model Predictions:\nClass 0: {cnn_class0_percent:.2f}%\nClass 1: {cnn_class1_percent:.2f}%\n"
+        result += f"QCNN Model Predictions:\nClass 0: {qcnn_class0_percent:.2f}%\nClass 1: {qcnn_class1_percent:.2f}%"
     return result
 
 def predict(video_input):
@@ -192,7 +192,7 @@ with gr.Blocks() as demo:
             )
         with gr.Column():
             output = gr.Textbox(label="Result")
-            predict_button = gr.Button("Predict")
+            predict_button = gr.Button("Predict", elem_classes="gr-button")
 
     predict_button.click(fn=predict, inputs=video_input, outputs=output)
     demo.launch()