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.env

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+GOOGLE_API_KEY="AIzaSyC9eG7mMxZqHojCeFm8xjLOaduRUWjNku8"

app.py

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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
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+google_api_key = os.getenv("GOOGLE_API_KEY")
+if google_api_key is None:
+    st.error("GOOGLE_API_KEY is not set. Please set it in the .env file.")
+else:
+    genai.configure(api_key=google_api_key)
+
+# Global variable to store chat history
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+
+# Function to extract text from PDF files
+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
+
+# Function to split text into chunks
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+# Function to create and save a 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")
+
+# Function to load a question-answering chain
+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, "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", temperature=0.5)
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+    return chain
+
+# Function to handle user input and generate a response
+def user_input(user_question):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    
+    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    docs = new_db.similarity_search(user_question)
+
+    chain = get_conversational_chain()
+
+    response = chain(
+        {"input_documents": docs, "question": user_question},
+        return_only_outputs=True
+    )
+
+    response_text = response["output_text"]
+    
+    # Update chat history
+    st.session_state.chat_history.append({"question": user_question, "answer": response_text})
+    
+    st.write("Reply: ", response_text)
+
+# Function to download chat history as a .txt file
+def download_chat_history_txt():
+    chat_lines = [f"Question: {entry['question']}\nAnswer: {entry['answer']}\n" for entry in st.session_state.chat_history]
+    chat_text = "\n".join(chat_lines)
+    return chat_text
+
+# Main function to run the Streamlit app
+def main():
+    st.set_page_config(page_title="Chat PDF", layout="wide")
+    st.header("Chat with PDF")
+
+    user_question = st.text_input("Ask a Question from the PDF Files")
+
+    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"):
+            if pdf_docs:
+                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")
+            else:
+                st.error("Please upload at least one PDF file.")
+        
+        # Only one download button
+        chat_txt = download_chat_history_txt()
+        st.download_button("Download Chat History (TXT)", chat_txt, file_name="chat_history.txt", mime="text/plain")
+
+if __name__ == "__main__":
+    main()