Create main.py

main.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
+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")
+
+
+    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()