app.py

import gradio as gr
import PyPDF2
from transformers import pipeline
import random
import re
from io import BytesIO

# Initialize models (CPU-optimized)
summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=-1)
qa_generator = pipeline("text2text-generation", model="google/flan-t5-base", device=-1)

def extract_text_from_pdf(pdf_file):
    """Extract text from uploaded PDF"""
    try:
        # Open the file path directly
        with open(pdf_file.name, 'rb') as f:
            pdf_reader = PyPDF2.PdfReader(f)
            text = ""
            # Limit to first 10 pages for CPU performance
            max_pages = min(10, len(pdf_reader.pages))
            for page_num in range(max_pages):
                text += pdf_reader.pages[page_num].extract_text()
        return text[:15000]  # Limit tokens
    except Exception as e:
        return f"Error reading PDF: {str(e)}"

def chunk_text(text, max_length=1000):
    """Split text into manageable chunks"""
    words = text.split()
    chunks = []
    current_chunk = []
    current_length = 0
    
    for word in words:
        current_length += len(word) + 1
        if current_length > max_length:
            chunks.append(" ".join(current_chunk))
            current_chunk = [word]
            current_length = len(word)
        else:
            current_chunk.append(word)
    
    if current_chunk:
        chunks.append(" ".join(current_chunk))
    return chunks

def generate_summary(text):
    """Generate concise summary"""
    if len(text) < 100:
        return "Text too short to summarize."
    
    chunks = chunk_text(text, 1000)
    summaries = []
    
    for chunk in chunks[:3]:  # Limit chunks for CPU
        try:
            summary = summarizer(chunk, max_length=130, min_length=30, do_sample=False)
            summaries.append(summary[0]['summary_text'])
        except:
            continue
    
    return "\n\n".join(summaries) if summaries else "Unable to generate summary."

def generate_flashcards(text, num_cards=5):
    """Generate flashcards from text"""
    sentences = [s.strip() for s in text.split('.') if len(s.strip()) > 20]
    selected = random.sample(sentences, min(num_cards, len(sentences)))
    
    flashcards = []
    for i, sentence in enumerate(selected, 1):
        # Extract key concept (simple heuristic)
        words = sentence.split()
        if len(words) > 5:
            question = f"Q{i}: What is explained by: '{' '.join(words[:5])}...'?"
            answer = sentence
            flashcards.append(f"**{question}**\n\nA: {answer}\n")
    
    return "\n---\n\n".join(flashcards) if flashcards else "Unable to generate flashcards."

def generate_quiz(text, num_questions=3):
    """Generate multiple choice quiz"""
    sentences = [s.strip() for s in text.split('.') if len(s.strip()) > 30]
    selected = random.sample(sentences, min(num_questions, len(sentences)))
    
    quiz = []
    for i, sentence in enumerate(selected, 1):
        prompt = f"Create a multiple choice question about: {sentence[:200]}"
        try:
            result = qa_generator(prompt, max_length=100)
            quiz.append(f"**Question {i}:**\n{result[0]['generated_text']}\n")
        except:
            quiz.append(f"**Question {i}:**\nBased on the text: {sentence[:150]}... (provide your answer)\n")
    
    return "\n---\n\n".join(quiz) if quiz else "Unable to generate quiz."

def process_document(pdf_file, text_input, features):
    """Main processing function"""
    # Get text from PDF or text input
    if pdf_file is not None:
        text = extract_text_from_pdf(pdf_file)
    elif text_input.strip():
        text = text_input[:15000]
    else:
        return "Please provide a PDF file or paste text.", "", "", ""
    
    if text.startswith("Error"):
        return text, "", "", ""
    
    # Generate outputs based on selected features
    summary = generate_summary(text) if "Summary" in features else ""
    flashcards = generate_flashcards(text) if "Flashcards" in features else ""
    quiz = generate_quiz(text) if "Quiz" in features else ""
    
    return text[:500] + "..." if len(text) > 500 else text, summary, flashcards, quiz

# Gradio Interface
with gr.Blocks(theme=gr.themes.Soft(), title="StudyForge AI") as demo:
    gr.Markdown("""
    # 📚 StudyForge AI - Your Intelligent Study Companion
    Transform any textbook chapter or notes into summaries, flashcards, and practice quizzes instantly!
    """)
    
    with gr.Row():
        with gr.Column():
            pdf_input = gr.File(label="Upload PDF (Max 10 pages)", file_types=[".pdf"])
            text_input = gr.Textbox(label="Or Paste Text Here", lines=5, placeholder="Paste your study material...")
            
            features = gr.CheckboxGroup(
                ["Summary", "Flashcards", "Quiz"],
                label="Select What You Need",
                value=["Summary", "Flashcards"]
            )
            
            generate_btn = gr.Button("🚀 Generate Study Materials", variant="primary")
        
        with gr.Column():
            text_preview = gr.Textbox(label="Text Preview", lines=3)
            summary_output = gr.Markdown(label="Summary")
            flashcards_output = gr.Markdown(label="Flashcards")
            quiz_output = gr.Markdown(label="Practice Quiz")
    
    generate_btn.click(
        fn=process_document,
        inputs=[pdf_input, text_input, features],
        outputs=[text_preview, summary_output, flashcards_output, quiz_output]
    )
    
    gr.Markdown("""
    ### Tips:
    - For best results, use clear, well-formatted text (10 pages max for CPU performance)
    - Flashcards work best with content that has clear concepts
    - Processing may take 30-60 seconds on CPU
    """)

if __name__ == "__main__":
    demo.launch()