# Lightweight CLIP-based image classifier with safe fallbacks.
from PIL import Image
import requests
import io
import threading

_clip_lock = threading.Lock()
_clip_model = None
_clip_processor = None
_available = False

def initialize_clip():
    global _clip_model, _clip_processor, _available
    try:
        from transformers import CLIPProcessor, CLIPModel
        _clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
        _clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
        _available = True
    except Exception as e:
        # Failed to load CLIP (no internet or packages). Continue with fallback.
        _available = False

def classify_image(image_url: str, candidate_labels=None) -> str:
    """Return best matching label from candidate_labels or 'other' on failure."""
    if candidate_labels is None:
        candidate_labels = [
    # Roads & Transport
    "pothole", "damaged road", "illegal parking", "broken footpath",
    "traffic signal not working", "road accident", "road", "street", "traffic",
    "speed breaker", "crosswalk", "footpath", "pavement",

    # Sanitation & Waste
    "garbage dump", "overflowing dustbin", "open drain", "sewage overflow",
    "dead animal", "toilet issue", "garbage", "trash", "waste", "bin",
    "sanitation", "dirty", "sewage", "cleanliness", "dustbin",

    # Electricity & Lighting
    "streetlight not working", "fallen electric pole", "loose wire", "power outage",
    "streetlight", "lamp", "bulb", "pole", "light", "electric pole",
    "street lamp", "lighting", "dark area", "electricity", "power",
    "broken streetlight", "non-working light", "flickering light", "dim light",
    "street lighting", "outdoor lighting", "public lighting", "night lighting",

    # Water Supply & Flood
    "waterlogging", "pipe burst", "no water supply", "drainage issue", "flood",
    "drain", "drainage", "sewage", "sewer", "leak", "leaking", "leakage",
    "pipe", "water", "overflow", "water supply", "drainage system",

    # Environment & Public Spaces
    "tree fallen", "illegal construction", "park maintenance", "encroachment",
    "park", "garden", "playground", "tree", "bench", "grass", "lawn",
    "recreation", "green space", "park area", "garden area", "flooded park",
    "water in park", "park with water", "playground equipment", "walking path",
    "fountain", "pond", "lake", "outdoor space", "public space",

    # Safety & Emergency
    "fire", "gas leak", "building collapse", "accident site",
    "crime", "robbery", "theft", "violence", "hazard", "danger",
    "safety", "harassment", "emergency", "accident",

    # Noise & Pollution
    "noise pollution", "air pollution", "industrial waste",

    # General
    "other"
]


    if not image_url:
        return "other"

    # If CLIP not available, use heuristic keywords from URL
    if not _available:
        url = image_url.lower()
        for lbl in candidate_labels:
            if lbl in url:
                return lbl
        return "other"

    try:
        resp = requests.get(image_url, timeout=5)
        resp.raise_for_status()
        image = Image.open(io.BytesIO(resp.content)).convert("RGB")
        inputs = _clip_processor(text=candidate_labels, images=image, return_tensors="pt", padding=True)
        outputs = _clip_model(**inputs)
        logits_per_image = outputs.logits_per_image  # shape (1, num_labels)
        probs = logits_per_image.softmax(dim=1)
        best = int(probs.argmax().item())
        return candidate_labels[best]
    except Exception:
        return "other"