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import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
from langchain_google_genai import GoogleGenerativeAIEmbeddings
import google.generativeai as genai
from langchain.vectorstores import FAISS
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
def get_conversational_chain():
prompt_template = """
Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
provided context just say, "Sorry, I don't know about this. You can ask anther question.", don't provide the wrong answer\n\n
Context:\n {context}?\n
Question: \n{question}\n
Answer:
"""
model = ChatGoogleGenerativeAI(model="gemini-pro",
temperature=0.3)
prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
return chain
def user_input(user_question):
embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
new_db = FAISS.load_local("faiss_index", embeddings)
docs = new_db.similarity_search(user_question)
chain = get_conversational_chain()
response = chain(
{"input_documents":docs, "question": user_question}
, return_only_outputs=True)
print(response)
st.write("Reply: ", response["output_text"])
st.set_page_config("DIAT Rakshak")
st.header("DIAT Assistant Chatbot�")
GOOGLE_API_KEY = st.text_input("Please enter your GOOGLE_API_KEY")
os.environ['GOOGLE_API_KEY'] = GOOGLE_API_KEY
user_question = st.text_input("Hello! I am Rakshak, your DIAT assistant. Please ask your query regarding DIAT.")
if user_question:
user_input(user_question)
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