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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 | |
| 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_dotenv() | |
| API_KEYS = [os.getenv("APIKEY1"), os.getenv("APIKEY2")] | |
| current_key_index = -1 | |
| def switch_api_key(): | |
| global current_key_index | |
| current_key_index = (current_key_index + 1) % len(API_KEYS) | |
| return API_KEYS[current_key_index] | |
| 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) | |
| return text_splitter.split_text(text) | |
| def get_vector_store(text_chunks): | |
| api_key = switch_api_key() | |
| embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=api_key) | |
| vector_store = FAISS.from_texts(text_chunks, embedding=embeddings) | |
| vector_store.save_local("faiss_index") | |
| def get_conversational_chain(): | |
| api_key = switch_api_key() | |
| prompt_template = """ | |
| You are a helpful assistant that only answers based on the context provided from the PDF documents. | |
| Do not use any external knowledge or assumptions. If the answer is not found in the context below, reply with "I don't know." | |
| Context: | |
| {context} | |
| Question: | |
| {question} | |
| Answer: | |
| """ | |
| model = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0, google_api_key=api_key) | |
| 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): | |
| api_key = switch_api_key() | |
| embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=api_key) | |
| 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) | |
| st.write("Reply: ", response["output_text"]) | |
| # Streamlit application | |
| def main(): | |
| st.set_page_config("Chat PDF") | |
| st.header("CSC 121: Computers and Scientific Thinking (Chatbot)") | |
| st.subheader("Ask a question ONLY from the CSC 121 textbook of Dr. Reed",divider=True) | |
| user_question = st.text_input("Ask a question") | |
| if user_question: | |
| user_input(user_question) | |
| pdf_docs = st.file_uploader("Upload PDF files", 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() | |