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7c6ffa6 | 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 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 | """PYQ Pattern Analysis Service.
Analyses extracted PYQ questions for patterns:
- Repeated topics across years
- Marks distribution
- Answer type distribution
- Chapter-wise and topic-wise question counts
- Years seen
Rules:
- Every pattern must be backed by actual question IDs.
- If fewer than 2 years exist, say "not enough data for pattern analysis."
- Do not use words like "sure shot" or fake high-yield.
- Use "seen in uploaded papers" instead.
"""
from __future__ import annotations
from collections import Counter
from dataclasses import dataclass, field
from typing import Any
from sqlalchemy import select
from sqlalchemy.orm import Session
from app.models.previous_question import PreviousQuestion
from app.models.previous_paper import PreviousPaper
@dataclass
class PatternEvidence:
question_id: str
question_text: str
year: int | None
marks: int | None
chapter: str
topic: str
paper_id: str
paper_title: str
source_url: str | None
@dataclass
class TopicPattern:
topic: str
chapter: str
count: int
years_seen: list[int]
question_ids: list[str]
evidence: list[PatternEvidence]
@dataclass
class MarksDistribution:
marks: int
count: int
percentage: float
@dataclass
class AnswerTypeDistribution:
answer_type: str
count: int
percentage: float
@dataclass
class ChapterWiseCount:
chapter: str
total_questions: int
total_marks: int
topics: list[str]
@dataclass
class PatternAnalysisResult:
repeated_topics: list[TopicPattern] = field(default_factory=list)
marks_distribution: list[MarksDistribution] = field(default_factory=list)
answer_type_distribution: list[AnswerTypeDistribution] = field(default_factory=list)
chapter_wise_count: list[ChapterWiseCount] = field(default_factory=list)
topic_wise_count: list[TopicPattern] = field(default_factory=list)
years_seen: list[int] = field(default_factory=list)
total_questions: int = 0
total_papers: int = 0
evidence_list: list[PatternEvidence] = field(default_factory=list)
analysis_note: str = ""
confidence: float = 0.0
_MIN_YEARS_FOR_PATTERN = 2
def analyze_pyq_patterns(
db: Session,
user_id: str,
*,
subject: str = "",
board: str = "",
class_level: str = "",
chapter: str = "",
topic: str = "",
year_range: tuple[int | None, int | None] | None = None,
paper_ids: list[str] | None = None,
) -> PatternAnalysisResult:
"""Analyze PYQ patterns from uploaded questions in the database.
All patterns are backed by actual question IDs from the database.
"""
result = PatternAnalysisResult()
paper_query = (
select(PreviousPaper)
.where(PreviousPaper.user_id == user_id)
.where(PreviousPaper.verification_status == "verified")
)
if paper_ids:
paper_query = paper_query.where(PreviousPaper.id.in_(paper_ids))
if subject:
paper_query = paper_query.where(PreviousPaper.subject == subject)
if board:
paper_query = paper_query.where(PreviousPaper.board == board)
if class_level:
paper_query = paper_query.where(PreviousPaper.class_level == class_level)
papers = list(db.scalars(paper_query).all())
if year_range:
min_year, max_year = year_range
papers = [
paper for paper in papers
if (not min_year or not paper.year or paper.year >= min_year)
and (not max_year or not paper.year or paper.year <= max_year)
]
result.total_papers = len(papers)
questions: list[PreviousQuestion] = []
for paper in papers:
paper_questions = list(paper.questions)
for q in paper_questions:
# Apply filters
if chapter and q.chapter and chapter.lower() not in q.chapter.lower():
continue
if topic and q.topic and topic.lower() not in q.topic.lower():
continue
if year_range:
min_year, max_year = year_range
if min_year and q.year and q.year < min_year:
continue
if max_year and q.year and q.year > max_year:
continue
questions.append(q)
result.total_questions = len(questions)
if not questions:
result.analysis_note = (
"No uploaded PYQ questions found for the given filters. "
"Upload previous year question papers to unlock pattern analysis."
)
return result
# Build evidence list
evidence_map: dict[str, PatternEvidence] = {}
for q in questions:
evidence = PatternEvidence(
question_id=q.id,
question_text=q.question_text[:200],
year=q.year,
marks=q.marks,
chapter=q.chapter or "",
topic=q.topic or "",
paper_id=q.previous_paper.id,
paper_title=q.previous_paper.title,
source_url=q.previous_paper.source_url,
)
evidence_map[q.id] = evidence
result.evidence_list.append(evidence)
# Years seen
all_years = sorted({q.year for q in questions if q.year is not None})
result.years_seen = all_years
# Check if enough data for pattern analysis
if len(all_years) < _MIN_YEARS_FOR_PATTERN:
result.analysis_note = (
f"Only {len(all_years)} year(s) of data available. "
"Upload papers from at least 2 different years for meaningful pattern analysis."
