app.py

# QandA Chatbot - attempt 1
from langchain.llms import OpenAI

# from dotenv import load_dotenv
import os

# take environment variables from .env
# load_dotenv()

import streamlit as st

# load OpenAI model and get a response


def get_openai_response(question):
    llm = OpenAI(
        openai_api_key=os.getenv("OPEN_API_KEY"),
        model_name="gpt-3.5-turbo-instruct",
        temperature=0.6,
    )
    response = llm(question)
    return response
    # modify with chain and other stuff


## streamlit app

st.set_page_config(page_title="QandA Demo")
st.header("QandA - Langchain Application")
st.write("UPDATE: This app uses the 'gpt-3.5-turbo-instruct' model through Langchain to answer queries")


input = st.text_input("Enter your query: ", key=input)
response = get_openai_response(input)


submit = st.button("Generate")
if submit:
    st.subheader("The response is")
    st.write(response)

# pip install langchain

# from langchain.llms import OpenAI

# # from dotenv import load_dotenv
# import os

# # take environment variables from .env
# # load_dotenv()

# import streamlit as st

# # load OpenAI model and get a response


# def get_openai_response(question):
#     llm = OpenAI(
#         openai_api_key=os.getenv("OPEN_API_KEY"),
#         model_name="gpt-3.5-turbo-instruct",
#         temperature=0.6,
#     )
#     response = llm(question)
#     return response
#     # modify with chain and other stuff


# ## streamlit app

# st.set_page_config(page_title="Trail Demo")
# st.header("Sample")
# st.write("UPDATE: This app uses the 'gpt-3.5-turbo-instruct' model through Langchain")


# # input = st.text_input("Enter your query: ", key=input)
# uploaded_file = st.file_uploader('Choose your .pdf file', type="pdf")
# if uploaded_file is not None:
#     df = extract_data(uploaded_file)
# response = get_openai_response(df)


# submit = st.button("Generate")
# if submit:
#     st.subheader("The response is")
#     st.write(response)