app.py

import streamlit as st
from PyPDF2 import PdfReader
import requests
import re

# Set page config first (only needed once)
st.set_page_config(
    page_title="Smart Study Assistant",
    page_icon="🎓",
    layout="wide"
)

# Initialize session state (if needed)
if 'generated' not in st.session_state:
    st.session_state.generated = False

def extract_text(pdf_file):
    """Simplified PDF text extraction"""
    if not pdf_file:
        st.warning("Please upload a PDF file")
        return None
    try:
        return "\n\n".join([page.extract_text() for page in PdfReader(pdf_file).pages])[:3000]
    except Exception as e:
        st.error(f"PDF Error: {str(e)}")
        return None

def call_ai(prompt):
    """Streamlined API call"""
    try:
        response = requests.post(
            "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1",
            headers={"Authorization": f"Bearer {st.secrets.HF_TOKEN}"},
            json={"inputs": prompt},
            timeout=30
        )
        return response.json()[0]['generated_text'] if response.status_code == 200 else f"Error: {response.text}"
    except Exception as e:
        return f"Connection Error: {str(e)}"

def generate_content(text):
    """Direct content generation without separate materials dict"""
    return {
        "summary": call_ai(f"Create structured markdown summary:\n{text}"),
        "bullets": call_ai(f"Convert to hierarchical bullets:\n{text}"),
        "mindmap": call_ai(f"Generate Mermaid mindmap syntax:\n{text}"),
        "flashcards": call_ai(f"Create 5 flashcards:\n{text}"),
        "mcq": call_ai(f"Generate 5 MCQs:\n{text}")
    }

# UI Components
st.title("🎓 Smart Study Assistant")
uploaded_file = st.file_uploader("Upload PDF Lecture Notes", type="pdf")

if uploaded_file:
    if st.button("Generate Study Materials") or st.session_state.generated:
        st.session_state.generated = True
        text = extract_text(uploaded_file)
        
        if text and len(text) > 100:
            with st.spinner("Generating materials..."):
                content = generate_content(text)
                
                # Display results in tabs
                tab1, tab2, tab3, tab4, tab5 = st.tabs([
                    "Summary", "Bullet Points", "Mind Map", 
                    "Flashcards", "MCQ Quiz"
                ])
                
                with tab1:
                    st.markdown(content["summary"])
                
                with tab2:
                    st.markdown(content["bullets"])
                
                with tab3:
                    st.code(content["mindmap"], language="mermaid")
                    st.components.v1.html(f"""
                    <div class="mermaid">{content['mindmap']}</div>
                    <script src="https://cdn.jsdelivr.net/npm/mermaid/dist/mermaid.min.js"></script>
                    <script>mermaid.initialize({{startOnLoad:true}});</script>
                    """, height=400)
                
                with tab4:
                    st.markdown(content["flashcards"])
                
                with tab5:
                    st.markdown(content["mcq"])
        else:
            st.warning("PDF text too short - try another file")