| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
| base = "meta-llama/Llama-2-7b-chat-hf" | |
| adapter = "FinGPT/fingpt-mt_llama2-7b_lora" | |
| tokenizer = AutoTokenizer.from_pretrained(base) | |
| model = AutoModelForCausalLM.from_pretrained(adapter, device_map="auto") | |
| pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) | |
| def chat(user_input, history): | |
| prompt = (history + "\nUser: " + user_input) if history else ("User: " + user_input) | |
| output = pipe(prompt, max_new_tokens=256, do_sample=True)[0]["generated_text"] | |
| return output, prompt + "\nAssistant: " + output | |
| with gr.Blocks() as demo: | |
| chatbot = gr.Chatbot() | |
| txt = gr.Textbox(placeholder="Ask a finance question...") | |
| state = gr.State("") | |
| txt.submit(lambda m, h: (chatbot + [(m, chat(m, h)[0])], chat(m, h)[1]), [txt, state], [chatbot, state]) | |
| demo.launch() | |