Create app.py

app.py

@@ -0,0 +1,115 @@
+# ---------------- app.py ----------------
+from flask import Flask, request, jsonify
+import os
+import uuid
+import pdfplumber
+from docx import Document
+import openpyxl
+from werkzeug.utils import secure_filename
+from model_utils import extract_mcqs_with_model
+
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = 'uploads'
+os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
+
+quiz_data_store = {}
+
+def extract_text_from_pdf(filepath):
+    with pdfplumber.open(filepath) as pdf:
+        return "\n".join([p.extract_text() for p in pdf.pages if p.extract_text()])
+
+def extract_text_from_docx(filepath):
+    doc = Document(filepath)
+    return "\n".join([para.text for para in doc.paragraphs])
+
+def extract_text_from_excel(filepath):
+    wb = openpyxl.load_workbook(filepath)
+    sheet = wb.active
+    text = ""
+    for row in sheet.iter_rows(min_row=2, values_only=True):
+        if any(row):
+            text += " ".join([str(cell) for cell in row if cell is not None]) + "\n"
+    return text
+
+@app.route('/upload', methods=['POST'])
+def upload_file():
+    if 'file' not in request.files:
+        return jsonify({'error': 'No file provided'}), 400
+
+    file = request.files['file']
+    filename = secure_filename(file.filename)
+    ext = filename.split('.')[-1].lower()
+
+    uid = str(uuid.uuid4())
+    save_path = os.path.join(app.config['UPLOAD_FOLDER'], uid + '_' + filename)
+    file.save(save_path)
+
+    if ext == 'pdf':
+        text = extract_text_from_pdf(save_path)
+    elif ext == 'docx':
+        text = extract_text_from_docx(save_path)
+    elif ext in ['xls', 'xlsx']:
+        text = extract_text_from_excel(save_path)
+    else:
+        return jsonify({'error': 'Unsupported file type'}), 400
+
+    mcqs = extract_mcqs_with_model(text)
+    quiz_id = str(uuid.uuid4())
+    quiz_data_store[quiz_id] = mcqs
+
+    return jsonify({'quiz_id': quiz_id, 'mcqs': mcqs})
+
+@app.route('/submit', methods=['POST'])
+def submit_quiz():
+    data = request.json
+    quiz_id = data.get('quiz_id')
+    user_answers = data.get('answers')
+
+    if quiz_id not in quiz_data_store:
+        return jsonify({'error': 'Invalid quiz ID'}), 404
+
+    mcqs = quiz_data_store[quiz_id]
+    correct = 0
+    for i, ans in enumerate(user_answers):
+        if i < len(mcqs) and ans.upper() == mcqs[i]['answer'].upper():
+            correct += 1
+
+    total = len(mcqs)
+    accuracy = round((correct / total) * 100, 2) if total else 0
+
+    return jsonify({'score': correct, 'total': total, 'accuracy': accuracy})
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)
+
+# ---------------- model_utils.py ----------------
+from transformers import pipeline
+
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+labels = ["question", "option", "answer", "other"]
+
+def classify_chunk(text):
+    result = classifier(text, labels)
+    return result['labels'][0]
+
+def extract_mcqs_with_model(text):
+    chunks = [chunk.strip() for chunk in text.split("\n\n") if chunk.strip()]
+    mcqs = []
+    current = {"question": "", "options": [], "answer": ""}
+
+    for chunk in chunks:
+        label = classify_chunk(chunk)
+        if label == "question":
+            if current["question"]:
+                mcqs.append(current)
+                current = {"question": "", "options": [], "answer": ""}
+            current["question"] = chunk
+        elif label == "option":
+            current["options"].append(chunk)
+        elif label == "answer":
+            current["answer"] = chunk
+
+    if current["question"]:
+        mcqs.append(current)
+    return mcqs
+