Fix folder structure for HuggingFace

ml-backend-with-image/Dockerfile

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+# Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

ml-backend-with-image/README.md

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+Civic ML Backend (Full: pHash + Location)
+
+Overview:
+
+- FastAPI backend that accepts citizen reports and performs:
+  - rule-based abuse detection
+  - text duplicate detection
+  - image duplicate detection using pHash (imagehash)
+  - location-based duplicate detection using Haversine formula
+  - image classification using CLIP (if available) with URL fallback
+  - priority detection (keyword-based)
+
+Run:
+
+1. python -m venv .venv
+2. source .venv/bin/activate # Windows: .venv\Scripts\Activate
+3. pip install -r requirements.txt
+4. uvicorn app.main:app --reload --port 8000
+
+Notes:
+
+- The in-memory stores (seen_reports, seen_image_hashes, seen_locations) are ephemeral and reset on server restart.
+- CLIP model download requires internet and may take time; if unavailable, the system uses URL keyword fallback for image labels.
+- data/dataset.jsonl collects all incoming reports and results for later training/audit.

ml-backend-with-image/app/__init__.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+app = FastAPI(title="Civic ML Backend (with Image)")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["http://localhost:3000", "http://localhost:3001", "*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"]
+)
+
+

ml-backend-with-image/app/dataset.py

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+import json
+from pathlib import Path
+
+# Always resolve the dataset path relative to this file so that it works
+# no matter where the application is started from (repo root, service dir, etc.)
+BASE_DIR = Path(__file__).resolve().parent.parent  # points to ml-backend-with-image/
+DATA_FILE = BASE_DIR / "data" / "dataset.jsonl"
+DATA_FILE.parent.mkdir(parents=True, exist_ok=True)
+
+
+def save_report(report_dict: dict):
+    """Append raw report to dataset.jsonl (build dataset dynamically)."""
+    with DATA_FILE.open("a", encoding="utf8") as f:
+        f.write(json.dumps(report_dict, ensure_ascii=False) + "\n")

ml-backend-with-image/app/image_classifier.py

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+# 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"

ml-backend-with-image/app/main.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from app.pipeline import classify_report
+
+app = FastAPI(title="Civic ML Backend API")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.get("/")
+def health():
+    return {"status": "ML API running"}
+
+@app.post("/submit")
+def submit_report(data: dict):
+    return classify_report(data)

ml-backend-with-image/app/models.py

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+from pydantic import BaseModel
+from typing import Optional
+
+class ReportIn(BaseModel):
+    report_id: str
+    description: str
+    category: str
+    user_id: Optional[str] = None
+    image_url: Optional[str] = None
+    latitude: Optional[float] = None
+    longitude: Optional[float] = None
+
+class ReportStatus(BaseModel):
+    report_id: str
+    status: str
+    reason: Optional[str] = None
+    priority: Optional[str] = None
+    category: Optional[str] = None
+    text_category: Optional[str] = None
+    image_category: Optional[str] = None

