app.py

import streamlit as st
from langchain.llms import HuggingFaceHub
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

# Function to return the response
def generate_answer(query):
    llm = HuggingFaceHub(
        repo_id="google/flan-t5-xxl",
        model_kwargs={"temperature": 0.7, "max_length": 64, "max_new_tokens": 512}
    )

    template = """Patient's Description: {patient_description}

Doctor's Response: Thank you for sharing details about your headaches. Based on what you've described, it seems like you're managing your symptoms well by identifying triggers, taking naps, practicing yoga, and staying hydrated. It's important to continue these healthy habits.

To further assist you, could you provide more insight into any specific patterns you've noticed with your headaches? For example, do they follow a particular schedule or worsen during certain times of the day? Additionally, have you experienced any other associated symptoms that might be relevant?

Your proactive approach to managing stress and avoiding triggers is commendable. Let's work together to explore additional strategies that may enhance your overall well-being.
"""
    
    prompt = PromptTemplate(template=template, input_variables=["query"])
    llm_chain = LLMChain(prompt=prompt, llm=llm)
    result = llm_chain.run(query)
    return result

# App UI starts here
st.set_page_config(page_title="Doctor Assistant Demo", page_icon=":robot:")
st.header("Doctor Assistant 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("Doctor's Response:")
    st.write(response)