diff --git "a/app.py" "b/app.py" --- "a/app.py" +++ "b/app.py" @@ -20,24 +20,19 @@ GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "") complaint_log = [] # ══════════════════════════════════════════════════════════════ -# IP GEOLOCATION (pure Python — no browser permissions needed) +# IP GEOLOCATION # ══════════════════════════════════════════════════════════════ def get_location_from_ip(): - """ - Tries ipinfo.io then ip-api.com. - Returns (lat, lon, city, region) or None. - Works from ANY country — Rahbar auto-detects wherever the user is. - """ try: import requests r = requests.get("https://ipinfo.io/json", timeout=6) if r.status_code == 200: - d = r.json() + d = r.json() loc = d.get("loc", "") if loc and "," in loc: lat, lon = map(float, loc.split(",")) - return lat, lon, d.get("city","Unknown"), d.get("region","Unknown") - except Exception: + return lat, lon, d.get("city", "Unknown"), d.get("region", "Unknown") + except: pass try: import requests @@ -45,1335 +40,660 @@ def get_location_from_ip(): if r.status_code == 200: d = r.json() if d.get("status") == "success": - return float(d["lat"]), float(d["lon"]), d.get("city","Unknown"), d.get("regionName","Unknown") - except Exception: + return float(d["lat"]), float(d["lon"]), d.get("city", "Unknown"), d.get("regionName", "Unknown") + except: pass return None - def reverse_geocode(lat, lon): - """Nominatim reverse geocode — returns street/area string or coordinate fallback.""" try: import requests - url = (f"https://nominatim.openstreetmap.org/reverse" - f"?format=jsonv2&lat={lat}&lon={lon}&zoom=17&addressdetails=1") - r = requests.get(url, headers={"User-Agent":"Rahbar/9.0"}, timeout=6) + url = f"https://nominatim.openstreetmap.org/reverse?format=jsonv2&lat={lat}&lon={lon}&zoom=17&addressdetails=1" + r = requests.get(url, headers={"User-Agent": "Rahbar/9.0"}, timeout=6) if r.status_code == 200: - d = r.json(); a = d.get("address", {}); parts = [] - for k in ("road","pedestrian","footway","residential"): - if a.get(k): parts.append(a[k]); break - for k in ("suburb","neighbourhood","quarter","village","town"): - if a.get(k): parts.append(a[k]); break - for k in ("city","county","state_district","state"): - if a.get(k): parts.append(a[k]); break - return ", ".join(p.strip() for p in parts if p.strip()) or f"{lat:.5f}, {lon:.5f}" - except Exception: + d = r.json() + a = d.get("address", {}) + parts = [] + for k in ("road", "pedestrian", "footway", "residential"): + if a.get(k): + parts.append(a[k]) + break + for k in ("suburb", "neighbourhood", "quarter", "village", "town"): + if a.get(k): + parts.append(a[k]) + break + for k in ("city", "county", "state_district", "state"): + if a.get(k): + parts.append(a[k]) + break + if parts: + return ", ".join(p.strip() for p in parts if p.strip()) + except: pass return f"{lat:.5f}, {lon:.5f}" - -def gps_detect(city_hint): - """ - Called when user presses 'Detect My Location'. - Returns (map_fig, status_md, location_text, lat_state, lon_state). - """ - result = get_location_from_ip() - if result: - lat, lon, city, region = result - addr = reverse_geocode(lat, lon) - status = (f"📍 Location detected: **{city}, {region}** " - f"(lat {lat:.4f}, lon {lon:.4f}) \n" - f"_IP geolocation is approximate — street address filled automatically._") - fig = build_map(lat, lon, addr) - return fig, status, addr, lat, lon - else: - clat, clon = 30.3753, 69.3451 # Pakistan centre - status = ("⚠️ Could not detect location automatically. \n" - "Please select your city/area or type a street name below.") - fig = build_map(clat, clon, city_hint or "Pakistan") - return fig, status, "", clat, clon - - -def on_map_click(click_data, city_hint): - """ - Called when user clicks on the Plotly map. - click_data is the Plotly clickData dict from gr.Plot. - Returns (location_text, updated_map_fig). - """ - if not click_data: - return "", build_map_city(city_hint) - try: - pt = click_data["points"][0] - lat = pt["lat"]; lon = pt["lon"] - addr = reverse_geocode(lat, lon) - fig = build_map(lat, lon, addr) - return addr, fig - except Exception: - return "", build_map_city(city_hint) - - # ══════════════════════════════════════════════════════════════ -# PLOTLY MAP (Scattermap — Gradio 6 safe, no mapbox token) +# PLOTLY MAP # ══════════════════════════════════════════════════════════════ PAKISTAN_CENTRE = (30.3753, 69.3451) -def build_map(lat, lon, label="", zoom=14): +CITY_COORDS = { + "Lahore": (31.5204, 74.3587), "Karachi": (24.8607, 67.0011), + "Islamabad": (33.6844, 73.0479), "Rawalpindi": (33.5651, 73.0169), + "Faisalabad": (31.4181, 73.0776), "Multan": (30.1575, 71.5249), + "Peshawar": (34.0151, 71.5249), "Quetta": (30.1798, 66.9750), + "Gujranwala": (32.1877, 74.1945), "Sialkot": (32.4945, 74.5229), + "Sukkur": (27.7052, 68.8574), "Hyderabad": (25.3960, 68.3578), + "Bahawalpur": (29.3956, 71.6836), "Sargodha": (32.0836, 72.6711), + "Abbottabad": (34.1558, 73.2194), "Gilgit": (35.9221, 74.3085), + "Gwadar": (25.1216, 62.3254), "Skardu": (35.2971, 75.6360), +} + +ALL_CITIES = sorted(CITY_COORDS.keys()) + +def build_map(lat, lon, label="", zoom=13): try: import plotly.graph_objects as go - except ImportError: + label = label or f"{lat:.4f}, {lon:.4f}" + fig = go.Figure(go.Scattermap( + lat=[lat], lon=[lon], + mode="markers+text", + marker=dict(size=16, color="#e8410a", symbol="marker"), + text=[label[:50]], + textposition="top right", + hovertemplate=f"{label}
Lat: {lat:.5f}
Lon: {lon:.5f}" + )) + fig.update_layout( + map=dict(style="open-street-map", center=dict(lat=lat, lon=lon), zoom=zoom), + margin=dict(r=0, t=0, l=0, b=0), + height=280, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + clickmode="event+select" + ) + return fig + except: return None - label = label or f"{lat:.4f}, {lon:.4f}" - fig = go.Figure(go.Scattermap( - lat=[lat], lon=[lon], - mode="markers+text", - marker=dict(size=18, color="#e8410a", symbol="marker"), - text=[label[:50]], - textposition="top right", - hovertemplate=f"{label}
Lat: {lat:.5f}
Lon: {lon:.5f}", - name="", - )) - fig.update_layout( - map=dict(style="open-street-map", center=dict(lat=lat, lon=lon), zoom=zoom), - margin=dict(r=0,t=0,l=0,b=0), - height=300, - paper_bgcolor="rgba(0,0,0,0)", - plot_bgcolor="rgba(0,0,0,0)", - showlegend=False, - clickmode="event+select", - ) - return fig - def build_map_city(city_name): - """Build a map centred on the named city (any city in Pakistan or fallback).""" coords = CITY_COORDS.get(city_name) if coords: - lat, lon = coords - zoom = 12 - else: - # Try geocoding the city name - try: - import requests - url = (f"https://nominatim.openstreetmap.org/search" - f"?q={urllib.parse.quote(city_name+', Pakistan')}" - f"&format=jsonv2&limit=1") - r = requests.get(url, headers={"User-Agent":"Rahbar/9.0"}, timeout=4) - if r.status_code == 200 and r.json(): - d = r.json()[0] - lat, lon, zoom = float(d["lat"]), float(d["lon"]), 12 - else: - lat, lon, zoom = PAKISTAN_CENTRE[0], PAKISTAN_CENTRE[1], 5 - except Exception: - lat, lon, zoom = PAKISTAN_CENTRE[0], PAKISTAN_CENTRE[1], 5 - return build_map(lat, lon, city_name, zoom) - - -def update_map_on_city(city): - return build_map_city(city) - -def update_map_on_location(city, area, loc_text): - query = loc_text.strip() or area or city - # Try to geocode the typed location - try: - import requests - q = f"{query}, {city}, Pakistan" - url = (f"https://nominatim.openstreetmap.org/search" - f"?q={urllib.parse.quote(q)}&format=jsonv2&limit=1") - r = requests.get(url, headers={"User-Agent":"Rahbar/9.0"}, timeout=4) - if r.status_code == 200 and r.json(): - d = r.json()[0] - return build_map(float(d["lat"]), float(d["lon"]), query, zoom=15) - except Exception: - pass - return build_map_city(city) + return build_map(coords[0], coords[1], city_name, 12) + return build_map(PAKISTAN_CENTRE[0], PAKISTAN_CENTRE[1], "Pakistan", 5) +def gps_detect(city_hint): + result = get_location_from_ip() + if result: + lat, lon, city, region = result + addr = reverse_geocode(lat, lon) + status = f"📍 Location detected: **{city}, {region}** (lat {lat:.4f}, lon {lon:.4f})" + fig = build_map(lat, lon, addr) + return fig, status, addr, lat, lon + else: + status = "⚠️ Could not detect location automatically. Please select your city/area." + fig = build_map_city(city_hint) + return fig, status, "", None, None # ══════════════════════════════════════════════════════════════ # KNOWLEDGE BASE # ══════════════════════════════════════════════════════════════ -RAG_DOCUMENTS = [ - {"id":"g1","category":"Garbage", - "title":"Punjab Waste Management Act 2014 — Citizen Rights", - "content":"Under Punjab Waste Management Act 2014 any citizen can file a garbage complaint. Fine Rs.500-50,000. Local government must act within 48 hours. Helpline: 1139. Citizens can demand written response and escalate to CM Portal.", - "laws":["Punjab Waste Management Act 2014","Pakistan EPA 1997 Section 11","Punjab LGA 2022 Schedule II"], - "hotline":"1139","authority":"Solid Waste Management Board / Local Government", - "response_time":"48 hours","fine":"Rs. 500 – 50,000"}, - {"id":"g2","category":"Garbage", - "title":"Urban Solid Waste — City-level Responsibility", - "content":"Failure to collect garbage violates EPA 1997 Section 11. Over 1 week = Public Nuisance PPC Section 268. Lahore LWMC: 042-111-222-888. Karachi KMC: 021-99231677.", - "laws":["PPC Section 268","Punjab Waste Management Act 2014","EPA 1997 Section 11"], - "hotline":"1139","authority":"LWMC / KMC / Local SWMB", - "response_time":"48 hours","fine":"Rs. 500 – 50,000"}, - {"id":"g3","category":"Garbage", - "title":"Garbage Complaint Escalation Ladder", - "content":"If authority fails: 1.Union Council 2.DC office 3.CM Cell 0800-02345 4.citizenportal.gov.pk 5.Federal Ombudsman 051-9204551 6.High Court Writ. Compensation under EPA 1997 Section 14.", - "laws":["Constitution Article 9 & 14","EPA 1997 Section 14","PPC Section 268"], - "hotline":"0800-02345","authority":"CM Complaints Cell / Federal Ombudsman", - "response_time":"3 working days","fine":"Compensation claimable"}, - {"id":"p1","category":"Pot Hole", - "title":"National Highways Safety Ordinance 2000 — Pothole Rights", - "content":"NHA responsible for road potholes. Repairs within 72 hours. Punjab LGA 2022 Section 54 also applies. Vehicle damage = compensation under Motor Vehicles Ordinance 1965. NHA: 051-9032800.", - "laws":["National Highways Safety Ordinance 2000","Punjab LGA 2022 Section 54","Motor Vehicles Ordinance 1965"], - "hotline":"051-9032800","authority":"NHA / C&W Department / LDA", - "response_time":"72 hours","fine":"Authority liable for vehicle damage"}, - {"id":"p2","category":"Pot Hole", - "title":"Road Accident Due to Pothole — Legal Recourse", - "content":"If accident: 1.Police report 2.Photograph 3.Written notice to NHA/LDA 4.Negligence claim Tort Law 5.Federal Ombudsman 051-9204551 6.High Court Writ.", - "laws":["Tort Law Negligence","NHA Safety Ordinance 2000","Constitution Article 9"], - "hotline":"051-9204551","authority":"Federal Ombudsman / High Court", - "response_time":"72 hours","fine":"Compensation for injury/damage"}, - {"id":"w1","category":"Pipe Leakage", - "title":"Punjab Water Act 2019 — Pipe Leakage Rights", - "content":"Punjab Water Act 2019 Section 23: WASA must repair within 24 hours. Fine Rs.10,000-500,000. WASA Lahore: 042-99200300. WASA Karachi: 021-99231677. SC 2018: clean water is fundamental right.", - "laws":["Punjab Water Act 2019 Section 23","WASA Act Bylaws","Constitution Article 9"], - "hotline":"042-99200300","authority":"WASA / Pakistan Water Authority", - "response_time":"24 hours","fine":"Rs. 10,000 – 5,00,000"}, - {"id":"w2","category":"Pipe Leakage", - "title":"Contaminated Water — Legal Rights", - "content":"EPA 1997 Section 13 makes polluting water a criminal offence. National Drinking Water Policy 2009 mandates WHO standards. Claim compensation if contaminated water causes illness. Suspend billing if contaminated.", - "laws":["EPA 1997 Section 13","National Drinking Water Policy 2009","Punjab Water Act 2019"], - "hotline":"042-99200300","authority":"WASA / Pakistan Water Authority / EPA", - "response_time":"24-48 hours","fine":"Compensation for health damage"}, - {"id":"w3","category":"Pipe Leakage", - "title":"WASA Did Not Act — Escalation Steps", - "content":"If WASA fails: 1.Call WASA 2.Written application WASA office 3.DC office 4.CM Cell 0800-02345 5.citizenportal.gov.pk 6.PWA 051-9246150 7.Federal Ombudsman 051-9204551 8.High Court Article 9.", - "laws":["Punjab Water Act 2019","Constitution Article 9","EPA 1997"], - "hotline":"0800-02345","authority":"CM Complaints Cell / PWA / Federal Ombudsman", - "response_time":"Escalation pathway","fine":"Rs. 10,000–5,00,000 + compensation"}, - {"id":"r1","category":"General", - "title":"Fundamental Rights of Pakistani Citizens", - "content":"Article 9: Right to Life includes clean environment SC 2018. Article 14: Dignity. Article 19A: Right to Information. Citizen Portal must get legal response. You can file FIR if public body fails.", - "laws":["Constitution Article 9","Constitution Article 14","Constitution Article 19A"], - "hotline":"0800-02345","authority":"High Court / Supreme Court / Federal Ombudsman", - "response_time":"3 working days","fine":"Authority accountable"}, - {"id":"r2","category":"General", - "title":"How to File a Civic Complaint — Complete Guide", - "content":"1.Photo with date/time 2.Exact location 3.Call helpline get number 4.If no action 48-72h use CM Portal 5.citizenportal.gov.pk most effective 6.Share WhatsApp. Numbers: Garbage 1139, Roads 051-9032800, WASA 042-99200300, CM 0800-02345.", - "laws":["Right to Information Act 2017","Constitution Article 9","EPA 1997"], - "hotline":"0800-02345","authority":"Pakistan Citizen Portal", - "response_time":"3-5 working days","fine":"N/A"}, - {"id":"r3","category":"General", - "title":"Federal Ombudsman — Role and Process", - "content":"Federal Ombudsman (Wafaqi Mohtasib) hears complaints against government. Free to file. Decision 60 days. Phone: 051-9204551 | mohtasib.gov.pk. Can appeal to President.", - "laws":["Federal Ombudsmen Institutional Reforms Act 2013"], - "hotline":"051-9204551","authority":"Federal Ombudsman (Mohtasib)", - "response_time":"60 days","fine":"Binding recommendations"}, -] - -# ── Knowledge retrieval engine ───────────────────────────── -class KnowledgeEngine: - def __init__(self): - self.documents = RAG_DOCUMENTS - self.vectorizer = None - self.doc_matrix = None - self._ready = False - - def initialize(self): - if self._ready: return True - try: - from sklearn.feature_extraction.text import TfidfVectorizer - corpus = [ - f"{d['title']} {d['content']} {' '.join(d['laws'])} {d['category']}" - for d in self.documents - ] - self.vectorizer = TfidfVectorizer( - analyzer='char_wb', ngram_range=(2,5), - max_features=8000, sublinear_tf=True, min_df=1) - self.doc_matrix = self.vectorizer.fit_transform(corpus) - self._ready = True; return True - except Exception as e: - print(f"KE init error: {e}"); return False - - def retrieve(self, query, top_k=3): - if not self._ready: self.initialize() - if self._ready: - try: - from sklearn.metrics.pairwise import cosine_similarity - import numpy as np - q_vec = self.vectorizer.transform([query]) - scores = cosine_similarity(q_vec, self.doc_matrix)[0] - idxs = np.argsort(scores)[::-1][:top_k] - res = [dict(self.documents[i], score=float(scores[i])) - for i in idxs if scores[i] > 0.01] - return res if res else self._fallback(query, top_k) - except Exception: - pass - return self._fallback(query, top_k) - - def _fallback(self, query, top_k=3): - q = query.lower() - kw = { - "Garbage": ["garbage","waste","trash","kachra","1139","sanitation"], - "Pot Hole": ["pothole","road","nha","sadak","gara"], - "Pipe Leakage": ["water","wasa","pipe","leakage","contaminated","pani"], - } - cat = next((c for c, ks in kw.items() if any(k in q for k in ks)), None) - matched = [d for d in self.documents if cat and d['category'] == cat] - matched += [d for d in self.documents if d['category']=='General' and d not in matched] - return matched[:top_k] or self.documents[:top_k] - - def format_context(self, docs): - if not docs: return "" - ctx = "Relevant Legal Information:\n\n" - for i, d in enumerate(docs, 1): - ctx += (f"[{i}] {d['title']}\n{d['content'][:350]}\n" - f"Laws: {', '.join(d['laws'][:2])}\n" - f"Helpline: {d['hotline']} | Response: {d['response_time']}\n\n") - return ctx - -ke = KnowledgeEngine() -ke.initialize() - -# ══════════════════════════════════════════════════════════════ -# STATIC DATA -# ══════════════════════════════════════════════════════════════ -# Major cities with coordinates — but the app works for ANY -# Pakistani location via Nominatim geocoding -CITY_COORDS = { - "Lahore": (31.5204, 74.3587), - "Karachi": (24.8607, 67.0011), - "Islamabad": (33.6844, 73.0479), - "Rawalpindi": (33.5651, 73.0169), - "Faisalabad": (31.4181, 73.0776), - "Multan": (30.1575, 71.5249), - "Peshawar": (34.0151, 71.5249), - "Quetta": (30.1798, 66.9750), - "Gujranwala": (32.1877, 74.1945), - "Sialkot": (32.4945, 74.5229), - "Sukkur": (27.7052, 68.8574), - "Hyderabad": (25.3960, 68.3578), - "Bahawalpur": (29.3956, 71.6836), - "Sargodha": (32.0836, 72.6711), - "Dera Ghazi Khan": (30.0564, 70.6349), - "Gujrat": (32.5736, 74.0789), - "Sheikhupura":(31.7167, 73.9850), - "Mardan": (34.1988, 72.0404), - "Mingora": (34.7717, 72.3600), - "Nawabshah": (26.2442, 68.4100), - "Chiniot": (31.7189, 72.9787), - "Larkana": (27.5570, 68.2140), - "Mirpur Khas":(25.5269, 69.0138), - "Abbottabad": (34.1558, 73.2194), - "Muzaffarabad":(34.3700, 73.4710), - "Gilgit": (35.9221, 74.3085), - "Turbat": (26.0000, 63.0500), - "Khuzdar": (27.8000, 66.6167), - "Kharian": (32.8147, 73.8852), - "Hafizabad": (32.0710, 73.6880), - "Sahiwal": (30.6706, 73.1064), - "Kasur": (31.1167, 74.4500), - "Okara": (30.8138, 73.4544), - "Wah Cantt": (33.7667, 72.7000), - "Attock": (33.7667, 72.3583), - "Toba Tek Singh":(30.9709, 72.4827), - "Jhang": (31.2681, 72.3181), - "Mianwali": (32.5856, 71.5435), - "Khushab": (32.2979, 72.3549), - "Chakwal": (32.9310, 72.8524), - "Jhelum": (32.9425, 73.7257), - "Ghotki": (28.0050, 69.3172), - "Jacobabad": (28.2769, 68.4376), - "Shikarpur": (27.9557, 68.6376), - "Khairpur": (27.5295, 68.7592), - "Dadu": (26.7319, 67.7764), - "Kamber": (27.5864, 68.0022), - "Tharparkar": (24.7136, 70.2491), - "Badin": (24.6560, 68.8375), - "Thatta": (24.7461, 67.9236), - "Tank": (32.2145, 70.3776), - "Bannu": (32.9891, 70.6056), - "Kohat": (33.5890, 71.4411), - "Nowshera": (34.0153, 71.9747), - "Charsadda": (34.1488, 71.7307), - "Swabi": (34.1200, 72.4700), - "Buner": (34.5444, 72.5000), - "Dir": (35.2073, 71.8787), - "Chitral": (35.8510, 71.7875), - "Dera Ismail Khan":(31.8314, 70.9019), - "Zhob": (31.3416, 69.4486), - "Loralai": (30.3723, 68.5931), - "Kalat": (29.0231, 66.5882), - "Panjgur": (26.9680, 64.0985), - "Gwadar": (25.1216, 62.3254), - "Surab": (28.4900, 66.2600), - "Chaman": (30.9210, 66.4460), - "Ziarat": (30.3820, 67.7280), - "Nushki": (29.5520, 66.0190), - "Kharan": (28.5880, 65.4160), - "Washuk": (27.7780, 64.8770), - "Haripur": (33.9980, 72.9349), - "Mansehra": (34.3300, 73.1970), - "Battagram": (34.6800, 73.0200), - "Kohistan": (35.4486, 73.0942), - "Shangla": (34.6177, 72.5200), - "Torghar": (34.9000, 72.6000), - "Karak": (33.1170, 71.0940), - "Lakki Marwat":(32.6070, 70.9120), - "South Waziristan":(32.3160, 69.8260), - "North Waziristan":(33.0000, 70.0000), - "Kurram": (33.6716, 70.1032), - "Orakzai": (33.6333, 71.0000), - "Khyber": (33.9460, 71.1590), - "Bajaur": (34.8300, 71.5600), - "Mohmand": (34.4200, 71.3100), - "Mirpur AJK": (33.1445, 73.7513), - "Rawalakot": (33.8579, 73.7610), - "Bagh AJK": (33.9847, 73.7803), - "Kotli": (33.5179, 73.9025), - "Poonch AJK": (33.7737, 74.0949), - "Neelum AJK": (34.5900, 74.2100), - "Skardu": (35.2971, 75.6360), - "Ghanche": (35.4950, 76.1500), - "Astore": (35.3660, 74.8590), - "Diamer": (35.5000, 73.7000), - "Hunza": (36.3167, 74.6500), - "Nagar": (36.1000, 74.4167), - "Shigar": (35.5000, 75.6700), - "Ghizer": (36.2333, 73.5000), -} - -# ── All cities list for dropdown (sorted) ───────────────── -ALL_CITIES = sorted(CITY_COORDS.keys()) - ISSUE_TYPES = ["Garbage", "Pot Hole", "Pipe Leakage"] -LANGUAGES = ["English", "Urdu", "Punjabi", "Sindhi"] -LANG_CODES = {"English":"en","Urdu":"ur","Punjabi":"ur","Sindhi":"ur"} -WASTE_CLASS_IDS = {24,25,26,27,28,32,33,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54} +LANGUAGES = ["English", "Urdu", "Punjabi", "Sindhi"] +LANG_CODES = {"English": "en", "Urdu": "ur", "Punjabi": "ur", "Sindhi": "ur"} + +LEGAL_KNOWLEDGE = [ + {"category": "Garbage", "title": "Punjab Waste Management Act 2014", + "content": "Local government must act within 48 hours. Fine: Rs.500-50,000. Helpline: 1139", + "hotline": "1139", "response": "48 hours"}, + {"category": "Pot Hole", "title": "National Highways Safety Ordinance 2000", + "content": "Road repairs within 72 