app.py

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()