Create app.py

app.py

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+import gradio as gr
+from transformers import BertForQuestionAnswering, BertTokenizer, pipeline
+
+# Load your fine-tuned model and tokenizer
+model = BertForQuestionAnswering.from_pretrained("RahulTextLLM")  # Path to the fine-tuned model
+tokenizer = BertTokenizer.from_pretrained("RahulTextLLM")  # Path to the tokenizer
+
+# Initialize the custom pipeline with your fine-tuned model
+qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
+
+# Function to get the answer using the fine-tuned model
+def answer_question(context, question):
+    result = qa_pipeline({'context': context, 'question': question})
+    return result['answer']
+
+# Define the Gradio interface
+interface = gr.Interface(fn=answer_question, inputs=["text", "text"], outputs="text")
+
+# Launch the interface
+interface.launch()