ml-backend-with-image/app/pipeline.py

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+from app import storage, dataset
+from app import image_classifier as ic
+import re
+
+# Profanity detection
+import warnings
+# Suppress pkg_resources deprecation warnings globally (they're just warnings, not errors)
+warnings.filterwarnings("ignore", category=UserWarning, message=".*pkg_resources.*")
+
+PROFANITY_AVAILABLE = False
+_profanity_predict = None
+try:
+    from profanity_check import predict as _profanity_predict
+    # Test that it works with a known profane word
+    _test_result = _profanity_predict(["fuck"])
+    # Check if result is valid (should be numpy array or list with 1)
+    if _test_result is not None:
+        PROFANITY_AVAILABLE = True
+except ImportError:
+    # Library truly not installed
+    pass  # Silently fall back to keyword-based detection
+except Exception as e:
+    # Any other import/runtime error - silently fall back
+    PROFANITY_AVAILABLE = False
+    _profanity_predict = None
+
+# Category-scoped urgent keywords (same methodology as CATEGORY_KEYWORDS)
+# Keep a Global bucket to preserve previous behavior while enabling per-category tuning
+URGENT_KEYWORDS = {
+    "Global": [
+        # Road & Transport
+        "accident", "collision", "roadblock", "traffic jam", "hit and run",
+        "bridge collapse", "pothole accident", "road caved in",
+
+        # Fire & Hazard
+        "fire", "burning", "smoke", "blast", "explosion", "short circuit",
+        "gas leak", "electric spark", "transformer burst",
+
+        # Water & Flood
+        "flood", "waterlogging", "sewage overflow", "pipe burst",
+        "drain blocked", "heavy rain", "overflowing drain", "contaminated water",
+
+        # Health & Sanitation
+        "garbage overflow", "dead animal", "toxic smell", "mosquito breeding",
+        "epidemic", "dengue outbreak", "cholera", "sanitation hazard",
+
+        # Safety & Security
+        "violence", "crime", "theft", "fight", "robbery", "public hazard",
+        "building collapse", "wall collapse", "tree fallen", "landslide",
+
+        # Other Civic Emergencies
+        "power outage", "electric shock", "streetlight sparks",
+        "ambulance needed", "emergency help"
+    ],
+    # Per-category overrides/extensions (can be expanded as needed)
+    "Road & Traffic": ["accident", "collision", "roadblock", "pothole"],
+    "Water & Drainage": ["flood", "waterlogging", "sewage overflow", "pipe burst"],
+    "Electricity": ["power outage", "short circuit", "electric shock"],
+    "Garbage & Sanitation": ["garbage overflow", "toxic smell"],
+    "Street Lighting": ["streetlight sparks", "dark area"],
+    "Public Safety": ["violence", "robbery", "fire"],
+    "Parks & Recreation": ["tree fallen"],
+    "Other": []
+}
+
+
+# optional model availability flags are handled inside image_classifier
+
+def initialize_models():
+    # initialize image classifier (CLIP) if available
+    try:
+        ic.initialize_clip()
+    except Exception:
+        pass
+
+def classify_report(report: dict):
+    desc = (report.get("description") or "").strip()
+    desc_lower = desc.lower()
+    cat = (report.get("category") or "").strip()
+
+    # 1. Reject if abusive (using profanity-check library + keyword matching)
+    # Comprehensive keyword list for abusive language detection
+    # This is the PRIMARY detection method - ML model is secondary
+    fallback_bad_words = {
+        "fuck", "fucking", "fucked", "fucker", "fuckin", "fucks",
+        "shit", "shitting", "shitted", "shitty", "shits",
+        "bitch", "bitches", "bitching", "bitched",
+        "bastard", "bastards",
+        "asshole", "ass", "asses", "assholes",
+        "dick", "dicks", "dickhead",
+        "cunt", "cunts",
+        "prick", "pricks",
+        "slut", "sluts","bloody",
+        "whore", "whores","rascal",
+        "damn", "damned", "damnit", "dammit",
+        "hell", "hells",
+        "crap", "crappy",
+        "piss", "pissed", "pissing",
+        "idiot", "idiots", "idiotic",
+        "stupid", "stupidity", "stupidly",
+        "moron", "morons",
+        "retard", "retarded",
+        "gay", "gays",  # Context-dependent, but often used abusively
+        "hate", "hateful", "hating"
+    }
+    
+    # ALWAYS check keywords first (most reliable)
+    # Check if any profane word appears in the description (case-insensitive substring match)
+    is_profane_keywords = any(word in desc_lower for word in fallback_bad_words)
+    
+    # Also try ML model if available
+    is_profane_ml = False
+    if PROFANITY_AVAILABLE and _profanity_predict is not None:
+        try:
+            # profanity_check.predict returns a numpy array with 1 for profane, 0 for clean
+            predictions = _profanity_predict([desc])
+            # Handle numpy array, list, or scalar returns
+            if hasattr(predictions, '__getitem__') and len(predictions) > 0:
+                is_profane_ml = bool(int(predictions[0]) == 1)
+            elif hasattr(predictions, 'item'):
+                is_profane_ml = bool(int(predictions.item()) == 1)
+            else:
+                is_profane_ml = bool(int(predictions) == 1)
+        except Exception:
+            # If profanity_check fails at runtime, just use keyword check
+            pass
+    
+    # Reject if EITHER detection method finds profanity
+    is_profane = is_profane_keywords or is_profane_ml
+    
+    if is_profane:
+        result = {
+            "report_id": report["report_id"],
+            "status": "rejected",
+            "reason": "Abusive language detected"
+        }
+        dataset.save_report({**report, **result})
+        return result
+
+    # 2. Category consistency validation (description/image must relate to selected category)
+    CATEGORY_KEYWORDS = {
+        "Road & Traffic": [
+            "pothole", "road", "street", "traffic", "signal", "accident", "jam", "footpath", "speed breaker", "crosswalk", "pavement", "highway", "bridge", "intersection"
+        ],
+        "Water & Drainage": [
+            "drain", "drainage", "sewage", "sewer", "leak", "leaking", "leakage", "waterlogging", "pipe", "flood", "overflow", "water", "supply", "system", "blocked", "clogged"
+        ],
+        "Electricity": [
+            "electric", "electricity", "power", "outage", "wire", "transformer", "short circuit", "shock", "cable", "meter", "electrical", "voltage", "current"
+        ],
+        "Garbage & Sanitation": [
+            "garbage", "trash", "waste", "bin", "dump", "sanitation", "dirty", "sewage", "overflow", "cleanliness", "collection", "disposal", "rubbish", "litter"
+        ],
+        "Street Lighting": [
