app.py

import streamlit as st
from tempfile import NamedTemporaryFile

import pprint
import google.generativeai as palm
import os
from dotenv import load_dotenv, find_dotenv
from langchain.embeddings import GooglePalmEmbeddings
from langchain.llms import GooglePalm

from langchain.document_loaders import UnstructuredURLLoader  #load urls into docoument-loader
from langchain.chains.question_answering import load_qa_chain
from langchain.indexes import VectorstoreIndexCreator #vectorize db index with chromadb
from langchain.text_splitter import CharacterTextSplitter #text splitter
from langchain.chains import RetrievalQA
from langchain.document_loaders import UnstructuredPDFLoader  #load pdf
from langchain.agents import create_pandas_dataframe_agent

import pandas as pd
import numpy as np
import pprint

# from request
#
# url = 'https://www.example.com/some_path?some_key=some_value'
# parsed_url = urlparse(url)
# captured_value = parse_qs(parsed_url.query)['some_key'][0]
#
# print(captured_value)
st.write(st.experimental_get_query_params()['pwd'][0][0])

urlInput = st.text_input('Enter your own URL', '', placeholder="Type your URL here (e.g. https://abc.xyz/investor/)", disabled=not isCustomURL)


radioButtonList = ["E-commerce CSV (https://www.kaggle.com/datasets/mervemenekse/ecommerce-dataset)",
"Upload my own CSV",
"Upload my own PDF",
"URL Chat with Google's Latest Earnings (https://abc.xyz/investor/)",
"Enter my own URL"]

# Add some designs to the radio buttons
st.markdown("""
<style>
.stRadio {
  padding: 10px;
  border-radius: 5px;
  background-color: #f5f5f5;
}

.stRadio input[type="radio"] {
  position: absolute;
  opacity: 0;
  cursor: pointer;
}

.stRadio label {
  display: flex;
  justify-content: center;
  align-items: center;
  cursor: pointer;
  font-size: 16px;
  color: #333;
}

.stRadio label:hover {
  color: #000;
}

.stRadio.st-selected input[type="radio"] ~ label {
  color: #000;
  background-color: #d9d9d9;
}
</style>
""", unsafe_allow_html=True)

genre = st.radio(
    "Choose dataset to finetune", radioButtonList, index=0
)

# Initialize language model
load_dotenv(find_dotenv()) # read local .env file
api_key = st.secrets["PALM_API_KEY"] # put your API key here
os.environ["GOOGLE_API_KEY"] = st.secrets["PALM_API_KEY"]
palm.configure(api_key=api_key)
llm = GooglePalm()
llm.temperature = 0.1

pdfCSVURLText = ""
if genre==radioButtonList[0]:
    pdfCSVURLText = "CSV"
    exampleQuestion = "Question1: What was the most sold item? Question2: What was the most common payment?"
    dataDF = pd.read_csv('EcommerceDataset.csv', encoding= 'unicode_escape')
    # st.write('You selected comedy.')
    # else:
    # st.write(f'''Password streamlit app: {st.secrets["PSWD"]}''')
elif genre==radioButtonList[1]:
    pdfCSVURLText = "CSV"
    exampleQuestion = "What are the data columns?"
elif genre==radioButtonList[2]:
    pdfCSVURLText = "PDF"
    exampleQuestion = "Can you summarize the contents?"
elif genre==radioButtonList[3]:
    pdfCSVURLText = "URL"
    exampleQuestion = "What is Google's latest earnings?"
    urls = ['https://abc.xyz/investor/']
    loader = [UnstructuredURLLoader(urls=urls)]
    index = VectorstoreIndexCreator(
            embedding=GooglePalmEmbeddings(),
            text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)).from_loaders(loader)

    chain = RetrievalQA.from_chain_type(llm=llm,
                                chain_type="stuff",
                                retriever=index.vectorstore.as_retriever(),
                                input_key="question")
elif genre==radioButtonList[4]:
    pdfCSVURLText = "URL"
    exampleQuestion = "Can you summarize the contents?"

isCustomURL = genre==radioButtonList[4]
urlInput = st.text_input('Enter your own URL', '', placeholder="Type your URL here (e.g. https://abc.xyz/investor/)", disabled=not isCustomURL)

isCustomPDF = genre==radioButtonList[1] or genre==radioButtonList[2]
uploaded_file = st.file_uploader(f"Upload your own {pdfCSVURLText} here", type=pdfCSVURLText.lower(), disabled=not isCustomPDF)
uploadedFilename = ""
if uploaded_file is not None:
    with NamedTemporaryFile(dir='.', suffix=f'.{pdfCSVURLText.lower()}') as f:
        f.write(uploaded_file.getbuffer())
        uploadedFilename = f.name
        if genre==radioButtonList[1]: # Custom CSV Upload
            dataDF = pd.read_csv(uploadedFilename, encoding= 'unicode_escape')
        elif genre==radioButtonList[2]: # Custom PDF Upload
            pdf_loaders = [UnstructuredPDFLoader(uploadedFilename)]
            pdf_index = VectorstoreIndexCreator(
                    embedding=GooglePalmEmbeddings(),
                    text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)).from_loaders(pdf_loaders)
            pdf_chain = RetrievalQA.from_chain_type(llm=llm,
                                        chain_type="stuff",
                                        retriever=pdf_index.vectorstore.as_retriever(),
                                        input_key="question")

enableChatBox = False
if genre==radioButtonList[0]: # E-commerce CSV
    enableChatBox = True
elif genre==radioButtonList[1]: # Custom CSV Upload
    enableChatBox = uploadedFilename[-4:]==".csv"
elif genre==radioButtonList[2]: # Custom PDF Upload
    enableChatBox = uploadedFilename[-4:]==".pdf"
elif genre==radioButtonList[3]: # Google Alphabet URL Earnings Report
    enableChatBox = True
elif genre==radioButtonList[4]: # Custom URL
    enableChatBox = True

chatTextStr = st.text_input(f'Ask me anything about this {pdfCSVURLText}', '', placeholder=f"Type here (e.g. {exampleQuestion})", disabled=not enableChatBox)
chatWithPDFButton = "CLICK HERE TO START CHATTING"
if st.button(chatWithPDFButton, disabled=not enableChatBox and not chatTextStr): #  Button Cliked


    if genre==radioButtonList[0]: # E-commerce CSV
        # Initializing the agent
        agent = create_pandas_dataframe_agent(llm, dataDF, verbose=False)
        answer = agent.run(chatTextStr)
        st.write(answer)

    elif genre==radioButtonList[1]: # Custom CSV Upload
        # Initializing the agent
        agent = create_pandas_dataframe_agent(llm, dataDF, verbose=False)
        answer = agent.run(chatTextStr)
        st.write(answer)

    elif genre==radioButtonList[2]: # Custom PDF Upload
        pdf_answer = pdf_chain.run(chatTextStr)
        st.write(pdf_answer)

    elif genre==radioButtonList[3]: # Google Alphabet URL Earnings Report
        answer = chain.run(chatTextStr)
        st.write(answer)

    elif genre==radioButtonList[4]: # Custom URL
        urls = [urlInput]
        loader = [UnstructuredURLLoader(urls=urls)]
        index = VectorstoreIndexCreator(
                embedding=GooglePalmEmbeddings(),
                text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)).from_loaders(loader)

        chain = RetrievalQA.from_chain_type(llm=llm,
                                    chain_type="stuff",
                                    retriever=index.vectorstore.as_retriever(),
                                    input_key="question")
        answer = chain.run(chatTextStr)
        st.write(answer)