app.py

import streamlit as st
from transformers import GPT2Tokenizer, TFGPT2LMHeadModel

# Load the fine-tuned model
model = TFGPT2LMHeadModel.from_pretrained("fine-tuned-gpt2")

# Initialize the tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Set the maximum length for the generated text
max_length = 100

def generate_answer(prompt):
    # Encode the prompt
    input_ids = tokenizer.encode(prompt, return_tensors="tf")

    # Generate text using the model
    output = model.generate(
        input_ids=input_ids,
        max_length=max_length,
        num_return_sequences=1,
        do_sample=True,
        temperature=0.5,
    )

    # Decode the generated text
    generated_text = tokenizer.decode(output[0])

    return generated_text

def main():
    st.title("Chatbot")

    # Get user input
    prompt = st.text_input("Enter your question")

    # Generate answer on button click
    if st.button("Generate Answer"):
        answer = generate_answer(prompt)
        st.text("Generated Answer:")
        st.text(answer)

if __name__ == '__main__':
    main()