app.py

import streamlit as st
from langchain.llms import HuggingFaceHub

#Function to return the response 
def generate_answer(query):
    llm = HuggingFaceHub(
        repo_id = "huggingfaceh4/zephyr-7b-alpha", 
        model_kwargs={"temperature": 0.5, "max_length": 64,"max_new_tokens":512}
    )
    prompt = f"""
            You are a doctor assistant trained to provide medical advice and support. Please respond with empathy and consider the patient's well-being.
            </s>
             
            {query}</s>
             
        """
    result = llm.predict(prompt)
    return result
    

#App UI starts here 
st.set_page_config(page_title = "LangChain Demo", page_icon = ":robot:")
st.header("LangChain Demo")


#Gets User Input 
def get_text():
    input_text = st.text_input("You: ", key="input")
    return input_text


user_input = get_text()
response = generate_answer(user_input)

submit = st.button("Generate")

#If the button clicked
if submit:
    st.subheader("Answer: ")
    st.write(response)