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chatbot.py
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chatbot.py
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import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
from langchain_community.vectorstores import FAISS # Updated import based on deprecation warning
from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
google_api_key = os.getenv("GOOGLE_API_KEY")
# Initialize GoogleGenerativeAIEmbeddings
embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
def get_pdf_text(pdf_docs):
text = ""
for pdf in pdf_docs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
return text
def get_text_chunks(text):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
chunks = text_splitter.split_text(text)
return chunks
def get_vector_store(text_chunks):
# Create vector store with FAISS using embeddings
vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
vector_store.save_local("faiss_index")
def get_conversational_chain():
prompt_template = """
Answer the question as detailed as possible from the provided context. If the answer is not in
the provided context, just say, "answer is not available in the context". Don't provide a 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=model, chain_type="stuff", prompt=prompt)
return chain
def user_input(user_question):
# Invoke the model for user input
model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
content = model.invoke(user_question)
# Extracting the content from the response
output_content = content.content if hasattr(content, 'content') else str(content)
# Displaying the output content in Streamlit
st.text_area("Output Content:", value=output_content, height=400)
def main():
st.set_page_config(page_title="RFID-Direct", layout="centered")
st.header("RFID-Direct Chatbot")
user_question = st.text_input("Ask your question")
if user_question:
user_input(user_question)
with st.sidebar:
st.title("Menu:")
pdf_docs = st.file_uploader("Upload Data", accept_multiple_files=True, type=["pdf"])
if st.button("Submit & Process"):
with st.spinner("Processing..."):
raw_text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(raw_text)
get_vector_store(text_chunks)
st.success("Done")
if __name__ == "__main__":
main()