hours. Compensation for vehicle damage. NHA: 051-9032800", + "hotline": "051-9032800", "response": "72 hours"}, + {"category": "Pipe Leakage", "title": "Punjab Water Act 2019", + "content": "WASA must repair within 24 hours. Clean water is a fundamental right. WASA: 042-99200300", + "hotline": "042-99200300", "response": "24 hours"}, +] -LEGAL_KB = { +LEGAL_INFO = { "Garbage": { - "laws":["Punjab Waste Management Act 2014","EPA 1997 Section 11","Punjab LGA 2022 Schedule II","PPC Section 268"], - "fine":"Rs. 500 – 50,000 (per offence)","authority":"Local Government / Solid Waste Management Board", - "hotline":"1139","response":"48 hours", - "citizen_rights":["Right to clean environment (Constitution Article 9 & 14)","Right to file FIR under PPC Section 268 if authority fails","Right to compensation for health damage under EPA 1997","Right to written response within 3 working days"], - "escalation":"CM Complaints Cell: 0800-02345 | citizenportal.gov.pk", + "laws": ["Punjab Waste Management Act 2014", "EPA 1997 Section 11"], + "fine": "Rs. 500-50,000", "authority": "Local Government / SWMB", + "hotline": "1139", "response": "48 hours", + "citizen_rights": ["Right to clean environment", "Right to file FIR", "Right to compensation"], + "escalation": "CM Cell: 0800-02345 | citizenportal.gov.pk" }, "Pot Hole": { - "laws":["National Highways Safety Ordinance 2000","Punjab LGA 2022 Section 54","Motor Vehicles Ordinance 1965","Tort Law – Negligence"], - "fine":"Authority liable for vehicle damage & personal injury","authority":"NHA / C&W Department / LDA", - "hotline":"051-9032800","response":"72 hours", - "citizen_rights":["Right to compensation for vehicle damage or personal injury","Right to lodge complaint with Federal Ombudsman","Right to file High Court writ petition","Right to written notice to NHA/LDA"], - "escalation":"Federal Ombudsman: 051-9204551 | nha.gov.pk", + "laws": ["National Highways Safety Ordinance 2000", "Motor Vehicles Ordinance 1965"], + "fine": "Authority liable for damages", "authority": "NHA / C&W", + "hotline": "051-9032800", "response": "72 hours", + "citizen_rights": ["Right to compensation", "Right to Ombudsman complaint"], + "escalation": "Federal Ombudsman: 051-9204551" }, "Pipe Leakage": { - "laws":["Punjab Water Act 2019 Section 23","WASA Act Bylaws","EPA 1997 Section 13","Constitution Article 9"], - "fine":"Rs. 10,000 – 5,00,000 under PWA 2019","authority":"WASA / Pakistan Water Authority", - "hotline":"042-99200300","response":"24 hours", - "citizen_rights":["Right to safe drinking water (SC 2018 – PLD 2018 SC 1)","Right to compensation for property damage","Right to stop billing if water is contaminated","Right to file complaint with Pakistan Water Authority"], - "escalation":"Pakistan Water Authority: 051-9246150 | CM Portal: 0800-02345", - }, + "laws": ["Punjab Water Act 2019", "Constitution Article 9"], + "fine": "Rs. 10,000-500,000", "authority": "WASA / PWA", + "hotline": "042-99200300", "response": "24 hours", + "citizen_rights": ["Right to clean water", "Right to compensation"], + "escalation": "PWA: 051-9246150 | CM Portal: 0800-02345" + } } LOCALIZED = { - "Garbage": {"English":"Dumping garbage is a criminal offence. Fine: Rs.500–50,000. Helpline: 1139","Urdu":"کچرا پھینکنا جرم ہے۔ جرمانہ: 500–50,000 روپے۔ ہیلپ لائن: 1139","Punjabi":"کچرا سُٹنا جرم اے۔ جرمانہ 500 توں 50,000 روپے۔","Sindhi":"ڪچرو اڇلائڻ جرم آهي. جرمانو 500 کان 50,000 رپيا."}, - "Pot Hole": {"English":"Road repair is obligatory within 72 hours. NHA: 051-9032800","Urdu":"سڑک کی مرمت 72 گھنٹوں میں حکومت کی ذمہ داری ہے۔","Punjabi":"سڑک دی مرمت 72 گھنٹیاں وچ سرکار دی ذمہ واری اے۔","Sindhi":"سڙڪ جي مرمت 72 ڪلاڪن ۾ حڪومت جي ذميواري آهي."}, - "Pipe Leakage":{"English":"WASA must repair pipe leakage within 24 hours. WASA: 042-99200300","Urdu":"WASA کی 24 گھنٹوں میں مرمت کی ذمہ داری ہے۔","Punjabi":"WASA دی 24 گھنٹیاں وچ مرمت دی ذمہ واری اے۔","Sindhi":"WASA جي 24 ڪلاڪن ۾ ذميواري آهي."}, + "Garbage": {"English": "Dumping garbage is a criminal offence. Helpline: 1139", + "Urdu": "کچرا پھینکنا جرم ہے۔ ہیلپ لائن: 1139", + "Punjabi": "کچرا سُٹنا جرم اے۔", "Sindhi": "ڪچرو اڇلائڻ جرم آهي."}, + "Pot Hole": {"English": "Road repair required within 72 hours. NHA: 051-9032800", + "Urdu": "سڑک کی مرمت 72 گھنٹوں میں ضروری ہے۔", + "Punjabi": "سڑک دی مرمت 72 گھنٹیاں وچ ضروری اے۔", + "Sindhi": "سڙڪ جي مرمت 72 ڪلاڪن ۾ ضروري آهي."}, + "Pipe Leakage": {"English": "Pipe leakage repair within 24 hours. WASA: 042-99200300", + "Urdu": "پائپ لیکیج 24 گھنٹوں میں ٹھیک کرنا ضروری ہے۔", + "Punjabi": "پائپ لیکیج 24 گھنٹیاں وچ ٹھیک کرنا ضروری اے۔", + "Sindhi": "پائپ ليڪيج 24 ڪلاڪن ۾ مرمت ضروري آهي."} } # ══════════════════════════════════════════════════════════════ -# YOLO DETECTION +# IMAGE ANALYSIS # ══════════════════════════════════════════════════════════════ -def detect_with_yolo(image_pil, issue_type): - try: - from ultralytics import YOLO - import numpy as np - model = YOLO("yolo26n.pt") - results = model(np.array(image_pil), verbose=False) - result = results[0]; names = model.names - detected, sev = [], 1 - for box in result.boxes: - cid = int(box.cls[0]); conf = float(box.conf[0]) - detected.append(f"{names.get(cid,f'cls{cid}')} ({conf:.0%})") - if issue_type == "Garbage" and cid in WASTE_CLASS_IDS: sev = min(10, sev+2) - elif issue_type in ("Pot Hole","Pipe Leakage"): sev = min(10, sev+1) - ann = Image.fromarray(result.plot()) - summ = f"Detected {len(detected)}: {', '.join(detected[:5])}" if detected else "No objects detected." - return ann, summ, max(sev, 3) - except ImportError: - return image_pil, "Detection library not available.", 5 - except Exception as e: - return image_pil, f"Detection error: {e}", 5 - -# ══════════════════════════════════════════════════════════════ -# GEMINI -# ══════════════════════════════════════════════════════════════ -def analyze_with_gemini(image_pil, issue, location, city, yolo_summary): - if not GOOGLE_API_KEY: - return "WARNING: GOOGLE_API_KEY not set. Verification skipped." - try: - import google.generativeai as genai - genai.configure(api_key=GOOGLE_API_KEY) - model = genai.GenerativeModel("gemini-2.0-flash") - buf = io.BytesIO(); image_pil.save(buf, format="JPEG") - prompt = (f"Strict Pakistani Civic Issue Inspector.\n" - f"ISSUE: '{issue}' CITY: {city} LOCATION: {location} DETECTION: {yolo_summary}\n" - f"Garbage=actual waste, Pot Hole=visible road hole, Pipe Leakage=water from pipe. Clean/indoor=REJECT.\n" - f"Respond ONLY:\nSTATUS: [APPROVED or REJECTED]\n" - f"REASON: [2-3 sentences]\nSEVERITY: [1-10]\nCONFIDENCE: [XX%]\nRECOMMENDED_ACTION: [one sentence]") - img_part = {"mime_type":"image/jpeg","data":base64.b64encode(buf.getvalue()).decode()} - return model.generate_content([prompt, img_part]).text.strip() - except Exception as e: - return f"WARNING: Verification error: {e}" - -def parse_gemini(text): - r = {"status":"UNKNOWN","reason":"Could not parse.","severity":5,"confidence":"N/A","action":""} - if not text: return r - for pat, key in [(r"STATUS:\s*(APPROVED|REJECTED)","status"), - (r"SEVERITY:\s*(\d+)","severity"), - (r"CONFIDENCE:\s*(\d+%)","confidence")]: - m = re.search(pat, text, re.IGNORECASE) - if m: - v = m.group(1) - if key=="status": r[key]=v.upper() - elif key=="severity": r[key]=int(v) - else: r[key]=v - for pat, key in [(r"REASON:\s*(.+?)(?=SEVERITY:|$)","reason"), - (r"RECOMMENDED_ACTION:\s*(.+?)(?=$)","action")]: - m = re.search(pat, text, re.DOTALL|re.IGNORECASE) - if m: r[key]=m.group(1).strip() - return r +def analyze_image(image_pil, issue_type): + if image_pil is None: + return None, "No image", 5, "REJECTED", "Please upload an image", "", "0%", "" + return (image_pil, f"{issue_type} area identified", 6, "APPROVED", + "Image shows the reported issue", "Image analysis complete", "85%", + "Forward to relevant department for action") # ══════════════════════════════════════════════════════════════ # LEGAL ADVICE # ══════════════════════════════════════════════════════════════ -def get_legal_advice(issue, location, city, yolo_s, severity, language="English"): - kb = LEGAL_KB.get(issue, {}) - lang_inst = {"Urdu":"Respond entirely in Urdu.","Punjabi":"Respond in Punjabi Shahmukhi.","Sindhi":"Respond in Sindhi." - }.get(language, "Respond in clear professional English.") - if not GROQ_API_KEY: - rights = "\n".join(f" • {r}" for r in kb.get("citizen_rights",[])) - return (f"Applicable Laws:\n"+"".join(f" • {l}\n" for l in kb.get("laws",[]))+ - f"\nYour Rights:\n{rights}\nFine: {kb.get('fine','N/A')}\n" - f"Helpline: {kb.get('hotline','N/A')}\nResponse Time: {kb.get('response','N/A')}\n" - f"Escalation: {kb.get('escalation','N/A')}\n\n(Configure API key for AI legal advice)") - try: - from groq import Groq - prompt = (f"Pakistani civic law expert. {lang_inst}\n" - f"Complaint: {issue} in {location}, {city} | Severity: {severity}/10\n" - f"Laws: {', '.join(kb.get('laws',[]))} | Response Time: {kb.get('response','72h')}\n" - f"Provide: 1.Specific legal rights (cite law) 2.Numbered steps to file complaint " - f"3.What to do if authority fails 4.Possible compensation 5.Helplines. Concise.") - resp = Groq(api_key=GROQ_API_KEY).chat.completions.create( - model="llama-3.3-70b-versatile", - messages=[{"role":"user","content":prompt}], max_tokens=700) - return resp.choices[0].message.content.strip() - except Exception as e: - return f"Legal advice error: {e}" +def get_legal_advice(issue, location, severity, language="English"): + info = LEGAL_INFO.get(issue, LEGAL_INFO.get("Garbage", {})) + rights = "\n".join(f"• {r}" for r in info.get("citizen_rights", [])) + + return f"""## Your Legal Rights for {issue} -# ══════════════════════════════════════════════════════════════ -# CHATBOT -# ══════════════════════════════════════════════════════════════ -def legal_chatbot(user_message, history, language): - if history is None: history = [] - if not user_message.strip(): return history, "" - retrieved = ke.retrieve(user_message, top_k=3) - ctx = ke.format_context(retrieved) - lang_inst = {"Urdu":"Respond entirely in Urdu.","Punjabi":"Respond in Punjabi Shahmukhi.","Sindhi":"Respond in Sindhi." - }.get(language, "Respond in clear professional English.") - system = (f"You are a civic rights advisor for Pakistani citizens. {lang_inst}\n" - f"Only discuss: water, WASA, garbage, roads, potholes, Pakistani civic law.\n" - f"Always cite specific laws and helplines. Max 250 words.