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| 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;'>Agrinuture 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() | |