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| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain_community.vectorstores import Chroma | |
| from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI | |
| from dotenv import load_dotenv | |
| import os | |
| load_dotenv() | |
| 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() if page.extract_text() else "" | |
| 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): | |
| embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") | |
| vector_store = Chroma.from_texts(text_chunks, embedding=embeddings, persist_directory="chroma_db") | |
| vector_store.persist() | |
| def get_gemini_response(prompt): | |
| chat_model = ChatGoogleGenerativeAI(model="gemini-1.5-flash", temperature=0.3) | |
| response = chat_model.predict(prompt) | |
| return response | |
| def user_input(user_question): | |
| embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") | |
| new_db = Chroma(persist_directory="chroma_db", embedding_function=embeddings) | |
| docs = new_db.similarity_search(user_question) | |
| context = "\n".join([doc.page_content for doc in docs]) | |
| prompt = f"Context:\n{context}\n\nQuestion: {user_question}\nAnswer:" | |
| response = get_gemini_response(prompt) | |
| st.write("Reply: ", response) | |
| def summarize_text(text, length="medium"): | |
| summary_prompt = f"Summarize the following text in a {length} manner:\n\n" + text | |
| return get_gemini_response(summary_prompt) | |
| def main(): | |
| st.set_page_config("PDF Genius - Chat & Summarizer") | |
| st.title("📚 PDF Genius") | |
| st.markdown("### Chat with your PDFs and generate summaries with Gemini! 💡") | |
| with st.sidebar: | |
| st.title("📌 Menu:") | |
| pdf_docs = st.file_uploader("Upload your 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.session_state["raw_text"] = raw_text | |
| st.success("Processing Complete ✅") | |
| tab1, tab2 = st.tabs(["💬 Chat with PDF", "📄 Document Summarizer"]) | |
| with tab1: | |
| 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 tab2: | |
| st.header("📄 Document Summarizer") | |
| if "raw_text" in st.session_state: | |
| length = st.selectbox("Select Summary Length", ["short", "medium", "detailed"], index=1) | |
| if st.button("Summarize Document"): | |
| summary = summarize_text(st.session_state["raw_text"], length) | |
| st.subheader("Summary:") | |
| st.write(summary) | |
| else: | |
| st.warning("⚠ Please upload and process a PDF first from the sidebar.") | |
| # Footer | |
| st.markdown("---") | |
| st.markdown("Developed by **Sheema Masood** | Powered by **Streamlit**") | |
| st.markdown("🚀 Running at [Hugging Face Spaces](https://huggingface.co/spaces/SheemaMasood/PDFGenius)") | |
| if __name__ == "__main__": | |
| main() | |