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| import torch | |
| import os | |
| from transformers import GPT2LMHeadModel, GPT2Tokenizer | |
| import streamlit as st | |
| # Update with your local file paths | |
| model_directory = 'https://huggingface.co/spaces/MBinAsif/Banking-Chatbot/blob/main/model.safetensors' | |
| # Load the fine-tuned GPT-2 model and tokenizer | |
| model = GPT2LMHeadModel.from_pretrained(model_directory) | |
| tokenizer = GPT2Tokenizer.from_pretrained(model_directory) | |
| st.title('Banking Chatbot with GPT-2') | |
| st.sidebar.title('Options') | |
| option = st.sidebar.selectbox('Choose an action', ('Chat', 'Settings')) | |
| if option == 'Chat': | |
| st.markdown('### Chat with the Bot') | |
| user_input = st.text_input('You:', '') | |
| if st.button('Send'): | |
| input_text = user_input.strip() | |
| # Tokenize user input | |
| input_ids = tokenizer.encode(input_text, return_tensors='pt') | |
| # Generate bot response | |
| bot_response_ids = model.generate( | |
| input_ids.to(torch.device("cpu")), # Set device to CPU or GPU as appropriate | |
| max_length=100, | |
| num_return_sequences=1, | |
| temperature=0.9, | |
| no_repeat_ngram_size=2, | |
| pad_token_id=tokenizer.eos_token_id | |
| ) | |
| # Decode and display bot response | |
| bot_response = tokenizer.decode(bot_response_ids[0], skip_special_tokens=True) | |
| st.text_area('Bot:', value=bot_response, height=100) | |
| elif option == 'Settings': | |
| st.markdown('### Settings') | |
| st.write("You can configure settings here.") | |
| # Run the Streamlit app in your local IDE | |
| if __name__ == "__main__": | |
| st.write("Make with ❤️ by [Muhammad & Danyaal](https://github.com/MBinAsif/GPT-FineTunned/blob/main/fine_tunning_gpt2.py)") | |