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