app.py

import gradio as gr
import tempfile
import shutil
import os
import json
import numpy as np
from pdf2image import convert_from_path
import easyocr
from PyPDF2 import PdfReader
from transformers import pipeline
import random

# -----------------------------
# Initialize OCR and Transformers
# -----------------------------
reader = easyocr.Reader(['en'])

# Question generation model
qg_pipeline = pipeline(
    "text2text-generation",
    model="valhalla/t5-small-qg-prepend",
    tokenizer="t5-small"
)

# Question-answer generation model
qa_pipeline = pipeline(
    "text2text-generation",
    model="valhalla/t5-small-qa-qg-hl",
    tokenizer="t5-small"
)

# -----------------------------
# Extract text from selectable PDFs
# -----------------------------
def extract_text_from_pdf(file_path):
    reader_pdf = PdfReader(file_path)
    text = ""
    for page in reader_pdf.pages:
        t = getattr(page, 'extract_text', lambda: None)()
        if t:
            text += t + "\n"
    return text.strip()

# -----------------------------
# Extract text from scanned PDFs using EasyOCR
# -----------------------------
def extract_text_from_scanned_pdf(file_path):
    pages = convert_from_path(file_path, dpi=150)
    text = ""
    for page in pages:
        try:
            img_array = np.array(page)
            result = reader.readtext(img_array, detail=0)
            text += " ".join(result) + "\n"
        except Exception as e:
            print("OCR error on page:", e)
    return text.strip()

# -----------------------------
# Generate dummy options
# -----------------------------
def generate_options(correct_answer):
    options = [correct_answer]
    dummy_opts = [
        "None of the above",
        "All of the above",
        "Not mentioned",
        "Cannot be determined",
        "Irrelevant information"
    ]
    while len(options) < 4:
        opt = random.choice(dummy_opts)
        if opt not in options:
            options.append(opt)
    random.shuffle(options)
    return options

# -----------------------------
# Main processing function
# -----------------------------
def process_pdf(pdf_file):
    # Save uploaded PDF to temp file
    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf:
        shutil.copy(pdf_file.name, temp_pdf.name)
        temp_pdf_path = temp_pdf.name

    # Step 1: Try extracting text from PDF directly
    extracted_text = extract_text_from_pdf(temp_pdf_path)

    # Step 2: If empty, use OCR
    if not extracted_text.strip():
        extracted_text = extract_text_from_scanned_pdf(temp_pdf_path)

    os.remove(temp_pdf_path)

    if not extracted_text.strip():
        return "❌ Could not extract text. Make sure the PDF has readable content."

    # Step 3: Generate questions
    prompt_q = "generate questions: " + extracted_text[:1000]
    questions_output = qg_pipeline(prompt_q, max_length=128, num_beams=3, num_return_sequences=3)

    # Step 4: Generate answers
    prompt_a = "answer questions: " + extracted_text[:1000]
    answers_output = qa_pipeline(prompt_a, max_length=64, num_beams=3, num_return_sequences=3)

    # Step 5: Build question list
    question_list = []
    for i, q in enumerate(questions_output):
        question = q["generated_text"]
        correct_answer = answers_output[i]["generated_text"] if i < len(answers_output) else "N/A"

        options = generate_options(correct_answer)
        question_list.append({
            "questiontext": question,
            "questiontype": "single_select",
            "marks": 10,
            "options": [
                {"optiontext": opt, "score": "10" if opt == correct_answer else "0"}
                for opt in options
            ]
        })

    # Step 6: Build <questiondata> structure
    data = {
        "title": "Certification Title",
        "totalmarks": "50",
        "time": "20",
        "cutoff": "35",
        "failurl": "",
        "passurl": "",
        "sendpassemail": True,
        "questions": json.dumps({"questions": question_list}),
        "maxattempts": 3
    }

    # Step 7: Wrap JSON in XML CDATA
    xml_output = "<questiondata><![CDATA[" + json.dumps(data, indent=2) + "]]></questiondata>"
    return xml_output

# -----------------------------
# Gradio Interface
# -----------------------------
iface = gr.Interface(
    fn=process_pdf,
    inputs=gr.File(label="📄 Upload your PDF"),
    outputs="text",
    title="PDF → Question & Answer Generator (with OCR)",
    description="Uploads a PDF, extracts text (or OCR for scanned PDFs), and generates XML with questions + answers."
)

iface.launch()