Removed the sharing

app.py

@@ -11,12 +11,14 @@ model = AutoModelForSequenceClassification.from_pretrained(model_name)
 premise = ""
 hypothesis = ""
 
-def zeroShotClassification(text_input, hypothesis, candidate_labels):
+def zeroShotClassification(text_input, candidate_labels, hypothesis):
+  print(text_input)
+  print(candidate_labels)
   input = tokenizer(text_input, hypothesis, truncation=True, return_tensors="pt")
   output = model(input["input_ids"].to(device))  # device = "cuda:0" or "cpu"
   prediction = torch.softmax(output["logits"][0], -1).tolist()
-  label_names = candidate_labels #["entailment", "neutral", "contradiction"]
-  prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
+  labels = [label.strip(' ') for label in candidate_labels.split(',')]
+  prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, labels)}
   return prediction
 
 examples = [
@@ -28,5 +30,5 @@ examples = [
   ["Submission Receipt", "Submission Receipt"]
 ]
 
-demo = gr.Interface(fn=zeroShotClassification, inputs=[gr.Textbox(label="Input"), gr.Textbox(label="Candidate Labels", value="Meeting Minutes / Outcomes,Submission Receipt")], outputs=gr.Label(label="Classification"), examples=examples)
+demo = gr.Interface(fn=zeroShotClassification, inputs=[gr.Textbox(label="Input"), gr.Textbox(label="Candidate Labels", value="Meeting Minutes / Outcomes,Submission Receipt"), gr.Textbox(label="Hypothesys", value="Meeting Minutes / Outcomes,Submission Receipt")], outputs=gr.Label(label="Classification"), examples=examples)
 demo.launch();