Update app.py

app.py

@@ -1,33 +1,15 @@
 import gradio as gr
 from transformers import pipeline
 
-# Specify the model explicitly
-model_name = "rahul7star/Rahul-FineTunedLLM-v03" 
-qa_pipeline = pipeline("question-answering", model=model_name)
+# Initialize the custom pipeline
+qa_pipeline = pipeline("question-answering")
 
 def answer_question(context, question):
-    """
-    Function to run the QA pipeline and get the answer.
-    
-    :param context: The context in which the question is asked.
-    :param question: The question that needs to be answered.
-    :return: Answer from the QA model.
-    """
     result = qa_pipeline({'context': context, 'question': question})
     return result['answer']
 
-# Create the Gradio interface
-interface = gr.Interface(
-    fn=answer_question,
-    inputs=[
-        gr.Textbox(label="Context", placeholder="Enter context here...", lines=4),  # Left-side input
-        gr.Textbox(label="Question", placeholder="Ask a question here...", lines=1)  # Right-side input
-    ],
-    outputs="text",  # Output as text
-    live=True,  # Optional: Display results as the user types
-    layout="horizontal"  # Align inputs side by side
-)
+# Define the Gradio interface
+interface = gr.Interface(fn=answer_question, inputs=["text", "text"], outputs="text")
 
 # Launch the interface
-if __name__ == "__main__":
-    interface.launch()
+interface.launch()