Update app.py

app.py

@@ -1,18 +1,26 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load the pre-trained question-answering model from Hugging Face
-qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")
+# Try loading the model with a fallback for any loading errors
+try:
+    # Load the pre-trained question-answering model from Hugging Face
+    qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
+except Exception as e:
+    # Print error message for debugging purposes
+    print(f"Error loading model: {e}")
+    qa_pipeline = None
 
 # Define the function that takes inputs and returns the answer
 def answer_question(context, question):
+    if qa_pipeline is None:
+        return "Error: Model not loaded."
     result = qa_pipeline(question=question, context=context)
     return result['answer']
 
 # Create the Gradio interface
 interface = gr.Interface(
     fn=answer_question, 
-    inputs=[gr.inputs.Textbox(lines=7, label="Context (Enter the passage)"), gr.inputs.Textbox(lines=2, label="Question")],
+    inputs=[gr.Textbox(lines=7, label="Context (Enter the passage)"), gr.Textbox(lines=2, label="Question")],
     outputs="text",
     title="Question Answering Model",
     description="Ask a question based on the given context.",