Update app.py

app.py

@@ -16,14 +16,13 @@ from collections import Counter
 device = torch.device("cpu")
 processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
 model = Wav2Vec2ForSequenceClassification.from_pretrained("facebook/wav2vec2-base-960h", num_labels=2).to(device)
-# model_path = "dysarthria_classifier12.pth"
+model_path = "dysarthria_classifier12.pth"
 # model_path = 'model_weights2.pth'
-model_path = '/home/user/app/dysarthria_classifier10.pth'
-model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+# model_path = '/home/user/app/dysarthria_classifier10.pth'
 
-# if os.path.exists(model_path):
-#     print(f"Loading saved model {model_path}")
-#     model.load_state_dict(torch.load(model_path))
+if os.path.exists(model_path):
+    print(f"Loading saved model {model_path}")
+    model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
 
 
 title = "Upload an mp3 file for parkinsons detection! (Thai Language)"
@@ -65,13 +64,13 @@ def predict(file_path):
         predicted_class_id = torch.argmax(logits, dim=-1).item()
 
     return predicted_class_id
-# gr.Interface(
-#     fn=predict,
-#     inputs="file",
-#     outputs="text",
-#     title=title,
-#     description=description,
-# ).launch()
+gr.Interface(
+    fn=predict,
+    inputs="file",
+    outputs="text",
+    title=title,
+    description=description,
+).launch()
 
-iface = gr.Interface(fn=predict, inputs="file", outputs="text")
-iface.launch()
+# iface = gr.Interface(fn=predict, inputs="file", outputs="text")
+# iface.launch()