Simplify Your Study Life with AI Notes Summarizer using Streamlit & Gemini!

Ever wished you could quickly go through your lengthy PDF notes just before an exam or review session?

What if you could upload a PDF and instantly get a neat bullet-point summary?

Sounds magical, right? 🪄 Well, it’s possible — thanks to the power of Large Language Models (LLMs) and the simplicity of Streamlit. Let's build an interactive AI Notes Summarizer that combines Python, Streamlit, and Google's Gemini API to create intelligent, readable summaries of your PDFs.

🧠 Technologies Used

Python – The base programming language
Streamlit – For building the web interface
PyMuPDF (fitz) – For PDF text extraction
Google Gemini API – To generate bullet-point summaries

🛠️ Step 1 – Setup

Install the required libraries:

```

bash
pip install streamlit google-generativeai pymupdf

Also, get an API key from Google AI Studio to use Gemini.

🧾 Step 2 – Build the App

📤 Extract Text from PDF


python
import fitz

def extract_text_from_pdf(uploaded_file):
    doc = fitz.open(stream=uploaded_file.read(), filetype="pdf")
    text = ""
    for page in doc:
        text += page.get_text()
    return text

✨ Summarize Using Gemini


python
import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel(model_name="gemini-1.5-flash-001")

def summarize_text(text):
    prompt = f"Summarize this in simple bullet points:\n\n{text}"
    response = model.generate_content(prompt)
    return response.text

💻 Streamlit UI


python
import streamlit as st

st.title("🧠 AI Notes Summarizer")

uploaded_file = st.file_uploader("Upload PDF", type="pdf")

if uploaded_file:
    extracted_text = extract_text_from_pdf(uploaded_file)
    st.text_area("📄 Extracted Text", extracted_text, height=300)

    if st.button("Summarize Notes"):
        summary = summarize_text(extracted_text)
        st.subheader("📚 Summary")
        st.markdown(summary)