diff --git "a/app.py" "b/app.py" --- "a/app.py" +++ "b/app.py" @@ -1,334 +1,285 @@ """ -Rahbar v9.0 — Pakistan AI Civic Complaint Platform -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -• Gradio 6+ compatible (css in launch, no type= on Chatbot) -• GPS: IP geolocation → shows city on map automatically -• Map: Plotly Scattermap, click-to-fill street/landmark box -• Full Pakistan coverage (not just big cities — any area) -• PDF via ReportLab (professional, no grid lines) -• Voice input/output fully working in chatbot -• Light + Dark mode CSS (auto + manual toggle) -• All UI in English; report content in selected language +Rahbar v8.1 — Pakistan AI Civic Complaint Platform +- Gradio 6+ compatible (css in launch(), no type= in Chatbot) +- GPS via IP geolocation (requests → ipinfo.io, no JS/Selenium) +- Scattermap (not Scattermapbox) for Plotly +- English UI, other languages optional for report content +- PDF via ReportLab (professional, no grid lines) +- Map via gr.Plot (Plotly Scattermap) +- Voice input/output fully working +- Light + Dark mode CSS """ import os, io, re, uuid, base64, datetime, urllib.parse from PIL import Image import gradio as gr +# ── ReportLab imports ────────────────────────────────────────── +from reportlab.lib.pagesizes import A4 +from reportlab.lib import colors +from reportlab.lib.units import inch +from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle +from reportlab.lib.enums import TA_LEFT, TA_CENTER, TA_RIGHT +from reportlab.platypus import (SimpleDocTemplate, Paragraph, Spacer, + Table, TableStyle, HRFlowable) + GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY", "") GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "") -complaint_log = [] + +complaint_log = [] # ══════════════════════════════════════════════════════════════ -# IP GEOLOCATION (pure Python — no browser permissions needed) +# GPS / IP GEOLOCATION (pure Python — no JS, no Selenium) # ══════════════════════════════════════════════════════════════ 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. + Fetch approximate location using IP geolocation. + Returns (lat, lon, city, region) or None on failure. + Tries ipinfo.io first, then ip-api.com as fallback. """ + import requests + + # ── Provider 1: ipinfo.io ──────────────────────────────── try: - import requests - r = requests.get("https://ipinfo.io/json", timeout=6) + r = requests.get("https://ipinfo.io/json", timeout=5) if r.status_code == 200: - d = r.json() - loc = d.get("loc", "") + data = r.json() + loc = data.get("loc", "") if loc and "," in loc: lat, lon = map(float, loc.split(",")) - return lat, lon, d.get("city","Unknown"), d.get("region","Unknown") + city = data.get("city", "Unknown") + region = data.get("region", "Unknown") + return lat, lon, city, region except Exception: pass - try: - import requests - r = requests.get("http://ip-api.com/json/", timeout=6) - 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: - pass - return None - -def reverse_geocode(lat, lon): - """Nominatim reverse geocode — returns street/area string or coordinate fallback.""" + # ── Provider 2: ip-api.com (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) + r = requests.get("http://ip-api.com/json/", timeout=5) 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}" + data = r.json() + if data.get("status") == "success": + return ( + float(data["lat"]), + float(data["lon"]), + data.get("city", "Unknown"), + data.get("regionName", "Unknown"), + ) except Exception: pass - return f"{lat:.5f}, {lon:.5f}" + return None # Both providers failed -def gps_detect(city_hint): + +def gps_locate_and_update(city_value): """ - Called when user presses 'Detect My Location'. - Returns (map_fig, status_md, location_text, lat_state, lon_state). + Called when user clicks 'Detect My Location'. + Returns (map_figure, status_message, lat, lon). + If detection fails, falls back to selected city centre. """ 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 + lat, lon, detected_city, detected_region = result + status = ( + f"📍 Location detected via IP: **{detected_city}, {detected_region}** " + f"(lat {lat:.4f}, lon {lon:.4f}). " + f"*Note: IP geolocation is approximate (~city level).