+            "street light", "streetlight", "lamp", "bulb", "pole", "light not working", "dark", "lighting", "illumination", "street lamp", "electric pole", "night light", "dim", "flickering"
+        ],
+        "Public Safety": [
+            "crime", "robbery", "theft", "violence", "fire", "hazard", "danger", "safety", "harassment", "emergency", "accident", "security", "threat", "risk"
+        ],
+        "Parks & Recreation": [
+            "park", "garden", "playground", "tree", "bench", "grass", "lawn", "recreation", "green space", "playground equipment", "walking path", "fountain", "pond", "lake", "water", "flooded", "maintenance", "amenities"
+        ],
+        "Other": []
+    }
+
+    # Image labels allowed per category (same methodology: category -> labels)
+    IMAGE_LABELS = {
+        "Road & Traffic": ["pothole", "road", "traffic", "street", "footpath", "pavement", "speed breaker", "crosswalk", "damaged road", "illegal parking", "broken footpath", "traffic signal not working", "road accident"],
+        "Garbage & Sanitation": ["garbage", "trash", "dump", "waste", "bin", "sanitation", "dirty", "sewage", "cleanliness", "dustbin", "garbage dump", "overflowing dustbin", "open drain", "sewage overflow", "dead animal", "toilet issue"],
+        "Street Lighting": ["streetlight", "lamp", "bulb", "pole", "light", "electric pole", "street lamp", "lighting", "dark area", "electricity", "power", "streetlight not working", "fallen electric pole", "loose wire", "power outage"],
+        "Water & Drainage": ["drain", "drainage", "sewage", "sewer", "leak", "leaking", "leakage", "pipe", "water", "overflow", "water supply", "drainage system", "waterlogging", "pipe burst", "no water supply", "drainage issue", "flood"],
+        "Electricity": ["transformer", "wire", "meter", "electricity", "power", "electric", "cable", "short circuit", "shock"],
+        "Public Safety": ["fire", "hazard", "danger", "safety", "emergency", "accident", "crime", "robbery", "theft", "violence", "harassment", "gas leak", "building collapse", "accident site"],
+        "Parks & Recreation": ["park", "garden", "playground", "tree", "bench", "grass", "lawn", "recreation", "green space", "park area", "garden area", "tree fallen", "illegal construction", "park maintenance", "encroachment"],
+        "Other": ["other"]
+    }
+
+    allowed_keywords = CATEGORY_KEYWORDS.get(cat, [])
+    desc_match = True if cat == "Other" else any(k in desc_lower for k in allowed_keywords)
+
+    image_url = report.get("image_url")
+    image_cat = None
+    if image_url:
+        image_cat = ic.classify_image(image_url)
+        labels_for_cat = [s.lower() for s in IMAGE_LABELS.get(cat, [])]
+        image_label = str(image_cat).lower() if image_cat is not None else "other"
+        
+        # More flexible matching - check if any part of the image label matches
+        image_match = (cat == "Other") or any(label in image_label or image_label in label for label in labels_for_cat)
+        
+        # Special cases for better matching
+        if cat == "Parks & Recreation":
+            # Accept images that contain park-related terms or water-related terms (for flooded parks)
+            park_related = ["park", "garden", "playground", "tree", "bench", "grass", "lawn", "recreation", "green", "water", "flood", "pond", "lake"]
+            image_match = any(term in image_label for term in park_related)
+        elif cat == "Street Lighting":
+            # Accept images that contain lighting-related terms
+            lighting_related = ["streetlight", "lamp", "bulb", "pole", "light", "electric", "power", "dark", "illumination", "street", "night"]
+            image_match = any(term in image_label for term in lighting_related)
+    else:
+        image_match = False
+
+    has_image = bool(image_url)
+    # If an image is provided and does NOT relate to the category, reject early with a clear reason
+    if has_image and not image_match:
+        result = {
+            "report_id": report["report_id"],
+            "status": "rejected",
+            "reason": "image not related to category"
+        }
+        dataset.save_report({**report, **result})
+        return result
+
+    valid_for_category = (desc_match or image_match) if has_image else desc_match
+
+    if not valid_for_category:
+        reason_parts = []
+        if not desc_match:
+            reason_parts.append("description not related to category")
+        reason = ", ".join(reason_parts) or "not related to category"
+        result = {
+            "report_id": report["report_id"],
+            "status": "rejected",
+            "reason": reason
+        }
+        dataset.save_report({**report, **result})
+        return result
+
+    # 3. Reject if duplicate (spam)
+    if storage.is_duplicate(report.get("user_id"), desc, cat):
+        result = {
+            "report_id": report["report_id"],
+            "status": "rejected",
+            "reason": "Duplicate spam"
+        }
+        dataset.save_report({**report, **result})
+        return result
+    # 3b. Reject image duplicate using pHash
+    if image_url and storage.is_duplicate_image(image_url):
+        result = {
+            "report_id": report["report_id"],
+            "status": "rejected",
+            "reason": "Duplicate spam (image)"
+        }
+        dataset.save_report({**report, **result})
+        return result
+
+    # 4. Reject location duplicate (same text+category within threshold meters)
+    lat = report.get("latitude")
+    lon = report.get("longitude")
+    if lat is not None and lon is not None:
+        if storage.is_duplicate_location(lat, lon, desc, cat):
+            result = {
+                "report_id": report["report_id"],
+                "status": "rejected",
+                "reason": "Duplicate spam (location)"
+            }
+            dataset.save_report({**report, **result})
+            return result
+
+    # 5. Priority (urgent vs normal) using keywords (category-scoped + global)
+    urgent_words = [
+        *(URGENT_KEYWORDS.get(cat, [])),
+        *(URGENT_KEYWORDS.get("Global", []))
+    ]
+    is_urgent = any(word in desc_lower for word in urgent_words)
+    priority = "urgent" if is_urgent else "normal"
+
+    # 6. Image classification (if image provided and not already classified)
+    if image_url and image_cat is None:
+        image_cat = ic.classify_image(image_url)
+    
+    result = {
+        "report_id": report["report_id"],
+        "status": "accepted",
+        "priority": priority,
+        "category": cat,
+        "text_category": cat,
+        "image_category": image_cat
+    }
+    dataset.save_report({**report, **result})
+    return result