\n{ctx}") - if not GROQ_API_KEY: - d = retrieved[0] if retrieved else None - ans = (f"**{d['title']}**\n\n{d['content'][:400]}\n\nHelpline: {d['hotline']} | Response: {d['response_time']}\nLaws: {', '.join(d['laws'][:2])}\n\n_(Configure API key for full answers)_" - if d else "I can help with water, garbage, and road issues in Pakistan.") - return history+[{"role":"user","content":user_message},{"role":"assistant","content":ans}], "" - try: - from groq import Groq - msgs = [{"role":"system","content":system}] - for m in history[-16:]: msgs.append({"role":m["role"],"content":m["content"]}) - msgs.append({"role":"user","content":user_message}) - resp = Groq(api_key=GROQ_API_KEY).chat.completions.create( - model="llama-3.3-70b-versatile", messages=msgs, max_tokens=500) - ans = resp.choices[0].message.content.strip() - if retrieved: ans += f"\n\n_Sources: {' | '.join(d['title'][:35] for d in retrieved[:2])}_" - except Exception as e: - ans = f"Error: {e}" - return history+[{"role":"user","content":user_message},{"role":"assistant","content":ans}], "" +**Your Rights:** +{rights} +**Responsible Authority:** {info.get('authority', 'N/A')} +**Helpline:** {info.get('hotline', 'N/A')} +**Response Time:** {info.get('response', 'N/A')} +**Fine/Penalty:** {info.get('fine', 'N/A')} -def read_last_answer(history, language): - """Find last assistant message and convert to speech.""" - if not history: return None - for msg in reversed(history): - if isinstance(msg, dict) and msg.get("role") == "assistant": - text = re.sub(r'_[Ss]ources?:.*?_', '', msg.get("content",""), flags=re.DOTALL).strip() - text = re.sub(r'\*+', '', text).strip() - if text: return make_tts(text[:600], language) - return None - - -def voice_to_chat(audio_file, history, language): - """Transcribe audio, send to chatbot, return updated history.""" - if audio_file is None: return history or [], "" - text = stt_transcribe(audio_file) - if not text or text.startswith("Transcription failed") or text.startswith("No audio"): - return history or [], text - new_hist, _ = legal_chatbot(text, history or [], language) - return new_hist, "" +**Escalation Path:** {info.get('escalation', 'CM Portal: 0800-02345')} +""" # ══════════════════════════════════════════════════════════════ -# TTS +# CHATBOT # ══════════════════════════════════════════════════════════════ -def make_tts(text, language): +def legal_chatbot(message, history, language): + if history is None: + history = [] + if not message or not message.strip(): + return history, "" + + response = "**Rahbar Legal Assistant**\n\nI can help with:\n" + for item in LEGAL_KNOWLEDGE: + response += f"• **{item['title']}**: {item['content'][:100]}...\n" + response += "\nPlease describe your specific issue for detailed guidance." + + history.append({"role": "user", "content": message}) + history.append({"role": "assistant", "content": response}) + return history, "" + +# ══════════════════════════════════════════════════════════════ +# VOICE FUNCTIONS +# ══════════════════════════════════════════════════════════════ +def text_to_speech(text, language): + if not text: + return None try: from gtts import gTTS - code = LANG_CODES.get(language,"en") - clean = re.sub(r'_[^_]+_','',str(text)); clean=re.sub(r'\*+','',clean).strip() - tts = gTTS(text=clean[:600], lang=code, slow=False) + clean = re.sub(r'[*_#`]', '', str(text))[:500] + if not clean: + return None + lang_code = LANG_CODES.get(language, "en") + tts = gTTS(text=clean, lang=lang_code, slow=False) path = f"/tmp/tts_{uuid.uuid4().hex[:8]}.mp3" - tts.save(path); return path - except Exception: - try: - from gtts import gTTS - tts = gTTS(text=str(text)[:600], lang="en", slow=False) - path = f"/tmp/tts_fb_{uuid.uuid4().hex[:8]}.mp3"; tts.save(path); return path - except Exception: return None - -# ══════════════════════════════════════════════════════════════ -# STT -# ══════════════════════════════════════════════════════════════ -def stt_transcribe(audio_file): - if audio_file is None: return "No audio received." - def to_wav(p): - if p.lower().endswith(".wav"): return p + tts.save(path) + return path + except: try: - from pydub import AudioSegment - out=p+"_c.wav"; AudioSegment.from_file(p).export(out,format="wav"); return out - except: return p + tts = gTTS(text=str(text)[:500], lang="en", slow=False) + path = f"/tmp/tts_fb_{uuid.uuid4().hex[:8]}.mp3" + tts.save(path) + return path + except: + return None + +def speech_to_text(audio_file): + if audio_file is None: + return "No audio recorded" + if GROQ_API_KEY: try: from groq import Groq - wav = to_wav(audio_file) - with open(wav,"rb") as f: - r = Groq(api_key=GROQ_API_KEY).audio.transcriptions.create( - model="whisper-large-v3",file=f,response_format="text") - t = (r if isinstance(r,str) else r.text).strip() - return t or "No speech detected." - except Exception as e: groq_err=str(e) - else: groq_err="API key not configured" + with open(audio_file, "rb") as f: + client = Groq(api_key=GROQ_API_KEY) + result = client.audio.transcriptions.create( + model="whisper-large-v3", file=f, response_format="text") + return result.strip() if result else "No speech detected" + except: + pass + try: import speech_recognition as sr - wav=to_wav(audio_file); rec=sr.Recognizer() - with sr.AudioFile(wav) as src: - rec.adjust_for_ambient_noise(src,duration=0.3); data=rec.record(src) - try: return rec.recognize_google(data,language="ur-PK") - except: return rec.recognize_google(data) - except Exception as e2: - return f"Transcription failed. Primary: {groq_err}. Fallback: {e2}" - -# ══════════════════════════════════════════════════════════════ -# LAW REFERENCE -# ══════════════════════════════════════════════════════════════ -def law_info(issue, language): - kb = LEGAL_KB.get(issue, {}) - rts = "\n".join(f" - {r}" for r in kb.get("citizen_rights",[])) - out = f"## Legal Reference: {issue}\n\n### Applicable Laws\n" - for l in kb.get("laws",[]): out+=f" - {l}\n" - out += (f"\n### Fine / Penalty\n{kb.get('fine','N/A')}\n" - f"\n### Responsible Authority\n{kb.get('authority','N/A')}\n" - f"\n### Helpline\n**{kb.get('hotline','N/A')}**\n" - f"\n### Response Time\n{kb.get('response','N/A')}\n" - f"\n### Citizen Rights\n{rts}\n" - f"\n### Escalation\n{kb.get('escalation','CM Portal: 0800-02345')}\n") - return out - -# ══════════════════════════════════════════════════════════════ -# ADMIN -# ══════════════════════════════════════════════════════════════ -def get_admin_stats(): - total=len(complaint_log) - if not total: return "No complaints filed yet.","" - counts={"Garbage":0,"Pot Hole":0,"Pipe Leakage":0}; cities={}; sevs=[] - for c in complaint_log: - iss=c.get("issue",""); counts[iss]=counts.get(iss,0)+1 - cit=c.get("city","?"); cities[cit]=cities.get(cit,0)+1 - sevs.append(c.get("severity",5)) - avg=sum(sevs)/len(sevs); top=max(cities,key=cities.get) - stats=(f"## Dashboard\n|Metric|Value|\n|---|---|\n|Total|**{total}**|\n" - f"|Avg Severity|**{avg:.1f}/10**|\n|Top City|**{top}**|\n\n" - f"### By Issue\n|Issue|Count|\n|---|---|\n" - f"|Garbage|{counts['Garbage']}|\n|Pot Hole|{counts['Pot Hole']}|\n|Pipe Leakage|{counts['Pipe Leakage']}|\n\n" - f"### By City\n|City|Count|\n|---|---|\n") - for c,n in sorted(cities.items(),key=lambda x:-x[1]): stats+=f"|{c}|{n}|\n" - log="## Recent Complaints\n\n" - for c in reversed(complaint_log[-10:]): - log+=(f"**{c['id']}** | {c['timestamp']} | {c['city']}, {c['location']} | " - f"{c['issue']} | Sev {c['severity']}/10 | {c.get('name','?')}\n\n") - return stats, log + recognizer = sr.Recognizer() + with sr.AudioFile(audio_file) as source: + audio = recognizer.record(source) + return recognizer.recognize_google(audio) + except: + return "Could not transcribe. Please type your question." -def sev_label(s): return "LOW" if s<=3 else ("MEDIUM" if s<=6 else ("HIGH" if s<=8 else "CRITICAL")) +def voice_to_chat(audio_file, history, language): + if audio_file is None: + return history or [], "" + transcribed = speech_to_text(audio_file) + if not transcribed or transcribed.startswith("Could not") or transcribed.startswith("No audio"): + return history or [], transcribed + new_hist, _ = legal_chatbot(transcribed, history or [], language) + return new_hist, "" -def update_areas(city): - """Not used anymore — we use free-text location instead of fixed areas.""" - return city +def read_last_answer(history, language): + if not history: + return None + for msg in reversed(history): + if isinstance(msg, dict) and msg.get("role") == "assistant": + content = msg.get("content", "") + if content: + clean = re.sub(r'[*_#`]', '', content[:500]) + return text_to_speech(clean, language) if clean else None + return None # ══════════════════════════════════════════════════════════════ -# PDF GENERATION (ReportLab — professional, no grid lines) +# PDF GENERATION # ══════════════════════════════════════════════════════════════ -def generate_pdf(cid, ts, name, cnic, phone, city, location, issue_type, - language, severity, g_status, g_reason, g_conf, kb, - description, advice): +def generate_pdf(cid, ts, name, cnic, phone, city, location, issue_type, language, + severity, status, reason, confidence, info, description): try: from reportlab.lib.pagesizes import A4 from reportlab.lib import colors from reportlab.lib.units import inch - from reportlab.lib.styles import ParagraphStyle - from reportlab.lib.enums import TA_CENTER, TA_LEFT - from reportlab.platypus import (SimpleDocTemplate, Paragraph, - Spacer, Table, TableStyle, HRFlowable) + from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet + from reportlab.lib.enums import TA_CENTER + from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle path = f"/tmp/Rahbar_{cid}.pdf" - doc = SimpleDocTemplate(path, pagesize=A4, - leftMargin=0.75*inch, rightMargin=0.75*inch, - topMargin=0.75*inch, bottomMargin=0.75*inch) - - DG = colors.HexColor("#1a5c3f"); MG = colors.HexColor("#25a06b") - LG = colors.HexColor("#eaf5ef"); GD = colors.HexColor("#c8860a") - GDL= colors.HexColor("#fef9ee"); WH = colors.white - TX = colors.HexColor("#0d2b1e"); MU = colors.HexColor("#5a8a6e") - SEV_C = {"LOW":colors.HexColor("#27ae60"),"MEDIUM":colors.HexColor("#f39c12"), - "HIGH":colors.HexColor("#e67e22"),"CRITICAL":colors.HexColor("#c0392b")} - - def PS(n,**kw): return ParagraphStyle(n,**kw) - W = 7.0*inch - - sTitW = PS("tw",fontName="Helvetica-Bold", fontSize=17,textColor=WH, alignment=TA_CENTER,leading=22,spaceAfter=2) - sSubW = PS("sw",fontName="Helvetica", fontSize=10,textColor=colors.HexColor("#b8e8cc"),alignment=TA_CENTER,leading=14,spaceAfter=2) - sRefW = PS("rw",fontName="Helvetica", fontSize=8, textColor=colors.HexColor("#a0d8b8"),alignment=TA_CENTER,spaceAfter=0) - sSecH = PS("sh",fontName="Helvetica-Bold", fontSize=10,textColor=WH, leading=14,spaceAfter=0) - sSevB = PS("sb",fontName="Helvetica-Bold", fontSize=11,textColor=WH, alignment=TA_CENTER,leading=16) - sLbl = PS("lb",fontName="Helvetica-Bold", fontSize=8.5,textColor=MU, leading=12) - sVal = PS("vl",fontName="Helvetica", fontSize=9.5,textColor=TX, leading=14) - sBod = PS("bd",fontName="Helvetica", fontSize=9, textColor=TX, leading=13,spaceAfter=3) - sBodI = PS("bi",fontName="Helvetica-Oblique", fontSize=9, textColor=colors.HexColor("#2d5a3e"),leading=13) - sBul = PS("bl",fontName="Helvetica", fontSize=9, textColor=TX, leading=13,leftIndent=12) - sGoldD = PS("gd",fontName="Helvetica-Bold", fontSize=10, textColor=WH, alignment=TA_CENTER,leading=15) - sDecl = PS("dc",fontName="Helvetica", fontSize=9, textColor=TX, leading=13) - sFoot = PS("ft",fontName="Helvetica", fontSize=7.5,textColor=WH, alignment=TA_CENTER,leading=11) - - date_str=datetime.datetime.now().strftime("%d %B %Y") - time_str=datetime.datetime.now().strftime("%I:%M %p") - sl=sev_label(severity) - - def sec(letter, title): - t=Table([[Paragraph(f" {letter}. {title.upper()}",sSecH)]],colWidths=[W]) - t.setStyle(TableStyle([("BACKGROUND",(0,0),(-1,-1),DG),("TOPPADDING",(0,0),(-1,-1),6), - ("BOTTOMPADDING",(0,0),(-1,-1),6),("LEFTPADDING",(0,0),(-1,-1),10)])) - return t - - def grid(pairs): - rows=[]; row=[] - for i,(lbl,val) in enumerate(pairs): - row.extend([Paragraph(lbl,sLbl),Paragraph(str(val),sVal)]) - if len(row)==4 or i==len(pairs)-1: - while len(row)<4: row.extend([Paragraph("",sLbl),Paragraph("",sVal)]) - rows.append(row); row=[] - t=Table(rows,colWidths=[2.1*inch,1.4*inch,2.1*inch,1.4*inch]) - t.setStyle(TableStyle([("BACKGROUND",(0,0),(-1,-1),LG),("TOPPADDING",(0,0),(-1,-1),5), - ("BOTTOMPADDING",(0,0),(-1,-1),5),("LEFTPADDING",(0,0),(-1,-1),6), - ("ROWBACKGROUNDS",(0,0),(-1,-1),[LG,WH])])) - return t - - def card(paras, bg=None): - bg=bg or LG - t=Table([[p] for p in paras],colWidths=[W]) - t.setStyle(TableStyle([("BACKGROUND",(0,0),(-1,-1),bg),("TOPPADDING",(0,0),(-1,-1),6), - ("BOTTOMPADDING",(0,0),(-1,-1),6),("LEFTPADDING",(0,0),(-1,-1),12), - ("RIGHTPADDING",(0,0),(-1,-1),10)])) - return t - - def sp(h=0.15): return Spacer(1,h*inch) - - story=[] - - # Banner - h_t=Table([[Paragraph("GOVERNMENT OF PAKISTAN",sTitW)], - [Paragraph("CIVIC COMPLAINT REPORT",sTitW)], - [Paragraph("Rahbar Digital Civic Redressal System",sSubW)], - [Paragraph(f"Reference: {cid} | {date_str} at {time_str} | Language: {language}",sRefW)]], - colWidths=[W]) - h_t.setStyle(TableStyle([("BACKGROUND",(0,0),(-1,-1),DG),("TOPPADDING",(0,0),(-1,-1),10), - ("BOTTOMPADDING",(0,0),(-1,-1),10),("LEFTPADDING",(0,0),(-1,-1),14)])) - story+=[h_t,sp(0.1)] - - # Severity badge - s_t=Table([[Paragraph(f"SEVERITY: {severity}/10 — {sl}",sSevB)]],colWidths=[W]) - s_t.setStyle(TableStyle([("BACKGROUND",(0,0),(-1,-1),SEV_C.get(sl,MG)), - ("TOPPADDING",(0,0),(-1,-1),8),("BOTTOMPADDING",(0,0),(-1,-1),8)])) - story+=[s_t,sp(0.16)] - - story+=[sec("A","Complainant Information"),sp(0.08)] - story+=[grid([("Full Name",name),("CNIC",cnic),("Phone",phone or "N/A"),("City",city)]),sp(0.14)] - - story+=[sec("B","Complaint Details"),sp(0.08)] - story+=[grid([("Issue Type",issue_type),("Location",location[:50]),("Date",date_str),("Time",time_str)])] - if description.strip(): - story+=[sp(0.08),card([Paragraph(f"Description: {description.strip()}",sBodI)])] - story+=[sp(0.14)] - - story+=[sec("C","Verification Results"),sp(0.08)] - ai_bg=colors.HexColor("#e6f7ed") if "APPROVED" in g_status else colors.HexColor("#fdecea") - story+=[card([Paragraph(f"Status: {g_status} | Confidence: {g_conf}",sBod), - Paragraph(f"Assessment: {g_reason}",sBod)],bg=ai_bg),sp(0.14)] - - story+=[sec("D","Legal Framework"),sp(0.08)] - story+=[grid([("Authority",kb.get("authority","N/A")),("Helpline",kb.get("hotline","N/A")), - ("Response Time",kb.get("response","N/A")),("Fine/Penalty",kb.get("fine","N/A"))]),sp(0.08)] - law_rows=[[Paragraph(f"{i}. {l}",sBul)] for i,l in enumerate(kb.get("laws",[]),1)] - if law_rows: - lt=Table(law_rows,colWidths=[W]) - lt.setStyle(TableStyle([("BACKGROUND",(0,0),(-1,-1),LG),("TOPPADDING",(0,0),(-1,-1),4), - ("BOTTOMPADDING",(0,0),(-1,-1),4),("LEFTPADDING",(0,0),(-1,-1),10)])) - story.append(lt) - story+=[sp(0.14)] - - story+=[sec("E","Citizen's Legal Rights"),sp(0.08)] - rt_rows=[[Paragraph(f"✓ {r}",sBul)] for r in kb.get("citizen_rights",[])] - if rt_rows: - rt=Table(rt_rows,colWidths=[W]) - rt.setStyle(TableStyle([("TOPPADDING",(0,0),(-1,-1),4),("BOTTOMPADDING",(0,0),(-1,-1),4), - ("LEFTPADDING",(0,0),(-1,-1),8), - ("ROWBACKGROUNDS",(0,0),(-1,-1),[WH,LG])])) - story.append(rt) - story+=[sp(0.08),card([Paragraph(f"Escalation Path: {kb.get('escalation','CM Portal: 0800-02345')}",sBodI)],bg=GDL),sp(0.14)] - - story+=[sec(f"F",f"Legal Advice ({language})"),sp(0.08)] - adv_paras=[Paragraph(line.strip(),sBod) for line in advice.strip().split("\n") if line.strip()] - if adv_paras: - at=Table([[p] for p in adv_paras],colWidths=[W]) - at.setStyle(TableStyle([("BACKGROUND",(0,0),(-1,-1),LG),("TOPPADDING",(0,0),(-1,-1),4), - ("BOTTOMPADDING",(0,0),(-1,-1),4),("LEFTPADDING",(0,0),(-1,-1),10)])) - story.append(at) - story+=[sp(0.14)] - - story+=[sec("G","Mandatory Action Directive"),sp(0.08)] - dir_t=Table([[Paragraph(f"MANDATORY ACTION REQUIRED WITHIN: {kb.get('response','72 hours').upper()}",sGoldD)]],colWidths=[W]) - dir_t.setStyle(TableStyle([("BACKGROUND",(0,0),(-1,-1),GD),("TOPPADDING",(0,0),(-1,-1),9),("BOTTOMPADDING",(0,0),(-1,-1),9)])) - story+=[dir_t,sp(0.08)] - story+=[grid([("Authority",kb.get("authority","N/A")),("Helpline",kb.get("hotline","N/A")), - ("Citizen Portal","citizenportal.gov.pk"),("CM Toll-Free","0800-02345")]),sp(0.16)] - - story+=[sec("H","Declaration & Official Use"),sp(0.08)] - decl_inner=[ - [Paragraph(f"I, {name} (CNIC: {cnic}), declare that the information provided is true and correct to the best of my knowledge.",sDecl)], - [sp(0.1)], - [Table([[Paragraph("Complainant Signature",sLbl),Paragraph("Date",sLbl),Paragraph("Reference No.",sLbl)], - [Paragraph("____________________________",sVal),Paragraph(date_str,sVal),Paragraph(cid,sVal)]], - colWidths=[2.5*inch,2.5*inch,2.0*inch])], - [sp(0.1)], - [Table([[Paragraph("Received By",sLbl),Paragraph("Date of Receipt",sLbl), - Paragraph("Action Taken",sLbl),Paragraph("Resolved On",sLbl)], - [Paragraph("______________",sVal),Paragraph("______________",sVal), - Paragraph("______________",sVal),Paragraph("______________",sVal)]], - colWidths=[1.75*inch]*4)], + doc = SimpleDocTemplate(path, pagesize=A4, + leftMargin=0.75*inch, rightMargin=0.75*inch, + topMargin=0.75*inch, bottomMargin=0.75*inch) + + styles = getSampleStyleSheet() + title_style = ParagraphStyle('Title', parent=styles['Heading1'], fontSize=14, alignment=TA_CENTER, textColor=colors.HexColor('#1a5c3f')) + body_style = ParagraphStyle('Body', parent=styles['Normal'], fontSize=9, leading=14) + + story = [] + date_str = datetime.datetime.now().strftime("%d %B %Y") + + story.append(Paragraph("GOVERNMENT OF PAKISTAN", title_style)) + story.append(Paragraph("CIVIC COMPLAINT REPORT", title_style)) + story.append(Spacer(1, 0.2*inch)) + + data = [ + ["Complaint ID:", cid, "Date:", date_str], + ["Name:", name, "CNIC:", cnic], + ["Issue:", issue_type, "Severity:", f"{severity}/10"], + ["Location:", f"{location}, {city}", "Status:", status], ] - decl_t=Table(decl_inner,colWidths=[W]) - decl_t.setStyle(TableStyle([("BACKGROUND",(0,0),(-1,-1),LG),("TOPPADDING",(0,0),(-1,-1),7), - ("BOTTOMPADDING",(0,0),(-1,-1),7),("LEFTPADDING",(0,0),(-1,-1),12), - ("RIGHTPADDING",(0,0),(-1,-1),12)])) - story+=[decl_t,sp(0.16)] - - foot_t=Table([[Paragraph(f"Generated by Rahbar — Pakistan's Civic Redressal Platform | {ts} | {cid}",sFoot)]],colWidths=[W]) - foot_t.setStyle(TableStyle([("BACKGROUND",(0,0),(-1,-1),DG),("TOPPADDING",(0,0),(-1,-1),7),("BOTTOMPADDING",(0,0),(-1,-1),7)])) - story.append(foot_t) - + + t = Table(data, colWidths=[1.5*inch, 2.2*inch, 1.2*inch, 2.1*inch]) + t.setStyle(TableStyle([ + ('FONTNAME', (0,0), (0,-1), 'Helvetica-Bold'), + ('FONTSIZE', (0,0), (-1,-1), 9), + ('TOPPADDING', (0,0), (-1,-1), 6), + ('BOTTOMPADDING', (0,0), (-1,-1), 6), + ('GRID', (0,0), (-1,-1), 0.5, colors.grey), + ])) + story.append(t) + story.append(Spacer(1, 0.2*inch)) + + story.append(Paragraph(f"Authority: {info.get('authority', 'N/A')}", body_style)) + story.append(Paragraph(f"Helpline: {info.get('hotline', 'N/A')}", body_style)) + story.append(Paragraph(f"Response Time: {info.get('response', 'N/A')}", body_style)) + story.append(Spacer(1, 0.2*inch)) + + story.append(Paragraph(f"Declaration: I, {name}, certify that the information is true.", body_style)) + story.append(Paragraph(f"Signature: ____________________", body_style)) + story.append(Paragraph(f"Reference: {cid}", body_style)) + doc.build(story) return path except Exception as e: - import traceback; traceback.print_exc() print(f"PDF error: {e}") - fallback=f"/tmp/Rahbar_{cid}.txt" - with open(fallback,"w",encoding="utf-8") as f: - f.write(f"RAHBAR COMPLAINT\nID:{cid}\nIssue:{issue_type}\nLocation:{location},{city}\nSeverity:{severity}/10\nName:{name} CNIC:{cnic}\n{ts}") - return fallback + path = f"/tmp/Rahbar_{cid}.txt" + with open(path, "w", encoding="utf-8") as f: + f.write(f"RAHBAR COMPLAINT REPORT\nID: {cid}\nIssue: {issue_type}\nLocation: {location}, {city}\nSeverity: {severity}/10\nName: {name}\nCNIC: {cnic}\nDate: {ts}") + return path # ══════════════════════════════════════════════════════════════ -# MAIN REPORT +# MAIN REPORT FUNCTION # ══════════════════════════════════════════════════════════════ def make_report(image, issue_type, city, location, name, cnic, phone, description, language, enable_tts): - if image is None: return None,"Please upload an image.","","",None,"",None,None,None - if not location.strip(): return None,"Please enter a location.","","",None,"",None,None,None - if not name.strip(): return None,"Please enter your full name.","","",None,"",None,None,None - if not cnic.strip(): return None,"Please enter your CNIC number.","","",None,"",None,None,None + if image is None: + return (None, "Please upload an image.", "", "", None, "", None, None, None) + if not location or not location.strip(): + return (None, "Please enter a location.", "", "", None, "", None, None, None) + if not name or not name.strip(): + return (None, "Please enter your full name.", "", "", None, "", None, None, None) + if not cnic or not cnic.strip(): + return (None, "Please enter your CNIC number.", "", "", None, "", None, None, None) cid = f"RB-{uuid.uuid4().hex[:8].upper()}" - ts = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") - - ann, yolo_s, yolo_sev = detect_with_yolo(image, issue_type) - gem_raw = analyze_with_gemini(image, issue_type, location, city, yolo_s) - gem = parse_gemini(gem_raw) - - if gem["status"] == "REJECTED": - return (ann, - f"COMPLAINT REJECTED\n\nReason: {gem['reason']}\nConfidence: {gem['confidence']}\n\n" - f"Please upload a clear image of the issue ({issue_type}).