import streamlit as st import torch from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig model_id = "Narrativaai/BioGPT-Large-finetuned-chatdoctor" tokenizer = AutoTokenizer.from_pretrained("microsoft/BioGPT-Large") model = AutoModelForCausalLM.from_pretrained(model_id) def answer_question(prompt, temperature=0.1, top_p=0.75, top_k=40, num_beams=2, **kwargs): inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs["input_ids"].to("cpu") attention_mask = inputs["attention_mask"].to("cpu") generation_config = GenerationConfig( temperature=temperature, top_p=top_p, top_k=top_k, num_beams=num_beams, **kwargs ) with torch.no_grad(): generation_output = model.generate( input_ids=input_ids, attention_mask=attention_mask, generation_config=generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=512, eos_token_id=tokenizer.eos_token_id, ) s = generation_output.sequences[0] output = tokenizer.decode(s, skip_special_tokens=True) return output.split(" Response:")[1] st.set_page_config(page_title="Medical Chat Bot", page_icon=":ambulance:", layout="wide") st.title("Medical Chat Bot") st.caption("Talk your way to better health") # with open("./sidebar.md", "r") as sidebar_file: # sidebar_content = sidebar_file.read() # with open("./styles.md", "r") as styles_file: # styles_content = styles_file.read() # # Display the DDL for the selected table # st.sidebar.markdown(sidebar_content) # st.write(styles_content, unsafe_allow_html=True) st.write("Please enter your question below:") # get user input user_input = st.text_input("You: ") if user_input: # generate response bot_response = answer_question(f"Input: {user_input}\nResponse:") st.write("") st.write("Bot:", bot_response)