ml-backend-with-image/app/storage.py

@@ -0,0 +1,64 @@
+from PIL import Image
+import imagehash
+import requests
+import io
+from math import radians, cos, sin, asin, sqrt
+
+# In-memory stores (ephemeral - reset on server restart)
+seen_reports = set()
+seen_image_hashes = []   # store imagehash objects
+seen_locations = []  # list of tuples (lat, lon, description, category)
+
+def is_duplicate(user_id: str, description: str, category: str) -> bool:
+    key = (user_id or "anon", description.strip().lower(), category.lower())
+    if key in seen_reports:
+        return True
+    seen_reports.add(key)
+    return False
+
+def is_duplicate_image(image_url: str, threshold: int = 5) -> bool:
+    """Check if an image is a duplicate using perceptual hash (pHash).
+    threshold = maximum Hamming distance allowed to consider images equal.
+    Returns True if duplicate found, else stores the hash and returns False.
+    """
+    try:
+        resp = requests.get(image_url, timeout=5)
+        resp.raise_for_status()
+        img = Image.open(io.BytesIO(resp.content)).convert('RGB')
+        img_hash = imagehash.phash(img)
+
+        for h in seen_image_hashes:
+            # imagehash library supports subtraction to get Hamming distance
+            if abs(img_hash - h) <= threshold:
+                return True
+
+        # store hash
+        seen_image_hashes.append(img_hash)
+        return False
+    except Exception:
+        # On any failure to fetch/process image, treat as non-duplicate
+        return False
+
+def haversine(lat1, lon1, lat2, lon2):
+    """Calculate great-circle distance between two lat/lon points in meters."""
+    lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])
+    delta_lat = lat2 - lat1
+    delta_lon = lon2 - lon1
+    a = sin(delta_lat/2)**2 + cos(lat1) * cos(lat2) * sin(delta_lon/2)**2
+    c = 2 * asin(sqrt(a))
+    r = 6371000  # Earth radius in meters
+    return c * r
+
+def is_duplicate_location(lat: float, lon: float, description: str, category: str, threshold: int = 20) -> bool:
+    """Return True if an existing report with same text+category exists within threshold meters."""
+    try:
+        for (lat0, lon0, desc0, cat0) in seen_locations:
+            if desc0.strip().lower() == description.strip().lower() and cat0.lower() == category.lower():
+                dist = haversine(lat, lon, lat0, lon0)
+                if dist <= threshold:
+                    return True
+        # not duplicate — store this location for future checks
+        seen_locations.append((lat, lon, description, category))
+        return False
+    except Exception:
+        return False

