`Problem Pattern Detector is a campus intelligence system designed to identify recurring and emerging issues from student complaints.

• Converts unstructured student complaints into structured records containing department, issue, location, time, severity, summary, and key themes.
• Uses Gemma 4B through Ollama for natural-language understanding and complaint classification.
• Stores structured complaints in SQLite for centralized analysis.
• Applies Python-based pattern detection to group related reports, measure frequency, identify emerging issues, and analyze trends.
• Provides an administrator dashboard with department-wise distribution, high-priority issues, emerging problems, trend analysis, and supporting reports.
• Enables students to report problems naturally without manually selecting categories or departments.

The system addresses a key limitation of conventional complaint portals: individual reports are collected but recurring problems can remain difficult to identify at an institutional level.

Problem Pattern Detector converts these isolated reports into aggregated, structured intelligence that can support faster identification, prioritization, and review of campus issues.`