Create app.py

app.py

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+import streamlit as st
+from PyPDF2 import PdfReader #library to read pdf files
+from langchain.text_splitter import RecursiveCharacterTextSplitter#library to split pdf files
+import os
+
+from langchain_google_genai import GoogleGenerativeAIEmbeddings #to embed the text
+import google.generativeai as genai
+
+from langchain.vectorstores import FAISS #for vector embeddings
+from langchain_google_genai import ChatGoogleGenerativeAI #
+from langchain.chains.question_answering import load_qa_chain #to chain the prompts
+from langchain.prompts import PromptTemplate #to create prompt templates
+from dotenv import load_dotenv
+
+load_dotenv()
+
+genai.configure(api_key = os.getenv("AIzaSyDDGaplA8ya5n_sc4hkMY_vxpsRE6ZDMV8"))
+
+def get_pdf_text(pdf_docs):
+    text = ""
+    # iterate over all pdf files uploaded
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        # iterate over all pages in a pdf
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+def get_text_chunks(text):
+    # create an object of RecursiveCharacterTextSplitter with specific chunk size and overlap size
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size = 10000, chunk_overlap = 1000)
+    # now split the text we have using object created
+    chunks = text_splitter.split_text(text)
+
+    return chunks
+
+def get_vector_store(text_chunks):
+    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001") # google embeddings
+    vector_store = FAISS.from_texts(text_chunks,embeddings) # use the embedding object on the splitted text of pdf docs
+    vector_store.save_local("faiss_index") # save the embeddings in local
+
+def get_conversation_chain():
+
+    # define the prompt
+    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, "answer is not available in the context", don't provide the wrong answer\n\n
+    Context:\n {context}?\n
+    Question: \n{question}\n
+
+    Answer:
+    """
+
+    model = ChatGoogleGenerativeAI(model = "gemini-pro", temperatue = 0.3) # create object of gemini-pro
+
+    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):
+    # user_question is the input question
+    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+    # load the local faiss db
+    new_db = FAISS.load_local("faiss_index", embeddings)
+
+    # using similarity search, get the answer based on the input
+    docs = new_db.similarity_search(user_question)
+
+    chain = get_conversation_chain()
+
+    
+    response = chain(
+        {"input_documents":docs, "question": user_question}
+        , return_only_outputs=True)
+
+    print(response)
+    st.write("Reply: ", response["output_text"])
+
+def main():
+    st.set_page_config("Chat PDF")
+    st.header("Chat with PDF using Gemini")
+
+    user_question = st.text_input("Ask a Question:")
+
+    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()