Update app.py

app.py

@@ -16,31 +16,7 @@ tokenizer3 = PegasusTokenizer.from_pretrained(model_name)
 model3 = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
 
 
-def paraphrase(text_input):
-    question = "What is the Sri Lanka's rank on human development Index?"
-    prediction = classifier(
-        context=text_input,
-        question=question,
-        truncation=True,
-        max_length=512,
-        padding=True,
-    )
-    answer = prediction['answer']
-    answer_start = prediction['start']
-    answer_end = prediction['end']
-    context = text_input.split(".")
-    for i in range(len(context)):
-        if answer in context[i]:
-            sentence = context[i].strip() + "."
-            break
-    batch = tokenizer3([sentence],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device)
-    translated = model3.generate(**batch,max_length=60,num_beams=10, num_return_sequences=1, temperature=1.5)
-    paraphrase = tokenizer3.batch_decode(translated, skip_special_tokens=True)[0]
-    return paraphrase
-
-
-def qa_paraphrase(text_input):
-    question = "What is the Sri Lanka's rank on human development Index?"
+def qa_paraphrase(text_input, question):
     prediction = classifier(
         context=text_input,
         question=question,
@@ -64,7 +40,10 @@ def qa_paraphrase(text_input):
 
 iface = gr.Interface(
     fn=qa_paraphrase,
-    inputs=gr.inputs.Textbox(label="Text Input"),
+    inputs=[
+        gr.inputs.Textbox(label="Text Input"),
+        gr.inputs.Textbox(label="Question")
+    ],
     outputs=[
         gr.outputs.Textbox(label="Answer"),
         gr.outputs.Textbox(label="Paraphrased Sentence")