Update app.py

app.py

@@ -1,24 +1,22 @@
 import gradio as gr
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
 
 # Load model
-model = AutoModelForSequenceClassification.from_pretrained("alperugurcan/Contradictory")
-tokenizer = AutoTokenizer.from_pretrained("alperugurcan/Contradictory")
+model = AutoModelForSequenceClassification.from_pretrained("nli_model")
+tokenizer = AutoTokenizer.from_pretrained("nli_model")
 
 def predict(premise, hypothesis):
-    # Tokenize and predict
     inputs = tokenizer(premise, hypothesis, return_tensors="pt", truncation=True)
     outputs = model(**inputs)
     prediction = outputs.logits.softmax(-1)[0]
     
-    # Return results
     return {
         "Entailment": float(prediction[0]),
         "Neutral": float(prediction[1]),
         "Contradiction": float(prediction[2])
     }
 
-# Create interface
 demo = gr.Interface(
     fn=predict,
     inputs=[
@@ -26,7 +24,7 @@ demo = gr.Interface(
         gr.Textbox(label="Hypothesis")
     ],
     outputs=gr.Label(),
-    title="NLI Classifier",
+    title="Natural Language Inference",
     examples=[
         ["The cat is sleeping.", "The cat is awake."],
         ["It's raining.", "The ground is wet."]