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import streamlit as st
from pathlib import Path
import os
import google.generativeai as genai
from research_assistant_app.components.data_ingestion import (
get_cleaned_dir_docs,
get_cleaned_input_docs,
)
from research_assistant_app.components.data_querying import user_query
from research_assistant_app.components.data_indexing import run_indexing_pipeline
from dotenv import load_dotenv
load_dotenv()
os.getenv("GOOGLE_API_KEY")
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
st.set_page_config("Chat PDF")
st.header("Your research assistant here to help💁 (Powered by Gemini)")
user_question = st.text_input(
"Chat with existing Pdfs in Pinecone data base or Your added PDF"
)
if user_question:
response = user_query(user_question)
st.write(response)
File = st.file_uploader(
"Upload Your new PDF file to store in Pinecone DB", type=("pdf"), key="pdf"
)
if File: # Save uploaded file to 'Data/' folder.
save_folder = "Data"
save_path = Path(save_folder, File.name)
with open(save_path, mode="wb") as w:
w.write(File.getvalue())
if save_path.exists():
st.success(f"File {File.name} is successfully saved!")
file_dir = f"Data/{File.name}"
res = get_cleaned_input_docs(file_dir)
print(res, "cleaned docs")
index_stats = run_indexing_pipeline(res)
print(index_stats, "checking indexes")
if index_stats != None:
st.success(f"File {File.name} is successfully upserted in Pinecone DB!")
user_question_pdf = st.text_input("Ask a Question from the PDF File")
if user_question_pdf:
response = user_query(user_question_pdf)
st.write(response)
File = None
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