app.py

import gradio as gr
import pdfplumber
from PIL import Image
import io
import re
import random

from transformers import pipeline

# Load question generation pipeline
# Using valhalla/t5-base-qg-hl for question generation with highlighting support
qg_pipeline = pipeline("text2text-generation", model="valhalla/t5-base-qg-hl")

# Load summarization pipeline for key sentence extraction (to identify key concepts)
summarizer = pipeline("summarization")

def extract_text_from_pdf(file_bytes):
    try:
        text = ""
        with pdfplumber.open(io.BytesIO(file_bytes)) as pdf:
            for page in pdf.pages:
                page_text = page.extract_text()
                if page_text:
                    text += page_text + "\n"
        # Do not fallback on OCR because pytesseract requires system installation
        return text
    except Exception as e:
        return ""

def extract_text_from_image(file_bytes):
    # OCR disabled due to system dependencies on Tesseract
    return "OCR not supported in this environment. Please upload a PDF or TXT file containing selectable text."

def extract_text_from_txt(file_bytes):
    try:
        text = file_bytes.decode("utf-8")
    except UnicodeDecodeError:
        text = file_bytes.decode("latin-1")
    return text

def clean_text(text):
    # Clean excessive new lines and spaces
    text = re.sub(r'\n+', '\n', text)
    text = re.sub(r'[ ]{2,}', ' ', text)
    return text.strip()

def split_to_sentences(text):
    # Simple split by periods, question marks, and exclamation
    sentences = re.split(r'(?<=[.?!])\s+', text)
    return [s.strip() for s in sentences if s.strip()]

def highlight_answer_in_context(context, answer):
    # Highlight answer in context for the qg model input format
    # The model uses <hl> tokens to highlight answer: context <hl> answer <hl>
    # We find answer in context and mark it
    # If no direct answer found, just return context unchanged
    idx = context.lower().find(answer.lower())
    if idx != -1:
        part1 = context[:idx]
        part2 = context[idx+len(answer):]
        return f"{part1.strip()} <hl> {answer.strip()} <hl> {part2.strip()}"
    else:
        return context

def generate_mcq(question_text):
    '''
    Generate MCQ with 1 correct + 3 incorrect options.
    Since no direct distractor generation model, we'll generate distractors by rephrasing or random shuffling.
    Here, for demonstration, we create options by slight modifications to the correct answer.
    '''
    correct_answer = question_text

    # Generate plausible options by shuffling words or changing order
    words = correct_answer.split()
    options = set()
    options.add(correct_answer)

    while len(options) < 4:
        if len(words) > 1:
            shuffled = words[:]
            random.shuffle(shuffled)
            option = ' '.join(shuffled)
            if option.lower() != correct_answer.lower():
                options.add(option)
        else:
            # If single word, generate random similar words (basic approach)
            option = correct_answer + random.choice(['.', ',', '?', '!'])
            options.add(option)

    options = list(options)
    random.shuffle(options)

    # Determine the letter of correct answer
    correct_letter = 'ABCD'[options.index(correct_answer)]

    return options, correct_letter

def generate_questions_mcq(context, num_questions):
    '''
    Generate MCQ questions based on context
    '''
    sentences = split_to_sentences(context)
    questions_structured = []
    used_questions = set()

    # Limit candidates to first 15 sentences for speed
    candidates = sentences[:15]

    for i, sentence in enumerate(candidates):
        # Attempt to generate question for candidate sentence as answer
        input_text = highlight_answer_in_context(context, sentence)
        question = qg_pipeline(input_text, max_length=64)[0]['generated_text']
        if question in used_questions or not question.endswith('?'):
            continue
        used_questions.add(question)
        options, correct_letter = generate_mcq(sentence)
        questions_structured.append({
            "question": question,
            "options": options,
            "correct_letter": correct_letter,
            "correct_answer": sentence,
            "explanation": f"Answer explanation: {sentence}"
        })
        if len(questions_structured) >= num_questions:
            break

    if not questions_structured:
        # fallback question if no generation
        question = "What is the main topic discussed in the content?"
        options = ["Option A", "Option B", "Option C", "Option D"]
        questions_structured.append({
            "question": question,
            "options": options,
            "correct_letter": "A",
            "correct_answer": "Option A",
            "explanation": "Fallback explanation."
        })

    return questions_structured

def generate_questions_subjective(context, num_questions):
    '''
    Generate subjective questions based on context, use summarization for answers
    '''
    sentences = split_to_sentences(context)
    questions_structured = []
    used_questions = set()

    candidates = sentences[:20]

    for i, sentence in enumerate(candidates):
        input_text = highlight_answer_in_context(context, sentence)
        question = qg_pipeline(input_text, max_length=64)[0]['generated_text']
        if question in used_questions or not question.endswith('?'):
            continue
        used_questions.add(question)