ml-backend-with-image/data/dataset.jsonl

@@ -0,0 +1,106 @@
+{"report_id": "r1", "description": "Huge pothole urgent fix", "category": "Road", "user_id": "u1", "image_url": "https://ultralytics.com/images/bus.jpg", "status": "accepted", "priority": "urgent", "text_category": "Road", "image_category": "other"}
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+{"report_id": "1758896696704", "description": "path hole is bloody stupid", "category": "Road & Traffic", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758896697/civicconnect/images/fsez2ag01jubeowq1ujz.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Road & Traffic", "image_category": "pothole"}
+{"report_id": "1758896778759", "description": "garbabge is fuck", "category": "Garbage & Sanitation", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758896778/civicconnect/images/emoevgldc12aa0ocixaf.jpg", "latitude": null, "longitude": null, "status": "rejected", "reason": "Abusive language detected"}
+{"report_id": "1758901794580", "description": "Garbage is on road is full", "category": "Garbage & Sanitation", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758901794/civicconnect/images/ke0o4aew7ydzpcov6xpl.jpg", "latitude": null, "longitude": null, "status": "rejected", "reason": "image not related to category"}
+{"report_id": "1758901820680", "description": "Garbage is on road is full", "category": "Other", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758901820/civicconnect/images/riob8f5c1oz3m0deumyp.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Other", "image_category": "overflowing dustbin"}
+{"report_id": "1758907723799", "description": "strret light is not working in this location", "category": "Street Lighting", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758907724/civicconnect/images/g6d64y5sd4murycnu80o.jpg", "latitude": null, "longitude": null, "status": "rejected", "reason": "image not related to category"}
+{"report_id": "1758907818963", "description": "strret light is not working in this location", "category": "Street Lighting", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758907819/civicconnect/images/dwefzcmauoazbshn35tv.jpg", "latitude": null, "longitude": null, "status": "rejected", "reason": "image not related to category"}
+{"report_id": "1758907835205", "description": "strret light is not working in this location", "category": "Electricity", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758907835/civicconnect/images/u7qbgykieffgkjxwqa0w.jpg", "latitude": null, "longitude": null, "status": "rejected", "reason": "image not related to category"}
+{"report_id": "1758907860526", "description": "strret light is not working in this location", "category": "Parks & Recreation", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758907860/civicconnect/images/bf3f8ttwuxc9enbcpqhs.jpg", "latitude": null, "longitude": null, "status": "rejected", "reason": "image not related to category"}
+{"report_id": "1758907981434", "description": "park is filled with water in this location", "category": "Parks & Recreation", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758907981/civicconnect/images/hd8h4jx8u6r4qmilrzon.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Parks & Recreation", "image_category": "park"}
+{"report_id": "1758908109234", "description": "park is filled with water in this location", "category": "Parks & Recreation", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758908109/civicconnect/images/kl01gjqyezvd1yhezc60.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Parks & Recreation", "image_category": "park"}
+{"report_id": "1758907723799", "description": "strret light is not working in this location", "category": "Street Lighting", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758907724/civicconnect/images/g6d64y5sd4murycnu80o.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Street Lighting", "image_category": "streetlight"}
+{"report_id": "1758907818963", "description": "strret light is not working in this location", "category": "Street Lighting", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758907819/civicconnect/images/dwefzcmauoazbshn35tv.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Street Lighting", "image_category": "streetlight"}
+{"report_id": "1758907835205", "description": "strret light is not working in this location", "category": "Street Lighting", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758907835/civicconnect/images/u7qbgykieffgkjxwqa0w.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Street Lighting", "image_category": "streetlight"}
+{"report_id": "1758907860526", "description": "strret light is not working in this location", "category": "Street Lighting", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758907860/civicconnect/images/bf3f8ttwuxc9enbcpqhs.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Street Lighting", "image_category": "streetlight"}
+{"report_id": "train_park_001", "description": "park flooded with water after heavy rain", "category": "Parks & Recreation", "user_id": "train_user", "image_url": "https://example.com/park_flooded.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Parks & Recreation", "image_category": "park"}
+{"report_id": "train_park_002", "description": "playground equipment damaged in park", "category": "Parks & Recreation", "user_id": "train_user", "image_url": "https://example.com/playground_damaged.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Parks & Recreation", "image_category": "park"}
+{"report_id": "train_park_003", "description": "garden area needs maintenance", "category": "Parks & Recreation", "user_id": "train_user", "image_url": "https://example.com/garden_maintenance.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Parks & Recreation", "image_category": "park"}
+{"report_id": "train_park_004", "description": "tree fallen in park area", "category": "Parks & Recreation", "user_id": "train_user", "image_url": "https://example.com/tree_fallen_park.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "urgent", "text_category": "Parks & Recreation", "image_category": "tree fallen"}
+{"report_id": "train_park_005", "description": "park bench broken and needs repair", "category": "Parks & Recreation", "user_id": "train_user", "image_url": "https://example.com/broken_bench.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Parks & Recreation", "image_category": "park"}
+{"report_id": "train_light_001", "description": "street light not working properly", "category": "Street Lighting", "user_id": "train_user", "image_url": "https://example.com/streetlight_broken.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Street Lighting", "image_category": "streetlight"}
