Spaces:
Sleeping
Sleeping
File size: 2,992 Bytes
b57b4eb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
# quiz_logic.py
import random
import nltk
from transformers import pipeline
from nltk.tokenize import sent_tokenize
# Download required tokenizer
nltk.download('punkt', quiet=True)
# Load NLI model
nli = pipeline("text-classification", model="facebook/bart-large-mnli")
def validate_inputs(context, num_questions, difficulty):
if not context.strip():
return False, "Context cannot be empty."
sentences = sent_tokenize(context)
if len(sentences) < num_questions:
return False, f"Context has only {len(sentences)} sentences, but {num_questions} questions requested."
if difficulty not in ["easy", "medium", "hard"]:
return False, "Difficulty must be 'easy', 'medium', or 'hard'."
return True, sentences
def apply_noise(sentence: str, level: str) -> str:
if level == "easy":
return sentence
elif level == "medium":
if "Sun" in sentence:
return sentence.replace("Sun", "Moon")
return sentence.replace("is", "is not") if "is" in sentence else sentence
elif level == "hard":
if "eight" in sentence:
return sentence.replace("eight", "ten")
return sentence.replace("planets", "stars") if "planets" in sentence else sentence
return sentence
def generate_statements(context, n, difficulty, sentences):
random.seed(42)
selected = random.sample(sentences, min(n * 2, len(sentences)))
final = []
for s in selected:
clean = s.strip()
modified = apply_noise(clean, difficulty)
label = "ENTAILMENT" if clean == modified else "CONTRADICTION"
final.append({"statement": modified, "actual_label": label})
if len(final) >= n:
break
return final
def score_answers(context, answers):
score = 0
results = []
for answer in answers:
statement = answer.get('statement')
user_answer = answer.get('user_answer', '').strip().lower()
if user_answer not in ['true', 'false']:
results.append({
"statement": statement,
"result": "Invalid answer. Please use 'true' or 'false'."
})
continue
input_text = f"{context} [SEP] {statement}"
result = nli(input_text)[0]
if result["label"] == "neutral":
results.append({
"statement": statement,
"result": "Skipped due to ambiguous statement."
})
continue
model_label = "ENTAILMENT" if result["label"] == "entailment" else "CONTRADICTION"
is_correct = (model_label == "ENTAILMENT" and user_answer == "true") or \
(model_label == "CONTRADICTION" and user_answer == "false")
results.append({
"statement": statement,
"result": "Correct" if is_correct else f"Incorrect (Correct answer: {'True' if model_label == 'ENTAILMENT' else 'False'})"
})
if is_correct:
score += 1
return score, results |