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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)
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