import streamlit as st
import tensorflow
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-medium")

# 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=0.7,
    )

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

    return generated_text

def main():
    st.markdown("<h1 style='text-align: center;'>Agriculture Bot</h1>", unsafe_allow_html=True)
    # Get user input
    st.markdown("<p style='text-align: center;'>Enter your question</p>", unsafe_allow_html=True)

    prompt = st.text_input("")
    st.markdown(
        """
        <style>
        .stButton > button {
            display: block;
            margin: 0 auto;
        }
        </style>
        """,
        unsafe_allow_html=True
    )
    # Generate answer on button click
    if st.button("Generate Answer"):
        answer = generate_answer(prompt)
        st.markdown("<h3 style='text-align: center;'>Generated Answer:</h3>", unsafe_allow_html=True)
        st.text(answer)

if __name__ == '__main__':
    main()