)
result.confidence = 0.3
# Still provide basic stats
_compute_basic_stats(questions, evidence_map, result)
return result
# Sufficient data — compute full patterns
result.confidence = min(0.5 + len(all_years) * 0.05, 1.0)
# Repeated topics
topic_year_map: dict[str, dict[int, list[str]]] = {}
for q in questions:
# Unknown-year evidence cannot prove repetition across exam years.
if q.year is None:
continue
topic_key = q.topic or q.chapter or "Unknown"
if topic_key not in topic_year_map:
topic_year_map[topic_key] = {}
year = q.year
if year not in topic_year_map[topic_key]:
topic_year_map[topic_key][year] = []
topic_year_map[topic_key][year].append(q.id)
for topic_key, year_map in topic_year_map.items():
if len(year_map) >= 2:
years = sorted([y for y in year_map if y > 0])
q_ids = []
evidence_items = []
for y, ids in year_map.items():
q_ids.extend(ids)
evidence_items.extend([evidence_map[qid] for qid in ids if qid in evidence_map])
# Find chapter for this topic
chapter_val = ""
for q in questions:
if (q.topic or q.chapter or "") == topic_key:
chapter_val = q.chapter or ""
break
result.repeated_topics.append(TopicPattern(
topic=topic_key,
chapter=chapter_val,
count=len(q_ids),
years_seen=years,
question_ids=q_ids,
evidence=evidence_items[:5],
))
# Sort repeated topics by frequency
result.repeated_topics.sort(key=lambda t: t.count, reverse=True)
_compute_basic_stats(questions, evidence_map, result)
if not result.analysis_note:
result.analysis_note = (
f"Pattern analysis based on {result.total_questions} questions "
f"across {len(all_years)} years ({', '.join(str(y) for y in all_years)}). "
"All patterns are backed by uploaded question paper evidence."
)
return result
def _compute_basic_stats(
questions: list[PreviousQuestion],
evidence_map: dict[str, PatternEvidence],
result: PatternAnalysisResult,
) -> None:
"""Compute marks distribution, answer type distribution, and chapter-wise counts."""
# Marks distribution
marks_counter: Counter[int] = Counter()
for q in questions:
if q.marks is not None:
marks_counter[q.marks] += 1
total = len(questions) or 1
result.marks_distribution = [
MarksDistribution(
marks=marks,
count=count,
percentage=round(count / total * 100, 1),
)
for marks, count in sorted(marks_counter.items())
]
# Answer type distribution
type_counter: Counter[str] = Counter()
for q in questions:
# Standard extraction fills question_type; answer_type is optional.
at = q.answer_type or q.question_type or "unknown"
type_counter[at] += 1
result.answer_type_distribution = [
AnswerTypeDistribution(
answer_type=at,
count=count,
percentage=round(count / total * 100, 1),
)
for at, count in type_counter.most_common()
]
# Chapter-wise count
chapter_map: dict[str, dict[str, Any]] = {}
for q in questions:
ch = q.chapter or "Unknown"
if ch not in chapter_map:
chapter_map[ch] = {"count": 0, "marks": 0, "topics": set()}
chapter_map[ch]["count"] += 1
chapter_map[ch]["marks"] += q.marks or 0
if q.topic:
chapter_map[ch]["topics"].add(q.topic)
result.chapter_wise_count = [
ChapterWiseCount(
chapter=ch,
total_questions=data["count"],
total_marks=data["marks"],
topics=sorted(data["topics"]),
)
for ch, data in sorted(chapter_map.items(), key=lambda x: x[1]["count"], reverse=True)
]
# Topic-wise count (all topics, not just repeated)
topic_map: dict[str, dict[str, Any]] = {}
for q in questions:
t = q.topic or q.chapter or "Unknown"
if t not in topic_map:
topic_map[t] = {"count": 0, "years": set(), "ids": [], "chapter": q.chapter or ""}
topic_map[t]["count"] += 1
if q.year:
topic_map[t]["years"].add(q.year)
topic_map[t]["ids"].append(q.id)
result.topic_wise_count = [
TopicPattern(
topic=t,
chapter=data["chapter"],
count=data["count"],
years_seen=sorted(data["years"]),
question_ids=data["ids"],
evidence=[evidence_map[qid] for qid in data["ids"][:5] if qid in evidence_map],
)
for t, data in sorted(topic_map.items(), key=lambda x: x[1]["count"], reverse=True)
]
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