*" + ) + fig = create_map(city_value, detected_city, lat=lat, lon=lon) + return fig, status, 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) + clat, clon = CITY_COORDS.get(city_value, (31.5204, 74.3587)) + status = ( + "⚠️ Could not detect location automatically. " + "Showing city centre. Please enter your street/area manually." + ) + fig = create_map(city_value) + return fig, status, clat, clon # ══════════════════════════════════════════════════════════════ -# PLOTLY MAP (Scattermap — Gradio 6 safe, no mapbox token) -# ══════════════════════════════════════════════════════════════ -PAKISTAN_CENTRE = (30.3753, 69.3451) - -def build_map(lat, lon, label="", zoom=14): - try: - import plotly.graph_objects as go - except ImportError: - 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) - - -# ══════════════════════════════════════════════════════════════ -# KNOWLEDGE BASE +# RAG 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"}, + { + "id": "garbage_001", "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": "garbage_002","category": "Garbage", + "title": "Urban Solid Waste — City-level Responsibility", + "content": "Failure to collect garbage is a serious violation. EPA 1997 Section 11 prohibits pollution. 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 Lahore / KMC Karachi", + "response_time": "48 hours","fine": "Rs. 500 – 50,000", + }, + { + "id": "garbage_escalation","category": "Garbage", + "title": "Garbage Complaint Escalation Ladder", + "content": "If authority fails: 1.Contact Union Council 2.Apply at DC office 3.CM Cell 0800-02345 4.citizenportal.gov.pk 5.Federal Ombudsman 051-9204551 6.High Court Writ. Compensation possible 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": "pothole_001","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 covers LDA and C&W. Vehicle damage = compensation claim. NHA: 051-9032800. LDA: 042-99230215.", + "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": "pothole_002","category": "Pot Hole", + "title": "Road Accident Due to Pothole — Legal Recourse", + "content": "If accident: 1.File police report 2.Photograph with date 3.Written notice to NHA/LDA 4.Negligence claim under Tort Law 5.Federal Ombudsman 051-9204551 6.High Court Writ. Reports at nha.gov.pk.", + "laws": ["Tort Law Negligence","NHA Safety Ordinance 2000","Constitution Article 9"], + "hotline": "051-9204551","authority": "Federal Ombudsman / High Court", + "response_time": "Court timeline","fine": "Compensation for injury/damage", + }, + { + "id": "water_001","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. Supreme Court 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": "water_escalation","category": "Pipe Leakage", + "title": "WASA Did Not Act — Escalation Steps", + "content": "If WASA fails: 1.Call WASA helpline 2.Written application at WASA office 3.DC office 4.CM Cell 0800-02345 5.citizenportal.gov.pk 6.PWA 051-9246150 7.Federal Ombudsman 8.High Court. Keep evidence.", + "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": "rights_001","category": "General", + "title": "Fundamental Rights of Pakistani Citizens", + "content": "Article 9: Right to Life includes clean environment. Article 14: Dignity. Article 19A: Right to Information. Citizen Portal complaints 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": "rights_002","category": "General", + "title": "How to File a Civic Complaint — Complete Guide", + "content": "1.Photograph with date/time 2.Note exact location 3.Call helpline get number 4.If no action in 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": "rights_003","category": "General", + "title": "Federal Ombudsman — Role and Process", + "content": "The Federal Ombudsman (Wafaqi Mohtasib) hears complaints against government institutions. Free to file. Decision within 60 days. Phone: 051-9204551 | mohtasib.gov.pk. Can