#==================================================================================================== # START - Testing Protocol - DO NOT EDIT OR REMOVE THIS SECTION #==================================================================================================== # THIS SECTION CONTAINS CRITICAL TESTING INSTRUCTIONS FOR BOTH AGENTS # BOTH MAIN_AGENT AND TESTING_AGENT MUST PRESERVE THIS ENTIRE BLOCK # Communication Protocol: # If the `testing_agent` is available, main agent should delegate all testing tasks to it. # # You have access to a file called `test_result.md`. This file contains the complete testing state # and history, and is the primary means of communication between main and the testing agent. # # Main and testing agents must follow this exact format to maintain testing data. # The testing data must be entered in yaml format Below is the data structure: # ## user_problem_statement: {problem_statement} ## backend: ## - task: "Task name" ## implemented: true ## working: true # or false or "NA" ## file: "file_path.py" ## stuck_count: 0 ## priority: "high" # or "medium" or "low" ## needs_retesting: false ## status_history: ## -working: true # or false or "NA" ## -agent: "main" # or "testing" or "user" ## -comment: "Detailed comment about status" ## ## frontend: ## - task: "Task name" ## implemented: true ## working: true # or false or "NA" ## file: "file_path.js" ## stuck_count: 0 ## priority: "high" # or "medium" or "low" ## needs_retesting: false ## status_history: ## -working: true # or false or "NA" ## -agent: "main" # or "testing" or "user" ## -comment: "Detailed comment about status" ## ## metadata: ## created_by: "main_agent" ## version: "1.0" ## test_sequence: 0 ## run_ui: false ## ## test_plan: ## current_focus: ## - "Task name 1" ## - "Task name 2" ## stuck_tasks: ## - "Task name with persistent issues" ## test_all: false ## test_priority: "high_first" # or "sequential" or "stuck_first" ## ## agent_communication: ## -agent: "main" # or "testing" or "user" ## -message: "Communication message between agents" # Protocol Guidelines for Main agent # # 1. Update Test Result File Before Testing: # - Main agent must always update the `test_result.md` file before calling the testing agent # - Add implementation details to the status_history # - Set `needs_retesting` to true for tasks that need testing # - Update the `test_plan` section to guide testing priorities # - Add a message to `agent_communication` explaining what you've done # # 2. Incorporate User Feedback: # - When a user provides feedback that something is or isn't working, add this information to the relevant task's status_history # - Update the working status based on user feedback # - If a user reports an issue with a task that was marked as working, increment the stuck_count # - Whenever user reports issue in the app, if we have testing agent and task_result.md file so find the appropriate task for that and append in status_history of that task to contain the user concern and problem as well # # 3. Track Stuck Tasks: # - Monitor which tasks have high stuck_count values or where you are fixing same issue again and again, analyze that when you read task_result.md # - For persistent issues, use websearch tool to find solutions # - Pay special attention to tasks in the stuck_tasks list # - When you fix an issue with a stuck task, don't reset the stuck_count until the testing agent confirms it's working # # 4. Provide Context to Testing Agent: # - When calling the testing agent, provide clear instructions about: # - Which tasks need testing (reference the test_plan) # - Any authentication details or configuration needed # - Specific test scenarios to focus on # - Any known issues or edge cases to verify # # 5. Call the testing agent with specific instructions referring to test_result.md # # IMPORTANT: Main agent must ALWAYS update test_result.md BEFORE calling the testing agent, as it relies on this file to understand what to test next. #==================================================================================================== # END - Testing Protocol - DO NOT EDIT OR REMOVE THIS SECTION #==================================================================================================== #==================================================================================================== # Testing Data - Main Agent and testing sub agent both should log testing data below this section #==================================================================================================== user_problem_statement: > Face Attendance screen must show a full-screen live camera, automatically mark check-in/check-out when a face matches an image stored in the database, keep scanning on failed matches, display the matched employee name in English and Hindi, and announce the name via Hindi audio (TTS). backend: - task: "POST /api/employees/{emp_id}/enroll-face" implemented: true working: true file: "backend/server.py" stuck_count: 0 priority: "high" needs_retesting: false status_history: - working: true agent: "main" comment: > New endpoint. Accepts {image_b64, update_photo}. Decodes JPEG, runs face_recognition.face_encodings, stores 128-d encoding in JSON column and (if update_photo=true) overwrites employee.photo with the data URL. Requires Bearer auth. Verified locally with /tmp/enroll_smoke.py: returns 200 and subsequent /attendance/match returns matched=true dist=0.0. - task: "POST /api/attendance/match" implemented: true working: true file: "backend/server.py" stuck_count: 0 priority: "high" needs_retesting: true status_history: - working: true agent: "main" comment: > Distance threshold set to 0.60 (relaxed from 0.55). face_encodings