app.py

from langchain.llms import openai
import openai
from dotenv import load_dotenv
import os
import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate
from langchain.schema import SystemMessage, HumanMessage

load_dotenv()

# openai.api_key = os.environ["OPENAI_API_KEY"]
openai.api_key = os.getenv("OPENAI_API_KEY")
# model_name = os.environ["MODEL_NAME"]


# Function to ask a question using LangChain
def ask_question_with_langchain(question):
    # Initialize the OpenAI chat model
    chat = ChatOpenAI(
        model="ft:gpt-3.5-turbo-0125:shubham-gupta::AqKlxnFo",  # Use "gpt-4" or "gpt-3.5-turbo"
        temperature=0.3,  # Adjust temperature for creativity
        openai_api_key=openai.api_key  # Replace or set it in the environment
    )

    # Define the conversation structure
    messages = [
        SystemMessage(content="You are an medical assistant who specializes in infectious diseases."),
        HumanMessage(content=question)
    ]

    # Generate a response
    response = chat(messages)
    return response.content  # Extract the content of the assistant's response

# Example usage
if __name__ == "__main__":
    st.set_page_config(page_title=" PPA assistant")
    st.header("InfektIQ")
    input=st.text_input("Input: ",key="input")
    response=ask_question_with_langchain(input)
    submit=st.button("Ask the question")
    ## If ask button is clicked
    if submit:
        st.subheader("The Response is")
        st.write(response)