\nNot logged.", - "","",None,cid,None,None,None) - - if gem["status"]=="UNKNOWN" and "not set" in gem_raw: - gem["reason"]="Verification skipped (API key not configured)."; gem["status"]="APPROVED_WITH_WARNING" - - final_sev = gem["severity"] if gem["status"]=="APPROVED" else yolo_sev - kb = LEGAL_KB.get(issue_type, {}) - local = LOCALIZED.get(issue_type,{}).get(language,"") - advice = get_legal_advice(issue_type, location, city, yolo_s, final_sev, language) - - pdf_path = generate_pdf(cid, ts, name, cnic, phone, city, location, issue_type, - language, final_sev, gem["status"], gem["reason"], - gem["confidence"], kb, description, advice) + ts = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + date_str = datetime.datetime.now().strftime("%d %B %Y") + + annotated_img, yolo_summary, severity, status, reason, reason_urdu, confidence, action = analyze_image(image, issue_type) + + info = LEGAL_INFO.get(issue_type, LEGAL_INFO.get("Garbage", {})) + legal_advice = get_legal_advice(issue_type, location, severity, language) + + severity_icon = "🟢" if severity <= 3 else ("🟡" if severity <= 6 else ("🟠" if severity <= 8 else "🔴")) + + report = f"""====================================================================== +GOVERNMENT OF PAKISTAN — CIVIC COMPLAINT REPORT +Rahbar Digital Civic Redressal System +====================================================================== +Complaint ID: {cid} +Date: {date_str} | Language: {language} +====================================================================== +SECTION A — COMPLAINANT INFORMATION +---------------------------------------------------------------------- +Full Name: {name} +CNIC: {cnic} +Phone: {phone or "Not Provided"} +City: {city} +Location: {location} +====================================================================== +SECTION B — COMPLAINT DETAILS +---------------------------------------------------------------------- +Issue Type: {issue_type} +Severity: {severity_icon} {severity}/10 +Description: {description.strip() if description else "[None provided]"} +====================================================================== +SECTION C — VERIFICATION RESULTS +---------------------------------------------------------------------- +Status: {status} +Confidence: {confidence} +Finding: {reason} +Action: {action} +====================================================================== +SECTION D — LEGAL FRAMEWORK +---------------------------------------------------------------------- +Authority: {info.get('authority', 'N/A')} +Helpline: {info.get('hotline', 'N/A')} +Response Time: {info.get('response', 'N/A')} +Fine/Penalty: {info.get('fine', 'N/A')} +====================================================================== +SECTION E — CITIZEN'S RIGHTS +---------------------------------------------------------------------- +{chr(10).join(f'• {r}' for r in info.get('citizen_rights', []))} + +Escalation Path: {info.get('escalation', 'CM Portal: 0800-02345')} +====================================================================== +MANDATORY ACTION WITHIN: {info.get('response', '72 hours').upper()} +Citizen Portal: citizenportal.gov.pk | CM: 0800-02345 +====================================================================== +DECLARATION +I, {name} (CNIC: {cnic}), declare that the information provided is true. +Signature: ______________________ +Reference: {cid} | {ts} +======================================================================""" + + complaint_log.append({ + "id": cid, "timestamp": ts, "city": city, "location": location, + "issue": issue_type, "severity": severity, "language": language, + "name": name, "cnic": cnic, "phone": phone + }) + + wa_text = f"Rahbar Complaint\nRef: {cid}\nIssue: {issue_type}\nLocation: {location}, {city}\nSeverity: {severity}/10\nAuthority: {info.get('authority', 'N/A')}\nHelpline: {info.get('hotline', 'N/A')}\nFiled: {ts}" + wa_md = f"[📲 Share on WhatsApp](https://wa.me/?text={urllib.parse.quote(wa_text[:1000])})" + + report_tts = text_to_speech(report[:800], language) if enable_tts else None + advice_tts = text_to_speech(legal_advice[:600], language) if enable_tts else None + pdf_path = generate_pdf(cid, ts, name, cnic, phone, city, location, issue_type, language, severity, status, reason, confidence, info, description or "") + + map_fig = build_map_city(city) + + return (annotated_img, report, wa_md, legal_advice, report_tts, cid, advice_tts, pdf_path, map_fig) + +# ══════════════════════════════════════════════════════════════ +# HELPER FUNCTIONS +# ══════════════════════════════════════════════════════════════ +def law_info(issue, language): + info = LEGAL_INFO.get(issue, LEGAL_INFO.get("Garbage", {})) + rights = "\n".join(f"• {r}" for r in info.get("citizen_rights", [])) + return f"""## Legal Reference: {issue} - sl = sev_label(final_sev) - report = ( - f"GOVERNMENT OF PAKISTAN — CIVIC COMPLAINT REPORT\n" - f"Rahbar Digital Civic Redressal System\n" - f"{'='*54}\n" - f"Complaint No. : {cid}\n" - f"Date / Time : {datetime.datetime.now().strftime('%d %B %Y')} / {datetime.datetime.now().strftime('%I:%M %p')}\n" - f"Language : {language}\n\n" - f"SECTION A — COMPLAINANT\n" - f"Name : {name}\nCNIC : {cnic}\nPhone : {phone or 'Not provided'}\n" - f"City : {city}\nLocation: {location}\n\n" - f"SECTION B — COMPLAINT DETAILS\n" - f"Issue : {issue_type}\nSeverity: {final_sev}/10 [{sl}]\n" - f"Description:\n{description.strip() or '[None provided]'}\n\n" - f"SECTION C — VERIFICATION\n" - f"Status : {gem['status']}\nConfidence: {gem['confidence']}\nFinding : {gem['reason']}\n\n" - f"SECTION D — LEGAL FRAMEWORK\n" - f"Authority: {kb.get('authority','N/A')}\n" - f"Helpline : {kb.get('hotline','N/A')}\n" - f"Response : {kb.get('response','N/A')}\n" - f"Fine : {kb.get('fine','N/A')}\n\n" - f"SECTION E — YOUR RIGHTS\n" + - "\n".join(f" - {r}" for r in kb.get("citizen_rights",[])) + - f"\n\nEscalation: {kb.get('escalation','CM Portal: 0800-02345')}\n\n" - f"MANDATORY ACTION WITHIN: {kb.get('response','72 hours').upper()}\n" - f"Portal: citizenportal.gov.pk | CM: 0800-02345\n\n" - f"DECLARATION\nI, {name} (CNIC: {cnic}), declare this information is accurate.\n" - f"Reference: {cid} | {ts}" - ) +**Applicable Laws:** +{chr(10).join(f'• {l}' for l in info.get('laws', []))} - wa_text = (f"Rahbar Civic Complaint\nID: {cid}\nIssue: {issue_type}\n" - f"Location: {location}, {city}\nSeverity: {final_sev}/10\n" - f"Authority: {kb.get('authority','N/A')}\nHotline: {kb.get('hotline','N/A')}\n{ts}") - wa_md = f"[📲 Share on WhatsApp](https://wa.me/?text={urllib.parse.quote(wa_text[:1000])})" +**Your Rights:** +{rights} - complaint_log.append({"id":cid,"timestamp":ts,"city":city,"location":location, - "issue":issue_type,"severity":final_sev,"language":language, - "name":name,"cnic":cnic,"phone":phone}) +**Authority:** {info.get('authority', 'N/A')} +**Helpline:** {info.get('hotline', 'N/A')} +**Response Time:** {info.get('response', 'N/A')} +**Escalation:** {info.get('escalation', 'CM Portal: 0800-02345')} +""" - report_tts=None - if enable_tts: - report_tts=make_tts( - f"Complaint {cid} filed. Issue: {issue_type}. " - f"Location: {location}, {city}. Severity: {final_sev} out of 10. " - f"Authority: {kb.get('authority','')}. Helpline: {kb.get('hotline','')}. {local}", - language) +def get_admin_stats(): + total = len(complaint_log) + if not total: + return "No complaints filed yet.", "" + counts = {"Garbage": 0, "Pot Hole": 0, "Pipe Leakage": 0} + cities = {} + for c in complaint_log: + issue = c.get('issue', '') + counts[issue] = counts.get(issue, 0) + 1 + city = c.get('city', 'Unknown') + cities[city] = cities.get(city, 0) + 1 + + stats = f"## Complaint Statistics\n**Total Complaints:** {total}\n\n### By Issue Type\n" + for k, v in counts.items(): + stats += f"- {k}: {v}\n" + stats += "\n### By City\n" + for c, n in sorted(cities.items(), key=lambda x: -x[1])[:10]: + stats += f"- {c}: {n}\n" + + logs = "## Recent Complaints\n" + for c in complaint_log[-10:]: + logs += f"- **{c['id']}** | {c['issue']} | {c['city']}, {c['location']} | Severity {c['severity']}/10\n" + return stats, logs + +# ══════════════════════════════════════════════════════════════ +# CSS +# ══════════════════════════════════════════════════════════════ +CSS = """ +@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap'); + +:root { + --bg: #ffffff; + --bg2: #f5f8f6; + --txt: #0d2b1e; + --border: #c0d9ca; + --green: #1f7a52; + --gold: #c8860a; +} - advice_tts = make_tts(advice[:600], language) - map_fig = build_map_city(city) +@media (prefers-color-scheme: dark) { + :root { + --bg: #0c1a10; + --bg2: #132118; + --txt: #d5f0e0; + --border: #243d2d; + --green: #2a9460; + --gold: #f5a623; + } +} - return ann, report, wa_md, advice, report_tts, cid, advice_tts, pdf_path, map_fig +* { font-family: 'Inter', sans-serif !important; } +.gradio-container { background: var(--bg) !important; } -# ══════════════════════════════════════════════════════════════ -# CSS — light + dark mode, both automatic and manual -# ══════════════════════════════════════════════════════════════ -CSS = """ -@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Playfair+Display:wght@700;900&family=JetBrains+Mono:wght@400;500&display=swap'); +label, .gr-label { color: var(--txt) !important; } -/* Light mode */ -:root{ - --bg:#ffffff;--bg2:#f5f8f6;--bg3:#e8f3ec; - --txt:#0d2b1e;--txt2:#2d5a3e;--muted:#6a8e7a; - --border:#c0d9ca;--border2:#1f7a52; - --green:#1f7a52;--green2:#25a06b;--green3:#2ec97f; - --gold:#c8860a;--gold2:#f5a623;--gold-bg:#fffbf0; - --info-bg:#f0faf4;--warn-bg:#fffbf0; - --shadow:0 2px 10px rgba(13,43,30,.10); - --radius:10px;--radius-lg:18px; - --header-bg:linear-gradient(135deg,#14432e 0%,#0d2b1e 60%,#091a10 100%); +input, textarea, select { + background: var(--bg) !important; + border: 1px solid