Some cleanup.

app.py

@@ -35,7 +35,6 @@ tokenizer_name = "MoritzLaurer/deberta-v3-base-zeroshot-v2.0"
 
 classifier_cpu = pipeline(task="zero-shot-classification", model=model_name, tokenizer=tokenizer_name)
 classifier_gpu = pipeline(task="zero-shot-classification", model=model_name, tokenizer=tokenizer_name, device="cuda:0")
-# classifier = pipeline(task="zero-shot-classification", model=model_name, tokenizer=tokenizer_name)
 
 def classify(data_string, request: gradio.Request):
     if request:
@@ -55,12 +54,12 @@ def classify(data_string, request: gradio.Request):
     try:
         if 'cpu' not in data:
             result = zero_shot_classification_gpu(data)
-            print(f"GPU Classification @ [{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] took {time.time() - start_time}.")
+            print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} - GPU Classification took {time.time() - start_time}.")
     except Exception as e:
         print(f"GPU classification failed: {e}\nFall back to CPU.")
     if not result:
         result = zero_shot_classification_cpu(data)
-        print(f"CPU Classification @ [{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] took {time.time() - start_time}.")
+        print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} - CPU Classification took {time.time() - start_time}.")
     return json.dumps(result)
 
 def zero_shot_classification_cpu(data):
@@ -73,22 +72,6 @@ def zero_shot_classification_gpu(data):
 def create_sequences(data):
     return [data['sequence'] + '\n' + data['hypothesis_template'].format(label) for label in data['candidate_labels']]
 
-# def few_shot_classification(data):
-#     sequences = create_sequences(data)
-#     print(sequences)
-#     # results = onnx_few_shot_model(sequences)
-#     probs = onnx_few_shot_model.predict_proba(sequences)
-#     scores = [true[0] for true in probs]
-
-#     composite = list(zip(scores, data['candidate_labels']))
-#     composite = sorted(composite, key=lambda x: x[0], reverse=True)
-
-#     labels, scores = zip(*composite)
-
-#     response_dict = {'scores': scores, 'labels': labels}
-#     print(response_dict)
-#     response_string = json.dumps(response_dict)
-#     return response_strin
 gradio_interface = gradio.Interface(
     fn = classify,
     inputs = gradio.Textbox(label="JSON Input"),
@@ -99,7 +82,3 @@ app.mount("/gradio", gradio_interface)
 
 # app = gradio.mount_gradio_app(app, gradio_interface, path="/gradio")
 gradio_interface.launch()
-
-# if __name__ == "__main__":
-#     import uvicorn
-#     uvicorn.run(app, host="0.0.0.0", port=8000)