+{"report_id": "train_light_002", "description": "lamp post damaged and needs fixing", "category": "Street Lighting", "user_id": "train_user", "image_url": "https://example.com/lamp_post_damaged.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Street Lighting", "image_category": "streetlight"}
+{"report_id": "train_light_003", "description": "electric pole light not functioning", "category": "Street Lighting", "user_id": "train_user", "image_url": "https://example.com/electric_pole_light.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Street Lighting", "image_category": "streetlight"}
+{"report_id": "train_light_004", "description": "street lamp flickering and dim", "category": "Street Lighting", "user_id": "train_user", "image_url": "https://example.com/flickering_lamp.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Street Lighting", "image_category": "streetlight"}
+{"report_id": "train_light_005", "description": "dark area due to non-working streetlight", "category": "Street Lighting", "user_id": "train_user", "image_url": "https://example.com/dark_street.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Street Lighting", "image_category": "streetlight"}
+{"report_id": "1758908444579", "description": "street light is not working near our locality", "category": "Street Lighting", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758908445/civicconnect/images/gy5oznfklrrsh3u2wzl3.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Street Lighting", "image_category": "street lighting"}
+{"report_id": "1758908512274", "description": "street light is not working in our locality", "category": "Street Lighting", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758908512/civicconnect/images/qum6cuhgkdsxsjtnjwhh.jpg", "latitude": null, "longitude": null, "status": "rejected", "reason": "image not related to category"}
+{"report_id": "1758908588848", "description": "park full with water entirely", "category": "Other", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758908589/civicconnect/images/zfhfxklqxbcimvmtzvmu.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Other", "image_category": "flooded park"}
+{"report_id": "1758911266239", "description": "streetlight is not working in our locality", "category": "Street Lighting", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758911266/civicconnect/images/hul0fmnlmbrrpytno61f.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Street Lighting", "image_category": "street lighting"}
+{"report_id": "1758911953881", "description": "park id totally filled with the water", "category": "Parks & Recreation", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1758911954/civicconnect/images/egz3lwnf1kstbehsrrrg.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Parks & Recreation", "image_category": "flooded park"}
+{"report_id": "1759913465036", "description": "trash is not collected fuck", "category": "Garbage & Sanitation", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1759913464/civicconnect/images/lpwi3zyb1adhqvgmknt7.jpg", "latitude": null, "longitude": null, "status": "rejected", "reason": "Abusive language detected"}
+{"report_id": "1759913483383", "description": "trash is not collected in this location", "category": "Garbage & Sanitation", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1759913482/civicconnect/images/psb9jgo0jtyzh71b2esf.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Garbage & Sanitation", "image_category": "overflowing dustbin"}
+{"report_id": "1765894932155", "description": "bloody rascal", "category": "Garbage & Sanitation", "user_id": "68d4172602906a425b987332", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1765894930/civicconnect/images/ffid8y15akucvy3z7ard.png", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Garbage & Sanitation", "image_category": "dustbin"}
+{"report_id": "1765905306799", "description": "Bloody is bloody blody", "category": "Water & Drainage", "user_id": "692029e2aeec8c1dd08cd88a", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1765905305/civicconnect/images/q5146lqbpj0qdzt7hgvi.png", "latitude": null, "longitude": null, "status": "rejected", "reason": "image not related to category"}
+{"report_id": "1765905360447", "description": "Bloody is bloody blody", "category": "Water & Drainage", "user_id": "692029e2aeec8c1dd08cd88a", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1765905359/civicconnect/images/aqb85fum8kgzcrnbkj7e.jpg", "latitude": null, "longitude": null, "status": "rejected", "reason": "image not related to category"}
+{"report_id": "1765905377190", "description": "Bloody is bloody blody", "category": "Garbage & Sanitation", "user_id": "692029e2aeec8c1dd08cd88a", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1765905376/civicconnect/images/h7g4zczzljwrvn59wqr3.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Garbage & Sanitation", "image_category": "bin"}
+{"report_id": "1765905946346", "description": "Bloody rascal Bloody rascal", "category": "Garbage & Sanitation", "user_id": "692029e2aeec8c1dd08cd88a", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1765905945/civicconnect/images/pifodkreri5jil7nukrb.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "normal", "text_category": "Garbage & Sanitation", "image_category": "bin"}
+{"report_id": "1765906207021", "description": "bloody stupid bloody stupid", "category": "Garbage & Sanitation", "user_id": "692029e2aeec8c1dd08cd88a", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1765906206/civicconnect/images/mwvyijxrgnybyxv7yiqr.jpg", "latitude": null, "longitude": null, "status": "rejected", "reason": "Abusive language detected"}
+{"report_id": "1765906298424", "description": "fire acciedent fire acciedent", "category": "Public Safety", "user_id": "692029e2aeec8c1dd08cd88a", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1765906297/civicconnect/images/ufzollwqqjwwnha7xgfc.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "urgent", "text_category": "Public Safety", "image_category": "building collapse"}
+{"report_id": "1765942883964", "description": "fire occured nearby in this lication", "category": "Public Safety", "user_id": "692029e2aeec8c1dd08cd88a", "image_url": "https://res.cloudinary.com/dhidj8n1c/image/upload/v1765942883/civicconnect/images/nkatgbrti1ydirokmrgt.jpg", "latitude": null, "longitude": null, "status": "accepted", "priority": "urgent", "text_category": "Public Safety", "image_category": "building collapse"}

