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facial.py — PsyPredict Facial Emotion Detection Endpoint (FastAPI)
Preserved feature: Keras CNN face emotion model (emotion_engine.py unchanged).
Adapted from Flask Blueprint to FastAPI APIRouter with async file handling.
"""
from __future__ import annotations
import logging
import cv2
import numpy as np
from fastapi import APIRouter, File, HTTPException, UploadFile
from fastapi.responses import JSONResponse
from app.schemas import EmotionResponse
from app.services.emotion_engine import emotion_detector
logger = logging.getLogger(__name__)
router = APIRouter()
@router.post("/predict/emotion", response_model=EmotionResponse)
async def predict_emotion(file: UploadFile = File(...)):
"""
Receives an image file and returns detected face emotion + confidence.
Preserved from original implementation — Keras CNN model unchanged.
Gracefully handles empty/corrupt webcam frames without crashing.
"""
if not file.filename:
raise HTTPException(status_code=400, detail="No file selected")
allowed_types = {"image/jpeg", "image/jpg", "image/png", "image/webp"}
if file.content_type not in allowed_types:
raise HTTPException(
status_code=400,
detail=f"Invalid file type '{file.content_type}'. Accepted: JPEG, PNG, WEBP",
)
try:
contents = await file.read()
# Guard: empty frame (webcam not ready yet) — return neutral silently
if not contents or len(contents) < 100:
return EmotionResponse(emotion="neutral", confidence=0.0, message="Empty frame skipped")
if len(contents) > 10 * 1024 * 1024: # 10 MB limit
raise HTTPException(status_code=413, detail="Image too large (max 10MB)")
# Decode to OpenCV format in memory (no disk I/O)
file_bytes = np.frombuffer(contents, np.uint8)
if file_bytes.size == 0:
return EmotionResponse(emotion="neutral", confidence=0.0, message="Empty buffer")
image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
# Guard: corrupted/blank frame — return neutral instead of crashing
if image is None:
return EmotionResponse(emotion="neutral", confidence=0.0, message="Camera frame not ready")
result = emotion_detector.detect_emotion(image)
if "error" in result:
# No face detected — return neutral without crashing
return EmotionResponse(emotion="neutral", confidence=0.0, message=result.get("error"))
return EmotionResponse(**result)
except HTTPException:
raise
except Exception as exc:
# Log at DEBUG level to reduce terminal noise during normal webcam polling
logger.debug("Facial emotion prediction skipped: %s", exc)
return EmotionResponse(emotion="neutral", confidence=0.0, message="Frame processing error") |