var(--border) !important; + color: var(--txt) !important; + border-radius: 8px !important; } -/* System dark mode */ -@media(prefers-color-scheme:dark){ - :root{ - --bg:#0c1a10;--bg2:#132118;--bg3:#1a3024; - --txt:#d5f0e0;--txt2:#8fd4ad;--muted:#5a9a78; - --border:#243d2d;--border2:#2a9460; - --green:#2a9460;--green2:#34c47a;--green3:#52e09a; - --gold:#f5a623;--gold2:#f7bc57;--gold-bg:#1e1500; - --info-bg:#0d2016;--warn-bg:#1a1300; - --shadow:0 2px 14px rgba(0,0,0,.45); - --header-bg:linear-gradient(135deg,#091a10 0%,#060d08 60%,#040a06 100%); - } -} -/* Manual dark toggle class */ -.rh-dark{ - --bg:#0c1a10;--bg2:#132118;--bg3:#1a3024; - --txt:#d5f0e0;--txt2:#8fd4ad;--muted:#5a9a78; - --border:#243d2d;--border2:#2a9460; - --green:#2a9460;--green2:#34c47a;--green3:#52e09a; - --gold:#f5a623;--gold2:#f7bc57;--gold-bg:#1e1500; - --info-bg:#0d2016;--warn-bg:#1a1300; - --shadow:0 2px 14px rgba(0,0,0,.45); - --header-bg:linear-gradient(135deg,#091a10 0%,#060d08 60%,#040a06 100%); + +button.primary { + background: linear-gradient(135deg, var(--green), #25a06b) !important; + color: white !important; + border: none !important; + font-weight: 600 !important; } -*,*::before,*::after{box-sizing:border-box;} -body,.gradio-container{ - font-family:'Inter',sans-serif!important; - background:var(--bg)!important;color:var(--txt)!important; - transition:background .3s,color .3s; + +.tab-nav { background: var(--bg2) !important; border-bottom: 2px solid var(--border) !important; } +.tab-nav button { color: var(--txt) !important; } +.tab-nav button.selected { color: var(--gold) !important; border-bottom: 2px solid var(--gold) !important; } + +.info-box { + background: var(--bg2); + border-left: 4px solid var(--green); + padding: 10px 15px; + border-radius: 8px; + margin: 10px 0; + font-size: 0.85rem; } -/* Header */ -.rh-header{background:var(--header-bg);padding:26px 20px 20px;text-align:center; - position:relative;overflow:hidden;border-bottom:2px solid var(--green);} -.rh-header::before{content:'';position:absolute;inset:0; - background:radial-gradient(ellipse 70% 60% at 50% 0%,rgba(37,160,107,.14),transparent);pointer-events:none;} -.rh-title{font-family:'Playfair Display',serif!important;font-size:clamp(2rem,5vw,3rem)!important; - font-weight:900!important;color:#f8fdf9!important;margin:0 0 4px!important;line-height:1.1;} -.rh-subtitle{font-size:clamp(.9rem,2.5vw,1.05rem);color:#a8e8c4;margin:4px 0 6px;} -.rh-tag{font-size:.76rem;color:#5de3a3;letter-spacing:.1em;text-transform:uppercase;} -/* Top bar */ -.top-bar{display:flex;flex-wrap:wrap;align-items:center;justify-content:space-between; - padding:7px 16px;background:var(--bg2);border-bottom:1px solid var(--border);gap:8px;} -.badge-group{display:flex;flex-wrap:wrap;gap:6px;} -.badge{font-size:.67rem;font-weight:600;letter-spacing:.05em;padding:3px 10px;border-radius:20px; - text-transform:uppercase;background:var(--bg);color:var(--green3);border:1px solid var(--border2);} -.badge-gold{color:var(--gold);border-color:var(--gold2);} -.badge-red {color:#ff8080;border-color:rgba(255,100,100,.4);} -.dark-toggle{background:transparent;border:1px solid var(--border2);border-radius:20px; - padding:4px 13px;cursor:pointer;color:var(--muted);font-size:.78rem;font-weight:500; - font-family:'Inter',sans-serif;transition:all .2s;} -.dark-toggle:hover{background:var(--bg3);color:var(--txt);} -/* Tabs */ -.gradio-container .tab-nav{background:var(--bg2)!important;border-bottom:2px solid var(--border)!important;} -.gradio-container .tab-nav button{font-family:'Inter',sans-serif!important;font-weight:500!important; - font-size:.83rem!important;color:var(--muted)!important;padding:11px 18px!important; - border-radius:0!important;background:transparent!important;transition:all .2s!important;} -.gradio-container .tab-nav button.selected, -.gradio-container .tab-nav button[aria-selected="true"]{ - color:var(--gold)!important;border-bottom:3px solid var(--gold2)!important;background:transparent!important;} -/* Card title */ -.sec-title{font-size:.67rem;font-weight:700;letter-spacing:.12em;text-transform:uppercase; - color:var(--green3);margin-bottom:10px;padding-bottom:7px;border-bottom:1px solid var(--border);} -/* Form */ -label,.gradio-container .label-wrap span{color:var(--txt)!important;} -.gradio-container input,.gradio-container textarea{ - background:var(--bg)!important;border:1px solid var(--border2)!important; - border-radius:var(--radius)!important;color:var(--txt)!important;font-family:'Inter',sans-serif!important;} -.gradio-container input:focus,.gradio-container textarea:focus{ - border-color:var(--gold2)!important;box-shadow:0 0 0 3px rgba(245,166,35,.15)!important;outline:none!important;} -.gradio-container .wrap{background:var(--bg)!important;border-color:var(--border2)!important;} -.gradio-container .block{background:var(--bg)!important;} -/* Buttons */ -.gradio-container button.primary{ - background:linear-gradient(135deg,var(--green),var(--green2))!important;color:#f8fdf9!important; - border:none!important;border-radius:var(--radius)!important;font-weight:600!important; - font-size:.88rem!important;padding:11px 22px!important;cursor:pointer!important; - box-shadow:var(--shadow)!important;transition:all .2s!important;} -.gradio-container button.primary:hover{ - background:linear-gradient(135deg,var(--green2),var(--green3))!important;transform:translateY(-1px)!important;} -.gradio-container button.secondary{ - background:var(--bg)!important;border:1px solid var(--border2)!important;color:var(--green3)!important;} -.gradio-container [data-testid="image"]{border:2px dashed var(--border2)!important; - border-radius:var(--radius-lg)!important;background:var(--bg2)!important;} -.gradio-container audio{width:100%!important;border-radius:var(--radius)!important;} -.gradio-container .prose h2,.gradio-container .prose h3{color:var(--gold)!important;} -/* Info boxes */ -.info-box{background:var(--info-bg);border:1px solid var(--border2);border-left:4px solid var(--green2); - border-radius:var(--radius);padding:10px 14px;font-size:.87rem;line-height:1.6;margin-bottom:8px;color:var(--txt2);} -.warn-box{background:var(--warn-bg);border:1px solid rgba(245,166,35,.4);border-left:4px solid var(--gold2); - border-radius:var(--radius);padding:10px 14px;font-size:.87rem;margin-bottom:8px;color:var(--txt2);} -.hotline-pill{display:inline-block;background:var(--bg2);color:var(--gold);border:1px solid var(--gold2); - border-radius:20px;padding:2px 11px;font-size:.78rem;font-weight:600;} -/* Report textarea */ -.gradio-container textarea{font-family:'JetBrains Mono',monospace!important;font-size:.82rem!important;line-height:1.7!important;} -/* Chatbot */ -.gradio-container .message.user{background:var(--bg3)!important;color:var(--txt)!important;} -.gradio-container .message.bot {background:var(--bg2)!important;color:var(--txt)!important;} -/* Dropdowns */ -.gradio-container select,.gradio-container [data-testid="dropdown"]{ - background:var(--bg)!important;color:var(--txt)!important;border-color:var(--border2)!important;} -/* Scrollbar */ -::-webkit-scrollbar{width:6px;height:6px;} -::-webkit-scrollbar-track{background:var(--bg2);} -::-webkit-scrollbar-thumb{background:var(--green);border-radius:3px;} -@media(max-width:640px){ - .rh-header{padding:14px 12px;} - .gradio-container .tab-nav button{padding:10px 10px!important;font-size:.74rem!important;} + +.sec-title { + font-size: 0.7rem; + font-weight: 700; + letter-spacing: 0.1em; + text-transform: uppercase; + color: var(--green); + margin-bottom: 10px; + padding-bottom: 6px; + border-bottom: 1px solid var(--border); } + +.message.user { background: var(--bg2) !important; } +.message.bot { background: var(--bg) !important; border: 1px solid var(--border) !important; } """ HEADER_HTML = """ -
-
Rahbar | رہبر
-
Pakistan's Civic Complaint Platform
-
Know Your Rights — File With Confidence
-
-
-
- Image Verification - Legal Assistant - Voice Support - 4 Languages - PDF Export - LIVE -
- +
+

Rahbar | رہبر

+

Pakistan's Civic Complaint System

- """ HOTLINES_HTML = """
- Emergency Helplines:  - Garbage 1139  - Roads/NHA 051-9032800  - WASA Lahore 042-99200300  - CM Portal 0800-02345  - Ombudsman 051-9204551 + Emergency Helplines:
+ 🗑️ Garbage: 1139 | 🕳️ Roads: 051-9032800 | 💧 WASA: 042-99200300 | 📞 CM Portal: 0800-02345
""" # ══════════════════════════════════════════════════════════════ -# UI +# BUILD UI # ══════════════════════════════════════════════════════════════ def build_ui(): default_map = build_map_city("Lahore") - + with gr.Blocks(title="Rahbar | Pakistan Civic Complaint System") as demo: gr.HTML(HEADER_HTML) - + with gr.Tabs(): - - # ════════════════════════════════════════════ - # TAB 1 — File Complaint - # ════════════════════════════════════════════ + # Tab 1 - File Complaint with gr.Tab("📝 File a Complaint"): - with gr.Row(equal_height=False): - - # Left: inputs - with gr.Column(scale=1, min_width=300): + with gr.Row(): + with gr.Column(scale=1): gr.HTML('
Citizen Details
') - name_tb = gr.Textbox(label="Full Name", placeholder="e.g. Ali Raza") - cnic_tb = gr.Textbox(label="CNIC (no dashes)", placeholder="1234567890123") - phone_tb = gr.Textbox(label="Phone Number (optional)", placeholder="03xxxxxxxxx") - - gr.HTML('
Issue Photo
') - gr.HTML('
Upload or capture a clear photo of the issue. On mobile, tap to use your camera.
') - image_input = gr.Image(type="pil", label="Upload / Capture Photo", - sources=["webcam","upload"], height=210) - - gr.HTML('
Complaint Details
') - issue_type = gr.Radio(choices=ISSUE_TYPES, value=ISSUE_TYPES[0], label="Issue Type") - city_dd = gr.Dropdown(choices=ALL_CITIES, value="Lahore", label="City", - allow_custom_value=True, - info="All cities in Pakistan — type to search") - - gr.HTML('
Location
') - gr.HTML("""
-Type your street/area below, or click Detect Location to auto-fill via your internet connection. -You can also click directly on the map to pin a location. -
""") - - location_tb = gr.Textbox( - label="Street / Landmark / Area", - placeholder="e.g. Near Jinnah Park, Main Boulevard, Street 5", - lines=1) - - gps_btn = gr.Button("📍 Detect My Location", variant="secondary") - gps_status = gr.Markdown( - value="_Click the button above to detect your approximate location._") - - # GPS hidden state - gps_lat = gr.State(value=None) - gps_lon = gr.State(value=None) - - gr.HTML('
Map
') - gr.HTML('
Click anywhere on the map to set a precise location — the street/area field will auto-fill.
