Add Air Quality, Noise Pollution, and Graffiti Verification Features

backend/hf_api_service.py

@@ -456,3 +456,49 @@ async def detect_abandoned_vehicle_clip(image: Union[Image.Image, bytes], client
     labels = ["abandoned car", "rusted vehicle", "car with flat tires", "wrecked car", "normal parked car"]
     targets = ["abandoned car", "rusted vehicle", "car with flat tires", "wrecked car"]
     return await _detect_clip_generic(image, labels, targets, client)
+
+async def detect_air_quality_clip(image: Union[Image.Image, bytes], client: httpx.AsyncClient = None):
+    """
+    Detects air quality/smog levels using CLIP.
+    """
+    labels = ["clean air", "mild smog", "dense smog", "hazardous air pollution", "fog", "clear sky", "blue sky", "polluted city"]
+    targets = ["mild smog", "dense smog", "hazardous air pollution", "fog", "polluted city"]
+    return await _detect_clip_generic(image, labels, targets, client)
+
+async def detect_cleanliness_clip(image: Union[Image.Image, bytes], client: httpx.AsyncClient = None):
+    """
+    Verifies if a wall/area is clean (for graffiti removal verification).
+    """
+    labels = ["clean wall", "graffiti", "vandalism", "freshly painted wall", "dirty wall"]
+    # We want to know if it is CLEAN
+    targets = ["clean wall", "freshly painted wall"]
+    return await _detect_clip_generic(image, labels, targets, client)
+
+async def detect_noise_pollution_event(audio_bytes: bytes, client: httpx.AsyncClient = None):
+    """
+    Wraps detect_audio_event to flag noise pollution.
+    """
+    events = await detect_audio_event(audio_bytes, client)
+
+    noise_pollution_labels = [
+        "traffic", "horn", "siren", "jackhammer", "construction", "drill",
+        "engine", "explosion", "gunshot", "scream", "shout", "bark", "chainsaw",
+        "aircraft", "helicopter", "train"
+    ]
+
+    detected_noise = []
+    for event in events:
+        # MIT/AST labels might be specific, e.g. "Car alarm"
+        label = event.get('label', '').lower()
+        score = event.get('score', 0)
+
+        is_noise = any(nl in label for nl in noise_pollution_labels)
+
+        if is_noise and score > 0.3:
+            detected_noise.append({
+                "type": event.get('label'),
+                "confidence": score,
+                "is_pollution": True
+            })
+
+    return detected_noise

backend/requirements-render.txt

@@ -13,5 +13,7 @@ Pillow
 firebase-functions
 firebase-admin
 a2wsgi
-python-jose[cryptography]
-passlib[bcrypt]
+python-jose
+cryptography
+passlib
+bcrypt

backend/routers/detection.py

@@ -35,7 +35,10 @@
     detect_civic_eye_clip,
     detect_graffiti_art_clip,
     detect_traffic_sign_clip,
-    detect_abandoned_vehicle_clip
+    detect_abandoned_vehicle_clip,
+    detect_air_quality_clip,
+    detect_cleanliness_clip,
+    detect_noise_pollution_event
 )
 from backend.dependencies import get_http_client
 import backend.dependencies
@@ -436,3 +439,49 @@ async def detect_abandoned_vehicle_endpoint(request: Request, image: UploadFile
     except Exception as e:
         logger.error(f"Abandoned vehicle detection error: {e}", exc_info=True)
         raise HTTPException(status_code=500, detail="Internal server error")
+
+@router.post("/api/detect-air-quality")
+async def detect_air_quality_endpoint(request: Request, image: UploadFile = File(...)):
+    # Optimized Image Processing: Validation + Optimization
+    _, image_bytes = await process_uploaded_image(image)
+
+    try:
+        client = get_http_client(request)
+        detections = await detect_air_quality_clip(image_bytes, client=client)
+        return {"detections": detections}
+    except Exception as e:
+        logger.error(f"Air quality detection error: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail="Internal server error")
+
+@router.post("/api/detect-cleanliness")
+async def detect_cleanliness_endpoint(request: Request, image: UploadFile = File(...)):
+    # Optimized Image Processing: Validation + Optimization
+    _, image_bytes = await process_uploaded_image(image)
+
+    try:
+        client = get_http_client(request)
+        detections = await detect_cleanliness_clip(image_bytes, client=client)
+        return {"detections": detections}
+    except Exception as e:
+        logger.error(f"Cleanliness detection error: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail="Internal server error")
+
+@router.post("/api/detect-noise-pollution")
+async def detect_noise_pollution_endpoint(request: Request, file: UploadFile = File(...)):
+    # Audio validation
+    if hasattr(file, 'size') and file.size and file.size > 10 * 1024 * 1024:
+         raise HTTPException(status_code=413, detail="Audio file too large")
+
+    try:
+        audio_bytes = await file.read()
+    except Exception as e:
+        logger.error(f"Invalid audio file: {e}", exc_info=True)
+        raise HTTPException(status_code=400, detail="Invalid audio file")
+
+    try:
+        client = get_http_client(request)
+        detections = await detect_noise_pollution_event(audio_bytes, client=client)
+        return {"detections": detections}
+    except Exception as e:
+        logger.error(f"Noise pollution detection error: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail="Internal server error")