Upload app.py

app.py

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+# Imports
+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_community.vectorstores import FAISS
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()  
+os.getenv("GOOGLE_API_KEY")
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))  # Configure Google Generative AI
+
+# Extracts text from all pages of provided PDF documents
+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
+
+# Splits text into chunks of 10,000 characters with 1,000 character overlap
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+# Creates and saves a FAISS vector store from text chunks
+def get_vector_store(text_chunks):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+    vector_store.save_local("faiss_index")
+
+# Creates and returns a conversational chain for question answering
+def get_conversational_chain():
+    prompt_template = """
+Answer the question concisely, focusing on the most relevant and important details from the PDF context. 
+Refrain from mentioning any mathematical equations, even if they are present in provided context. 
+Focus on the textual information available. Please provide direct quotations or references from PDF
+to back up your response. If the answer is not found within the PDF, 
+please state "answer is not available in the context.\n\n
+
+Context:\n {context}?\n
+Question: \n{question}\n
+
+Example response format:
+- Overview: (brief summary or introduction)
+- Key points: 
+(point 1: paragraph for main details)
+(point 2: paragraph for main details)
+...
+
+Use a mix of paragraphs and points to effectively convey the information.
+"""
+
+# Adjust temperature parameter to lower value to: 
+# reduce model creativity & focus on factual accuracy
+    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.2)
+
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+
+    return chain
+
+# Processes user question and provides a response
+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.invoke(
+        {"input_documents": docs, "question": user_question},
+        return_only_outputs=True
+    )
+
+    st.write("Reply: ", response["output_text"])  
+
+# Streamlit UI
+def main():
+    st.set_page_config(page_title="Chat with PDFs", page_icon="")
+    st.header("Chat with multiple PDFs using AI 💬")
+
+    user_question = st.text_input("Ask a Question from PDF file(s)")  
+
+    if user_question:
+        user_input(user_question)
+
+    with st.sidebar:
+        st.title("Menu ✨")
+        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button ",
+                                     accept_multiple_files=True)
+
+        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()