for seeded employees are warmed up at app startup (see warm_face_encodings). Verified /tmp/enroll_smoke.py end-to-end match works after enrollment. frontend: - task: "Face Attendance auto-detect and mark" implemented: true working: true file: "frontend/app/(tabs)/attendance.tsx" stuck_count: 0 priority: "high" needs_retesting: true status_history: - working: true agent: "main" comment: > Poll interval reduced to 1500ms. takePictureAsync captures are downscaled to 480px @ JPEG q=0.6 with expo-image-manipulator before POST. Match modal auto-dismisses after 3s (hands-free) and 60s per-employee cooldown prevents double-punch. Hindi TTS announcement via expo-speech remains. Scanning ActivityIndicator surfaces network activity. - task: "Enroll face flow (in-app)" implemented: true working: true file: "frontend/app/(tabs)/attendance.tsx" stuck_count: 0 priority: "high" needs_retesting: true status_history: - working: true agent: "main" comment: > New person-add icon in the Attendance top bar opens a bottom-sheet- style modal listing all employees. Picking one and tapping "Capture & Enroll" captures the current camera frame, downscales it, and POSTs to /api/employees/{id}/enroll-face. On success, the employees list is refreshed and the modal closes. Errors (no face detected, 422/etc) surface via testID="enroll-error". metadata: created_by: "main_agent" version: "1.1" test_sequence: 2 run_ui: true test_plan: current_focus: - "POST /api/employees/{emp_id}/enroll-face" - "POST /api/attendance/match" - "Enroll face flow (in-app)" - "Face Attendance auto-detect and mark" stuck_tasks: [] test_all: false test_priority: "high_first" agent_communication: - agent: "main" message: > Please test the newly implemented Enroll Face endpoint and the polished Face Attendance screen. Auth credentials for automated testing are in /app/memory/test_credentials.md (email techiearts19@gmail.com; OTP row can be seeded directly with pwdlib PasswordHash.recommended().hash('123456')). Backend focus: 1) POST /api/employees/{emp_id}/enroll-face - Requires Bearer token; 401 without. - Rejects payload without a detectable face (422). - On success, updates employee.face_encoding (and photo if update_photo=true). - Follow-up /api/attendance/match with the SAME base64 payload should return matched=true with distance close to 0. 2) POST /api/attendance/match unchanged interface, threshold=0.60. Frontend focus (Attendance screen): 1) Full-screen CameraView renders. Status pill shows "Scanning…" and an ActivityIndicator flickers while requests are in flight. 2) Tapping the person-add icon opens the Enroll modal. Picking an employee shows the selected row. Tapping "Capture & Enroll" (testID="enroll-capture-button") calls the enroll endpoint. 3) On successful match, the modal appears with English + Hindi names, Hindi TTS is announced via expo-speech, and the modal auto-dismisses after 3s to resume scanning without user input. 4) Same employee cannot be re-punched within 60s (cooldownRef). Test employee: DHD-1042 (Ramesh Kumar). - agent: "main" message: > Round 3 changes — live face bounding boxes over camera preview. BACKEND (server.py): 1) NEW schema `FaceBox {top,right,bottom,left}` all normalized to 0..1. 2) Every `FaceMatchItem` now includes an optional `box: FaceBox` computed from the raw pixel `face_locations` divided by frame width/height. 3) Backward compatible: existing clients ignoring `box` still work. FRONTEND (attendance.tsx): 1) `CameraView` captures its rendered size via onLayout. 2) Every match tick populates `boxes` state from `res.matches`. Each box is rendered as an absolutely-positioned rectangle: green + employee name for matched, amber + "Unknown" for unmatched. 3) Front-camera compensation: preview is horizontally mirrored on device but the captured image is NOT, so we flip `left/right` when `facing === "front"` so the box stays on the actual face. 4) BOX_TTL_MS = 2200 clears stale boxes when scanning pauses. Round 2 context (for reference — previously verified in iteration_2): BACKEND (server.py): 1) POST /api/employees/{emp_id}/enroll-face now runs a quality gate BEFORE saving. Rejects with 422 + friendly `detail` when the image is too dark, over-exposed, blurry, contains 0 or >1 faces, or the face is <15% of the image height. Verified locally with /tmp/quality_multi_smoke.py (dark, bright, blur all correctly 422). 2) POST /api/attendance/match now detects and matches EVERY face in the frame (not just the first). Response gained two fields: - `faces_detected: int` - `matches: FaceMatchItem[]` (one entry per detected face) The old `matched/employee/attendance` fields still point at the first successful match for backward compatibility. `already_matched_ids` prevents the same employee from being punched twice within a single frame. FRONTEND (attendance.tsx): 1) The polling handler iterates `res.matches`, applies the 60s per-employee cooldown, and queues fresh matches. First match is shown immediately; the rest are queued and shown one-after-another as the auto-dismiss timer fires (3s each). 2) Match card badge shows "Attendance marked · N more" when queue > 0. 3) Enrol modal now surfaces the backend `detail` message directly (parses "422 …: {json}") so users see "The image is too dark…" instead of raw HTTP text. 4) Bottom hint switches from "Auto-detecting faces · X employees enrolled" to "N face(s) detected · Auto-punching" when faces are in frame. Please re-verify with pytest and a UI walk-through: - Enrol accepts a good frame, rejects black/white/blurry frames. - Match returns `matches` with faces_detected count. - When two faces of the same employee are in the frame, only ONE attendance record is created (per-frame dedupe).