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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
tokenizer = AutoTokenizer.from_pretrained("FinGPT/fingpt-mt_llama2-7b_lora", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("FinGPT/fingpt-mt_llama2-7b_lora", trust_remote_code=True, device_map="auto")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
chat_history = []
def chat(user_input, history):
prompt = (history + "\nUser: " + user_input) if history else ("User: " + user_input)
output = pipe(prompt, max_new_tokens=300, do_sample=True)[0]["generated_text"]
history = prompt + "\nAssistant: " + output
return output, history
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
txt = gr.Textbox(placeholder="Ask a finance question...", show_label=False)
state = gr.State("")
txt.submit(lambda msg, hist: (chatbot + [(msg, chat(msg, hist)[0])], chat(msg, hist)[1]), [txt, state], [chatbot, state])
demo.launch()
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