Update app.py

app.py

@@ -23,18 +23,16 @@
 # iface.launch(share=True)
 
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "gpt2"
-model = AutoModelForCausalLM.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+from transformers import pipeline
 
+# Load the question answering pipeline
+qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad", tokenizer="distilbert-base-cased")
 
+# Define a function to generate answer for the given question
 def generate_answer(question):
-    inputs = tokenizer.encode("Question: " + question, return_tensors="pt")
-    outputs = model.generate(inputs, max_length=100, num_return_sequences=1, early_stopping=True, do_sample=True)
-    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return answer
+    # Call the question answering pipeline
+    result = qa_pipeline(question=question, context=None)
+    return result["answer"]
 
 iface = gr.Interface(
     fn=generate_answer,