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.env

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+GOOGLE_API_KEY="AIzaSyDs9p7qZMOl8XG_th5QPZ7ILgMlhzSxSBw"

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()
+api_key = os.getenv("GOOGLE_API_KEY")
+genai.configure(api_key=api_key)
+
+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):
+    embedding_function = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vector_store = FAISS.from_texts(text_chunks, embedding=embedding_function)
+    vector_store.save_local("faiss_index")
+
+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-1.5-pro-latest", 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, 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)
+    return response["output_text"]
+
+# Main function
+def main():
+    st.header("ChatBot")
+
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    with st.form(key="uploader_form"):
+        pdf_docs = st.file_uploader("Upload your PDF Files", accept_multiple_files=True)
+        submit_button = st.form_submit_button(label="Submit & Process")
+
+    if submit_button:
+        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("Processing completed successfully.")
+        else:
+            st.warning("Please upload at least one PDF file.")
+
+    # Display chat messages from history on app rerun
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # React to user input
+    if prompt := st.chat_input("Ask a question from the PDF files"):
+        # Display user message in chat message container
+        st.chat_message("user").markdown(prompt)
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+
+        response = user_input(prompt)
+        # Display assistant response in chat message container
+        with st.chat_message("assistant"):
+            st.markdown(response)
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": response})
+
+if __name__ == "__main__":
+    main()
+

requirements.txt

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+streamlit
+streamlit-option-menu
+streamlit
+google-generativeai
+python-dotenv
+langchain
+PyPDF2
+chromadb
+faiss-cpu
+langchain_google_genai