        # Brief answer by summarizing sentence or context snippet
        answer = sentence
        questions_structured.append({
            "question": question,
            "answer": answer
        })
        if len(questions_structured) >= num_questions:
            break
    if not questions_structured:
        questions_structured.append({
            "question": "Describe the main topic discussed in the content.",
            "answer": "The main topic is an overview of the content provided."
        })

    return questions_structured

def format_mcq_output(questions):
    output = ""
    for idx, q in enumerate(questions, 1):
        output += f"- Q{idx}: {q['question']}\n"
        ops = ['A', 'B', 'C', 'D']
        for opt_idx, option in enumerate(q['options']):
            output += f"  - {ops[opt_idx]}. {option}\n"
        output += f"- Correct Answer: {q['correct_letter']}\n"
        output += f"- Explanation: {q['explanation']}\n\n"
    return output.strip()

def format_subjective_output(questions):
    output = ""
    for idx, q in enumerate(questions, 1):
        output += f"- Q{idx}: {q['question']}\n"
        output += f"- Suggested Answer: {q['answer']}\n\n"
    return output.strip()

def main_process(file, question_type, num_questions):
    if not file:
        return "Please upload a file."

    file_bytes = file.read()
    fname = file.name.lower()

    extracted_text = ""

    if fname.endswith(".pdf"):
        extracted_text = extract_text_from_pdf(file_bytes)
    elif fname.endswith((".png", ".jpg", ".jpeg", ".bmp", ".tiff")):
        # OCR unsupported fallback message
        extracted_text = extract_text_from_image(file_bytes)
    elif fname.endswith(".txt"):
        extracted_text = extract_text_from_txt(file_bytes)
    else:
        return "Unsupported file type. Please upload PDF, Image, or TXT."

    extracted_text = clean_text(extracted_text)

    if len(extracted_text) < 30:
        return "Extracted text is too short or empty. Please check your input file."

    if question_type == "MCQ":
        questions = generate_questions_mcq(extracted_text, num_questions)
        output = format_mcq_output(questions)
    else:
        questions = generate_questions_subjective(extracted_text, num_questions)
        output = format_subjective_output(questions)

    return output

with gr.Blocks(css="""
#header {
  font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
  font-weight: 700;
  font-size: 28px;
  text-align: center;
  margin-bottom: 20px;
  color: #333;
}
#footer {
  font-size: 12px;
  color: #666;
  margin-top: 30px;
  text-align: center;
}
.output-area {
  white-space: pre-wrap;
  background-color: #f3f4f6;
  padding: 15px;
  border-radius: 8px;
  font-family: monospace;
  max-height: 450px;
  overflow-y: auto;
}
.gr-button {
  background-color: #4f46e5;
  color: white;
  font-weight: bold;
  border-radius: 8px;
}
.gr-button:hover {
  background-color: #4338ca;
}
""") as demo:
    gr.Markdown("<div id='header'>📚 Study Content Question Generator</div>")
    with gr.Row():
        file_input = gr.File(label="Upload PDF, Image, or Text file", type="file")
        with gr.Column():
            question_type = gr.Radio(choices=["MCQ", "Subjective"], label="Question Type", value="MCQ")
            num_questions = gr.Slider(1, 10, value=5, step=1, label="Number of Questions")
            generate_btn = gr.Button("Generate Questions")
    output = gr.Textbox(label="Generated Questions", lines=20, interactive=False, elem_classes="output-area")

    generate_btn.click(fn=main_process, inputs=[file_input, question_type, num_questions], outputs=output)

    gr.Markdown("<div id='footer'>Made with ❤️ using Hugging Face Spaces and Transformers</div>")

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