ml-backend-with-image/debug_profanity.py

@@ -0,0 +1,114 @@
+#!/usr/bin/env python3
+"""
+Debug script to test profanity detection
+"""
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+# Test import
+print("=" * 60)
+print("Testing Profanity-Check Import")
+print("=" * 60)
+
+try:
+    from profanity_check import predict
+    print("✅ Successfully imported profanity_check.predict")
+    
+    # Test with clean text
+    print("\nTesting with clean text: 'This is a clean sentence'")
+    result_clean = predict(["This is a clean sentence"])
+    print(f"Result: {result_clean}")
+    print(f"Type: {type(result_clean)}")
+    if hasattr(result_clean, '__getitem__'):
+        print(f"Value: {result_clean[0]}")
+        print(f"Is profane: {bool(int(result_clean[0]) == 1)}")
+    
+    # Test with profane text
+    print("\nTesting with profane text: 'This is a fucking pothole'")
+    result_profane = predict(["This is a fucking pothole"])
+    print(f"Result: {result_profane}")
+    print(f"Type: {type(result_profane)}")
+    if hasattr(result_profane, '__getitem__'):
+        print(f"Value: {result_profane[0]}")
+        print(f"Is profane: {bool(int(result_profane[0]) == 1)}")
+    
+    # Test with more profane words
+    test_cases = [
+        "fuck",
+        "shit",
+        "bitch",
+        "damn",
+        "hell",
+        "This is a clean description of a pothole",
+        "fuck this pothole",
+        "what the hell is wrong",
+    ]
+    
+    print("\n" + "=" * 60)
+    print("Testing Multiple Cases")
+    print("=" * 60)
+    
+    for test_text in test_cases:
+        result = predict([test_text])
+        is_profane = bool(int(result[0]) == 1) if hasattr(result, '__getitem__') else bool(int(result) == 1)
+        status = "PROFANE" if is_profane else "CLEAN"
+        print(f"{status:8} | '{test_text}'")
+    
+except ImportError as e:
+    print(f"❌ Failed to import profanity_check: {e}")
+except Exception as e:
+    print(f"❌ Error: {e}")
+    import traceback
+    traceback.print_exc()
+
+# Test the actual pipeline
+print("\n" + "=" * 60)
+print("Testing Pipeline Function")
+print("=" * 60)
+
+try:
+    from app.pipeline import classify_report, PROFANITY_AVAILABLE, _profanity_predict
+    
+    print(f"PROFANITY_AVAILABLE: {PROFANITY_AVAILABLE}")
+    print(f"_profanity_predict is None: {_profanity_predict is None}")
+    
+    # Test cases
+    test_reports = [
+        {
+            "report_id": "test1",
+            "description": "This is a fucking pothole",
+            "category": "Road & Traffic",
+            "user_id": "test"
+        },
+        {
+            "report_id": "test2",
+            "description": "There is a large pothole on Main Street",
+            "category": "Road & Traffic",
+            "user_id": "test"
+        },
+        {
+            "report_id": "test3",
+            "description": "What the hell is wrong with this road",
+            "category": "Road & Traffic",
+            "user_id": "test"
+        },
+    ]
+    
+    for report in test_reports:
+        result = classify_report(report)
+        status = result.get("status")
+        reason = result.get("reason", "")
+        print(f"\nDescription: '{report['description']}'")
+        print(f"Status: {status}")
+        if reason:
+            print(f"Reason: {reason}")
+        if status == "rejected" and "abusive" in reason.lower():
+            print("✅ Profanity detected correctly!")
+        elif status == "accepted" and any(word in report['description'].lower() for word in ["fuck", "hell"]):
+            print("❌ Profanity NOT detected!")
+            
+except Exception as e:
+    print(f"❌ Error testing pipeline: {e}")
+    import traceback
+    traceback.print_exc()

ml-backend-with-image/requirements.txt

@@ -0,0 +1,9 @@
+fastapi
+uvicorn[standard]
+pydantic
+transformers
+torch
+pillow
+requests
+imagehash
+profanity-check

