Update app.py

app.py

@@ -7,7 +7,7 @@ CLASSES = {
     'irrelevant': 1,
     'no': 2,
 }
-tokenizer = DebertaV2Tokenizer.from_pretrained('cross-encoder/nli-deberta-v3-base',do_lower_case=True)
+tokenizer = DebertaV2Tokenizer.from_pretrained('cross-encoder/nli-deberta-v3-base', do_lower_case=True)
 model = AutoModelForSequenceClassification.from_pretrained('MrPio/TheSeagullStory-nli-deberta-v3-base')
 model.eval()
 story = open('story.txt').read().replace("\n\n", "\n").replace("\n", " ").strip()
@@ -15,11 +15,11 @@ story = open('story.txt').read().replace("\n\n", "\n").replace("\n", " ").strip(
 
 def ask(question):
     inputs = tokenizer(story, question, truncation=True, padding=True)
-    prediction = torch.round(torch.softmax(model(**inputs).logits, 1).squeeze().numpy(), 3)
-    return {c: prediction[i].item() for c, i in CLASSES}
+    prediction = torch.softmax(model(**inputs).logits, 1).squeeze()
+    return {c: round(prediction[i].item(), 3) for c, i in CLASSES}
 
 
-demo = gr.Interface(
+gradio = gr.Interface(
     ask,
     inputs=[gr.Textbox(value="", label="Your question, as an affirmative sentence:")],
     outputs=[gr.Label(label="Answer", num_top_classes=3)],
@@ -35,4 +35,4 @@ demo = gr.Interface(
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    gradio.launch(share=True)