') - map_plot = gr.Plot(label="Location Map", value=default_map) - - desc_tb = gr.Textbox(label="Description (optional)", placeholder="Describe the issue...", lines=3) - language_dd = gr.Dropdown(choices=LANGUAGES, value="English", label="Report & Voice Language") - tts_cb = gr.Checkbox(label="Read report aloud (TTS)", value=False) - submit_btn = gr.Button("Submit Complaint", variant="primary", size="lg") - - # Right: outputs - with gr.Column(scale=2, min_width=320): + name_tb = gr.Textbox(label="Full Name", placeholder="e.g., Ali Raza") + cnic_tb = gr.Textbox(label="CNIC (no dashes)", placeholder="1234567890123") + phone_tb = gr.Textbox(label="Phone (optional)", placeholder="03xxxxxxxxx") + + gr.HTML('
Issue Photo
') + image_input = gr.Image(type="pil", label="Upload Photo", height=200) + + gr.HTML('
Complaint Details
') + issue_type = gr.Radio(choices=ISSUE_TYPES, label="Issue Type") + city_dd = gr.Dropdown(choices=ALL_CITIES, value="Lahore", label="City", allow_custom_value=True) + + gr.HTML('
Location
') + gps_btn = gr.Button("📍 Detect My Location", variant="secondary") + gps_status = gr.Markdown("_Click the button above to detect your location_") + location_tb = gr.Textbox(label="Street / Landmark / Area", placeholder="Enter exact location") + map_plot = gr.Plot(label="Map", value=default_map) + + desc_tb = gr.Textbox(label="Description (optional)", lines=3) + language_dd = gr.Dropdown(choices=LANGUAGES, value="English", label="Language") + tts_cb = gr.Checkbox(label="🔊 Read report aloud", value=False) + submit_btn = gr.Button("Submit Complaint", variant="primary") + + with gr.Column(scale=1): gr.HTML('
Verification Result
') - annotated_out = gr.Image(label="Detection Output", height=230) - complaint_id_out = gr.Textbox(label="Complaint Reference ID", interactive=False) - - gr.HTML('
Complaint Report
') - report_out = gr.Textbox(label="Official Summary", lines=14, interactive=False, - placeholder="Report will appear here after submission...") + annotated_out = gr.Image(label="Analysis Result") + complaint_id_out = gr.Textbox(label="Complaint ID", interactive=False) + + gr.HTML('
Complaint Report
') + report_out = gr.Textbox(label="Report", lines=15, interactive=False) + pdf_out = gr.File(label="Download PDF Report") wa_out = gr.Markdown() - - gr.HTML('
Download PDF
') - gr.HTML('
Professional government-format PDF — download and share via WhatsApp.
') - pdf_out = gr.File(label="Download PDF Report", interactive=False) - report_tts_out = gr.Audio(label="Report Audio", autoplay=False) - - gr.HTML('
Legal Advice
') - gr.HTML('
Your rights and steps under Pakistani civic law — in your selected language.
') - legal_out = gr.Textbox(label="Legal Rights & Steps", lines=12, interactive=False, - placeholder="Legal advice will appear here...") - advice_tts_out = gr.Audio(label="Legal Advice Audio", autoplay=False) - - # ── GPS button ── - def on_gps(city): - fig, status, addr, lat, lon = gps_detect(city) - return fig, status, addr, lat, lon - - gps_btn.click(fn=on_gps, - inputs=[city_dd], - outputs=[map_plot, gps_status, location_tb, gps_lat, gps_lon]) - - # ── Map click → fill location ── - def on_map_clicked(evt: gr.SelectData, city): - """Triggered when user clicks Plotly map.""" - try: - lat = evt.index[0]; lon = evt.index[1] - addr = reverse_geocode(lat, lon) - fig = build_map(lat, lon, addr) - return addr, fig - except Exception: - return "", build_map_city(city) - - map_image.select(fn=on_map_clicked, - inputs=[city_dd], - outputs=[location_tb, map_plot]) - - # ── City change → update map ── - city_dd.change(fn=update_map_on_city, inputs=[city_dd], outputs=[map_plot]) - - # ── Location text change → update map ── - location_tb.change(fn=update_map_on_location, - inputs=[city_dd, city_dd, location_tb], - outputs=[map_plot]) - - # ── Submit ── + report_tts_out = gr.Audio(label="Report Audio") + + gr.HTML('
Legal Advice
') + legal_out = gr.Markdown() + advice_tts_out = gr.Audio(label="Legal Advice Audio") + + # GPS and map interactions + gps_btn.click(fn=gps_detect, inputs=[city_dd], outputs=[map_plot, gps_status, location_tb, gr.State(), gr.State()]) + city_dd.change(fn=build_map_city, inputs=[city_dd], outputs=[map_plot]) + location_tb.change(fn=build_map_city, inputs=[city_dd], outputs=[map_plot]) + submit_btn.click( fn=make_report, - inputs=[image_input, issue_type, city_dd, location_tb, - name_tb, cnic_tb, phone_tb, desc_tb, language_dd, tts_cb], - outputs=[annotated_out, report_out, wa_out, legal_out, - report_tts_out, complaint_id_out, advice_tts_out, - pdf_out, map_plot]) - - # ════════════════════════════════════════════ - # TAB 2 — Legal Reference & Chatbot - # ════════════════════════════════════════════ - with gr.Tab("⚖️ Legal Reference & Chatbot"): - - gr.HTML('
Civic Laws Quick Reference
') - with gr.Row(): - law_issue_dd = gr.Dropdown(choices=ISSUE_TYPES, value=ISSUE_TYPES[0], label="Issue Type", scale=1) - law_lang_dd = gr.Dropdown(choices=LANGUAGES, value="English", label="Language", scale=1) + inputs=[image_input, issue_type, city_dd, location_tb, name_tb, cnic_tb, phone_tb, desc_tb, language_dd, tts_cb], + outputs=[annotated_out, report_out, wa_out, legal_out, report_tts_out, complaint_id_out, advice_tts_out, pdf_out, map_plot] + ) + + # Tab 2 - Legal Rights + with gr.Tab("⚖️ Legal Rights"): + issue_dd = gr.Dropdown(choices=ISSUE_TYPES, label="Select Issue") + lang_dd = gr.Dropdown(choices=LANGUAGES, value="English", label="Language") + law_btn = gr.Button("Show Legal Information", variant="primary") law_out = gr.Markdown() - gr.Button("Show My Rights", variant="primary").click( - fn=law_info, inputs=[law_issue_dd, law_lang_dd], outputs=[law_out]) + law_btn.click(fn=law_info, inputs=[issue_dd, lang_dd], outputs=[law_out]) gr.HTML(HOTLINES_HTML) - - gr.HTML('
Ask the Legal Assistant
') - gr.HTML('
Ask any question about civic issues, your legal rights, or how to escalate a complaint. Voice input and audio output are both supported.
') - - chat_lang_dd = gr.Dropdown(choices=LANGUAGES, value="English", label="Response Language") - chatbot = gr.Chatbot(label="Legal Assistant", height=400, value=[]) - - with gr.Row(): - chat_input = gr.Textbox(label="Your Question", - placeholder="e.g. WASA didn't fix the pipe for 3 days — what are my rights?", - lines=2, scale=4) - chat_send_btn = gr.Button("Send ➤", variant="primary", scale=1) - - gr.HTML('
Voice Question
') - with gr.Row(): - chat_audio_in = gr.Audio(type="filepath", label="Record Your Question", - sources=["microphone","upload"], scale=3) - chat_voice_btn = gr.Button("🎤 Send Voice", variant="secondary", scale=1) - - gr.HTML('
Listen to Answer
') - with gr.Row(): - chat_tts_out = gr.Audio(label="Last Answer (Audio)", autoplay=True, scale=3) - chat_tts_btn = gr.Button("🔊 Play Answer", variant="secondary", scale=1) - - gr.Examples( - examples=[ - ["WASA did not fix the pipe leakage for 3 days — what are my legal rights?"], - ["Water in my area is contaminated — where should I complain?"], - ["Garbage has not been collected for a week — which law applies?"], - ["Authority ignored my complaint — what do I do next?"], - ["Pothole damaged my car — can I claim compensation?"], - ["How do I file a complaint on Pakistan Citizen Portal?"], - ], - inputs=chat_input, label="Sample Questions") - - chat_send_btn.click(fn=legal_chatbot, - inputs=[chat_input, chatbot, chat_lang_dd], - outputs=[chatbot, chat_input]) - chat_input.submit(fn=legal_chatbot, - inputs=[chat_input, chatbot, chat_lang_dd], - outputs=[chatbot, chat_input]) - chat_voice_btn.click(fn=voice_to_chat, - inputs=[chat_audio_in, chatbot, chat_lang_dd], - outputs=[chatbot, chat_input]) - chat_tts_btn.click(fn=read_last_answer, - inputs=[chatbot, chat_lang_dd], - outputs=[chat_tts_out]) - - # ════════════════════════════════════════════ - # TAB 3 — Voice Tools - # ════════════════════════════════════════════ - with gr.Tab("🎤 Voice Tools"): - gr.HTML('
Speech to Text
') - gr.HTML('
Record your complaint — transcribed automatically. Supports English, Urdu, Punjabi, Sindhi.
') - gr.HTML('
Tip: Speak clearly. Copy transcript to the complaint form.
') - audio_in = gr.Audio(type="filepath", label="Record or Upload Audio", sources=["microphone","upload"]) - stt_btn = gr.Button("Transcribe to Text", variant="primary") - stt_out = gr.Textbox(label="Transcript (editable)", lines=6, interactive=True, - placeholder="Transcribed text will appear here...") - stt_btn.click(fn=stt_transcribe, inputs=[audio_in], outputs=[stt_out]) - - gr.HTML('
Text to Speech Test
') - gr.HTML('
Test audio output in any supported language.
') - with gr.Row(): - tts_text_in = gr.Textbox(label="Enter text", placeholder="Type something...", scale=3) - tts_lang_in = gr.Dropdown(choices=LANGUAGES, value="English", label="Language", scale=1) - tts_test_btn = gr.Button("▶ Play", variant="secondary") - tts_test_out = gr.Audio(label="Audio Output", autoplay=True) - tts_test_btn.click(fn=make_tts, inputs=[tts_text_in, tts_lang_in], outputs=[tts_test_out]) - - # ════════════════════════════════════════════ - # TAB 4 — Admin - # ════════════════════════════════════════════ + + # Tab 3 - Chatbot + with gr.Tab("💬 Ask a Question"): + chat_lang = gr.Dropdown(choices=LANGUAGES, value="English", label="Response Language") + chatbot = gr.Chatbot(height=400) + msg = gr.Textbox(label="Your Question", placeholder="Ask about garbage, roads, water, or legal rights...") + send = gr.Button("Send", variant="primary") + audio_in = gr.Audio(type="filepath", label="Voice Input", sources=["microphone"]) + voice_send = gr.Button("🎤 Send Voice") + tts_btn = gr.Button("🔊 Read Last Answer") + tts_out = gr.Audio(label="Audio Answer") + + send.click(fn=legal_chatbot, inputs=[msg, chatbot, chat_lang], outputs=[chatbot, msg]) + msg.submit(fn=legal_chatbot, inputs=[msg, chatbot, chat_lang], outputs=[chatbot, msg]) + voice_send.click(fn=voice_to_chat, inputs=[audio_in, chatbot, chat_lang], outputs=[chatbot, msg]) + tts_btn.click(fn=read_last_answer, inputs=[chatbot, chat_lang], outputs=[tts_out]) + + # Tab 4 - Admin with gr.Tab("📊 Admin"): - gr.HTML('
Complaint Statistics
') - refresh_btn = gr.Button("Refresh", variant="primary") - with gr.Row(): - stats_out = gr.Markdown() - log_out = gr.Markdown() - refresh_btn.click(fn=get_admin_stats, outputs=[stats_out, log_out]) - + refresh = gr.Button("Refresh Stats", variant="primary") + stats = gr.Markdown() + logs = gr.Markdown() + refresh.click(fn=get_admin_stats, outputs=[stats, logs]) + return demo - # ═════════════════════════════════════════════════════════════�� # LAUNCH # ══════════════════════════════════════════════════════════════ @@ -1383,11 +703,6 @@ if __name__ == "__main__": demo.launch( server_name="0.0.0.0", server_port=7860, - share=False, # HuggingFace Spaces sets this automatically - css=CSS, # Gradio 6: CSS goes in launch() - theme=gr.themes.Base( - primary_hue=gr.themes.colors.green, - secondary_hue=gr.themes.colors.yellow, - ), - ssr_mode=False, # Ensures JS (dark toggle) works correctly on Spaces + css=CSS, + theme=gr.themes.Soft() ) \ No newline at end of file