ml-backend-with-image/test_classification.py

@@ -0,0 +1,93 @@
+#!/usr/bin/env python3
+"""
+Test script to verify the improved image classification
+"""
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from app.pipeline import classify_report
+
+def test_park_water_classification():
+    """Test park filled with water classification"""
+    print("Testing park filled with water classification...")
+    
+    report = {
+        "report_id": "test_park_001",
+        "description": "park is filled with water in this location",
+        "category": "Parks & Recreation",
+        "user_id": "test_user",
+        "image_url": "https://example.com/park_water.jpg"
+    }
+    
+    result = classify_report(report)
+    print(f"Result: {result}")
+    
+    if result["status"] == "accepted":
+        print("✅ Park water classification PASSED")
+    else:
+        print(f"❌ Park water classification FAILED: {result.get('reason', 'Unknown reason')}")
+    
+    return result["status"] == "accepted"
+
+def test_streetlight_classification():
+    """Test streetlight classification"""
+    print("\nTesting streetlight classification...")
+    
+    report = {
+        "report_id": "test_light_001",
+        "description": "street light is not working in this location",
+        "category": "Street Lighting",
+        "user_id": "test_user",
+        "image_url": "https://example.com/streetlight.jpg"
+    }
+    
+    result = classify_report(report)
+    print(f"Result: {result}")
+    
+    if result["status"] == "accepted":
+        print("✅ Streetlight classification PASSED")
+    else:
+        print(f"❌ Streetlight classification FAILED: {result.get('reason', 'Unknown reason')}")
+    
+    return result["status"] == "accepted"
+
+def test_without_image():
+    """Test classification without image (should rely on text)"""
+    print("\nTesting without image (text-only classification)...")
+    
+    report = {
+        "report_id": "test_text_001",
+        "description": "park is filled with water in this location",
+        "category": "Parks & Recreation",
+        "user_id": "test_user",
+        "image_url": None
+    }
+    
+    result = classify_report(report)
+    print(f"Result: {result}")
+    
+    if result["status"] == "accepted":
+        print("✅ Text-only classification PASSED")
+    else:
+        print(f"❌ Text-only classification FAILED: {result.get('reason', 'Unknown reason')}")
+    
+    return result["status"] == "accepted"
+
+if __name__ == "__main__":
+    print("🧪 Testing improved image classification...")
+    print("=" * 50)
+    
+    # Run tests
+    park_test = test_park_water_classification()
+    light_test = test_streetlight_classification()
+    text_test = test_without_image()
+    
+    print("\n" + "=" * 50)
+    print("📊 Test Results:")
+    print(f"Park water classification: {'✅ PASSED' if park_test else '❌ FAILED'}")
+    print(f"Streetlight classification: {'✅ PASSED' if light_test else '❌ FAILED'}")
+    print(f"Text-only classification: {'✅ PASSED' if text_test else '❌ FAILED'}")
+    
+    all_passed = park_test and light_test and text_test
+    print(f"\nOverall: {'✅ ALL TESTS PASSED' if all_passed else '❌ SOME TESTS FAILED'}")

ml-backend-with-image/test_profanity.py

@@ -0,0 +1,84 @@
+#!/usr/bin/env python3
+"""
+Test script to verify profanity detection is working correctly
+"""
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from app.pipeline import classify_report
+
+def test_profanity_detection():
+    """Test profanity detection with various abusive and clean texts"""
+    print("🧪 Testing Profanity Detection")
+    print("=" * 60)
+    
+    # Test cases: (description, should_be_rejected, test_name)
+    test_cases = [
+        # Abusive language - should be rejected
+        ("This is a fucking pothole that needs fixing", True, "Explicit profanity"),
+        ("What the hell is wrong with this road", True, "Mild profanity"),
+        ("This shit needs to be fixed immediately", True, "Strong profanity"),
+        ("You're an idiot for not fixing this", True, "Insult"),
+        ("This is crap and needs attention", True, "Mild profanity"),
+        
+        # Clean language - should be accepted
+        ("There is a large pothole on Main Street that needs repair", False, "Clean description"),
+        ("The street light is not working properly", False, "Clean technical issue"),
+        ("Water is leaking from the pipe", False, "Clean water issue"),
+        ("Garbage is overflowing from the bin", False, "Clean sanitation issue"),
+        ("Park maintenance is needed", False, "Clean maintenance request"),
+        
+        # Edge cases
+        ("FUCK this pothole", True, "All caps profanity"),
+        ("fuck this pothole", True, "Lowercase profanity"),
+        ("F*ck this pothole", False, "Censored profanity (might pass)"),
+        ("This is a damn good road", True, "Profanity in positive context"),
+    ]
+    
+    passed = 0
+    failed = 0
+    
+    for desc, should_reject, test_name in test_cases:
+        report = {
+            "report_id": f"test_{test_name.replace(' ', '_').lower()}",
+            "description": desc,
+            "category": "Road & Traffic",
+            "user_id": "test_user",
+            "image_url": None,
+            "latitude": 12.9,
+            "longitude": 77.6
+        }
+        
+        result = classify_report(report)
+        was_rejected = result.get("status") == "rejected"
+        reason = result.get("reason", "")
+        
+        # Check if result matches expectation
+        if was_rejected == should_reject:
+            status = "✅ PASS"
+            passed += 1
+        else:
+            status = "❌ FAIL"
+            failed += 1
+        
+        print(f"\n{status} - {test_name}")
+        print(f"  Description: \"{desc}\"")
+        print(f"  Expected: {'REJECTED' if should_reject else 'ACCEPTED'}")
+        print(f"  Got: {'REJECTED' if was_rejected else 'ACCEPTED'}")
+        if was_rejected:
+            print(f"  Reason: {reason}")
+    
+    print("\n" + "=" * 60)
+    print(f"📊 Test Results: {passed} passed, {failed} failed out of {len(test_cases)} tests")
+    
+    if failed == 0:
+        print("✅ All profanity detection tests PASSED!")
+    else:
+        print(f"❌ {failed} test(s) FAILED - profanity detection needs improvement")
+    
+    return failed == 0
+
+if __name__ == "__main__":
+    success = test_profanity_detection()
+    sys.exit(0 if success else 1)