Rename utils/quiz_generator.py to utils/quiz_offline.py

utils/quiz_generator.py

@@ -1,17 +0,0 @@
-import os
-import requests
-
-HF_TOKEN = os.getenv("HF_TOKEN")
-API_URL = "https://api-inference.huggingface.co/models/google/flan-t5-base"
-headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-
-def generate_quiz(topic, num_questions):
-    prompt = f"Generate {num_questions} MCQ questions based on the following content:\n{topic}"
-    
-    payload = {"inputs": prompt}
-    response = requests.post(API_URL, headers=headers, json=payload)
-
-    if response.status_code != 200:
-        return f"❌ Error: {response.status_code} - {response.json()}"
-
-    return response.json()[0]["generated_text"]

utils/quiz_offline.py

@@ -0,0 +1,28 @@
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load the model and tokenizer
+model_name = "t5-base"  # lightweight and works offline
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+def generate_mcqs(text, num_questions=3):
+    input_text = f"generate questions: {text}"
+    input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
+
+    outputs = model.generate(
+        input_ids=input_ids,
+        max_length=256,
+        num_return_sequences=1,
+        temperature=0.7
+    )
+
+    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return decoded.strip()
+
+# 🔽 TEST EXAMPLE 🔽
+if __name__ == "__main__":
+    sample = """
+    The process of photosynthesis allows plants to convert sunlight, water, and carbon dioxide into food. This process takes place in the chloroplasts and releases oxygen as a byproduct.
+    """
+    print("📘 Quiz Output:\n")
+    print(generate_mcqs(sample))