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import json
from pathlib import Path
from sentence_transformers import SentenceTransformer, util
import re
# -----------------------------
# normalize subject name (for better grouping)
# -----------------------------
def normalize_subject(subject: str) -> str:
if not subject:
return ""
subject = subject.strip().lower()
subject = re.sub(r"\s+", " ", subject) # collapse spaces
subject = re.sub(r"[^\w\s]", "", subject) # remove symbols
return subject
# -----------------------------
# Load all questions from multiple JSONs
# -----------------------------
def load_all_questions(json_files):
part_a = []
part_b = []
subject = None
print("from all loaded question papers spliting into 3 parts subject, part a and part b")
for file in json_files:
data = json.loads(Path(file).read_text(encoding="utf-8"))
# subject = subject or data.get("subject")
# 🔐 SUBJECT NORMALIZATION & CHECK (ADD HERE)
# current_subject_raw = data.get("subject") or ""
# current_subject = normalize_subject(current_subject_raw)
# if subject is None:
# subject = current_subject
# display_subject = current_subject_raw.strip()
# elif subject != current_subject:
# raise ValueError(
# f"❌ Mixed subjects detected: '{subject}' vs '{current_subject}'"
# )
current_subject_raw = data.get("subject") or ""
current_subject = normalize_subject(current_subject_raw)
# If current JSON has no subject, ignore it
if not current_subject:
pass
# First valid subject wins
elif subject is None:
subject = current_subject
display_subject = current_subject_raw.strip()
# Conflict only if BOTH are non-empty and different
elif subject != current_subject:
raise ValueError(
f"❌ Mixed subjects detected: '{subject}' vs '{current_subject}'"
)
# PART A
for sq in data.get("PART_A", []):
question = sq.get("question")
# ✅ skip if None, empty, or not string
if not isinstance(question, str):
continue
question = question.strip()
if not question:
continue
part_a.append({
"text": question,
"images": [sq.get("image")] if sq.get("image") else []
})
# for q in data["PART_A"]:
# if not q["question"].strip():
# continue
# part_a.append({
# "text": q["question"].strip(),
# "images": [q["image"]] if q["image"] else []
# })
# PART B
for block in data["PART_B"]:
# for sq in block["subquestions"]:
# if not sq["question"].strip():
# continue
# part_b.append({
# "text": sq["question"].strip(),
# "images": [sq["image"]] if sq["image"] else []
# })
for sq in block.get("subquestions", []):
question = sq.get("question")
# ✅ skip if None, empty, or not string
if not isinstance(question, str):
continue
question = question.strip()
if not question:
continue
part_b.append({
"text": question,
"images": [sq.get("image")] if sq.get("image") else []
})
print("data splited into 3 parts subject, part a and part b successfully")
return subject, part_a, part_b
# -----------------------------
# Semantic clustering + frequency
# -----------------------------
def semantic_frequency(questions, threshold=0.70):
print("clustring started for part using all-mpnet-base-v2 model")
model = SentenceTransformer("all-mpnet-base-v2") #SentenceTransformer("all-MiniLM-L6-v2")
texts = [q["text"] for q in questions]
embeddings = model.encode(texts, convert_to_tensor=True, normalize_embeddings=True)
visited = set()
results = []
for i in range(len(texts)):
if i in visited:
continue
cluster = [i]
visited.add(i)
sims = util.cos_sim(embeddings[i], embeddings)[0]
for j in range(len(texts)):
if j not in visited and sims[j] >= threshold:
cluster.append(j)
visited.add(j)
# Representative question (NO rewriting)
rep_question = texts[cluster[0]]
# Merge all images
images = set()
for idx in cluster:
images.update(questions[idx]["images"])
results.append({
"question": rep_question,
"frequency": len(cluster),
"images": list(images) if images else None
})
return results
# -----------------------------
# Main runner
# -----------------------------
def run_semantic_frequency_multiple(input_jsons, output_json):
print("in semantic frequency multiple function")
subject, part_a, part_b = load_all_questions(input_jsons)
output = {
"subject": subject,
"PART_A": semantic_frequency(part_a),
"PART_B": semantic_frequency(part_b)
}
print("✅ Questions clustered and frequency calculated")
Path(output_json).write_text(
json.dumps(output, indent=2),
encoding="utf-8"
)
print("✅ Semantic Frequency Analysis Completed")
print(f"📄 Output saved at: {output_json}")
return output
# -----------------------------
# Entry point
# -----------------------------
# if __name__ == "__main__":
# INPUT_JSONS = [
# "vl_output_bro\\query_3\\final_document.json",
# "vl_output_bro\\query_2\\final_document.json",
# ]
# OUTPUT_JSON = "frequency_output.json"
# run_semantic_frequency_multiple(INPUT_JSONS, OUTPUT_JSON)
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