appeal to President of Pakistan.", + "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: +# ══════════════════════════════════════════════════════════════ +# RAG ENGINE +# ══════════════════════════════════════════════════════════════ +class RAGEngine: def __init__(self): - self.documents = RAG_DOCUMENTS + self.documents = RAG_DOCUMENTS self.vectorizer = None self.doc_matrix = None - self._ready = False + self._initialized = False def initialize(self): - if self._ready: return True + if self._initialized: + return True try: from sklearn.feature_extraction.text import TfidfVectorizer corpus = [ - f"{d['title']} {d['content']} {' '.join(d['laws'])} {d['category']}" + f"{d['title']} {d['content']} {' '.join(d.get('laws',[]))} " + f"{d.get('category','')} {d.get('hotline','')} {d.get('authority','')}" for d in self.documents ] self.vectorizer = TfidfVectorizer( analyzer='char_wb', ngram_range=(2,5), - max_features=8000, sublinear_tf=True, min_df=1) + max_features=8000, sublinear_tf=True, min_df=1 + ) self.doc_matrix = self.vectorizer.fit_transform(corpus) - self._ready = True; return True + self._initialized = True + return True except Exception as e: - print(f"KE init error: {e}"); return False + print(f"RAG 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"], + if not self._initialized: + if not self.initialize(): + return self._keyword_fallback(query, top_k) + 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] + top_idx = np.argsort(scores)[::-1][:top_k] + results = [] + for idx in top_idx: + if scores[idx] > 0.01: + doc = self.documents[idx].copy() + doc['relevance_score'] = float(scores[idx]) + results.append(doc) + return results if results else self._keyword_fallback(query, top_k) + except Exception: + return self._keyword_fallback(query, top_k) + + def _keyword_fallback(self, query, top_k=3): + q = query.lower() + keywords = { + "Garbage": ["garbage","waste","sanitation","trash","1139"], + "Pot Hole": ["pothole","pot hole","road","nha"], + "Pipe Leakage": ["water","wasa","pipe","leakage","contaminated"], } - 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] + found_cat = None + for cat, kws in keywords.items(): + if any(kw in q for kw in kws): + found_cat = cat; break + matched = [d for d in self.documents if found_cat and d['category'] == found_cat] + for d in self.documents: + if d['category'] == 'General' and d not in matched: + matched.append(d) + return matched[:top_k] if matched else self.documents[:top_k] def format_context(self, docs): - if not docs: return "" + 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") + for i, doc in enumerate(docs, 1): + ctx += (f"[{i}] {doc['title']}\n" + f"Content: {doc['content'][:400]}\n" + f"Laws: {', '.join(doc['laws'][:2])}\n" + f"Helpline: {doc['hotline']} | Response: {doc['response_time']}\n\n") return ctx -ke = KnowledgeEngine() -ke.initialize() +rag_engine = RAGEngine() +rag_engine.initialize() # ══════════════════════════════════════════════════════════════ # STATIC DATA # ══════════════════════════════════════════════════════════════ -# Major cities with coordinates — but the app works for ANY -# Pakistani location via Nominatim geocoding +CITIES_AREAS = { + "Lahore": ["Model Town","DHA","Gulberg","Johar Town","Bahria Town","Township","Cantonment"], + "Karachi": ["Clifton","DHA","Gulshan-e-Iqbal","PECHS","Korangi","Saddar","North Nazimabad"], + "Islamabad": ["F-7","F-8","F-10","G-9","G-10","G-11","Blue Area"], + "Rawalpindi": ["Saddar","Bahria Town","Chaklala","Satellite Town","Murree Road"], + "Faisalabad": ["Jinnah Colony","Madina Town","Peoples Colony","Ghulam Muhammad Abad","Susan Road"], + "Multan": ["Shah Rukn-e-Alam","Cantt","Gulgasht Colony","New Multan","Bosan Road"], + "Peshawar": ["Hayatabad","University Town","Cantt","Saddar","Gulbahar"], + "Quetta": ["Satellite Town","Jinnah Town","Cantt","Sariab Road","Brewery Road"], +} + CITY_COORDS = { "Lahore": (31.5204, 74.3587), "Karachi": (24.8607, 67.0011), @@ -338,137 +289,77 @@ CITY_COORDS = { "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} LEGAL_KB = { "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", + "Pakistan Environmental Protection Act 1997 (Section 11)", + "Punjab Local Government Act 2022 (Schedule II – Sanitation Duties)", + "Pakistan Penal Code Section 268 – Public Nuisance", + ], + "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 of Pakistan, Article 9 & 14)", + "Right to file FIR under PPC Section 268 if authority fails to act", + "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", + "dataset_ref": "Punjab SWMB | Urban Issues Dataset", }, "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", + "Punjab Local Government Act 2022 (Section 54 – Road Maintenance)", + "Motor Vehicles Ordinance 1965 (Road Authority Liability)", + "Tort Law – Negligence (Pakistani courts)", + ], + "fine": "Authority liable for vehicle damage & personal injury", + "authority": "National Highway Authority (NHA) / C&W Department / LDA", + "hotline": "051-9032800", + "response": "72 hours", + "citizen_rights": [ + "Right to claim compensation for vehicle damage or personal injury", + "Right to lodge complaint with Federal Ombudsman", + "Right to file High Court writ petition for dereliction of duty", + "Right to written notice to NHA/LDA", + ], + "escalation": "Federal Ombudsman: 051-9204551 | nha.gov.pk", + "dataset_ref": "NHA Road Quality Reports | Road Issues Detection Dataset", }, "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 (Section 23 – Supply Obligation)", + "WASA Act – Water & Sanitation Agency Bylaws", + "Pakistan Environmental Protection Act 1997 (Section 13)", + "Punjab Local Government Act 2022 (Water & Sewerage Schedules)", + "Constitution of Pakistan Article 9 – Right to Life", + ], + "fine": "Compensatory damages + Rs. 10,000 – 5,00,000", + "authority": "WASA / Pakistan Water Authority", + "hotline": "042-99200300", + "response": "24 hours", + "citizen_rights": [ + "Right to safe drinking water (Supreme Court ruling 2018 – PLD 2018 SC 1)", + "Right to compensation for property damage from water leakage", + "Right to disconnect billing if water supply is contaminated", + "Right to file complaint with Pakistan Water Authority (PWA)", + ], + "escalation": "Pakistan Water Authority: 051-9246150 | CM Portal: 0800-02345", + "dataset_ref": "WASA Annual Reports | Consumer Complaints Dataset", }, } -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."}, -} +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} # ══════════════════════════════════════════════════════════════ # YOLO DETECTION @@ -479,23 +370,27 @@ def detect_with_yolo(image_pil, issue_type): 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 + result = results[0] + names = model.names + detected, severity = [], 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) + cls_id = int(box.cls[0]); conf = float(box.conf[0]) + detected.append(f"{names.get(cls_id, f'class_{cls_id}')} ({conf:.0%})") + if issue_type == "Garbage" and cls_id in WASTE_CLASS_IDS: + severity = min(10, severity + 2) + elif issue_type in ("Pot Hole", "Pipe Leakage"): + severity = min(10, severity + 1) + annotated = Image.fromarray(result.plot()) + summary = (f"Detected {len(detected)} object(s): {', '.join(detected[:5])}" + if detected else "No specific objects detected.") + return annotated, summary, max(severity, 3) except ImportError: - return image_pil, "Detection library not available.", 5 + return image_pil, "Object detection library not available.", 5 except Exception as e: return image_pil, f"Detection error: {e}", 5 # ══════════════════════════════════════════════════════════════ -# GEMINI +# GEMINI VISION # ══════════════════════════════════════════════════════════════ def analyze_with_gemini(image_pil, issue, location, city, yolo_summary): if not GOOGLE_API_KEY: @@ -503,637 +398,884 @@ def analyze_with_gemini(image_pil, issue, location, city, yolo_summary): 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() + model = genai.GenerativeModel("gemini-3-flash-preview") + buf = io.BytesIO() + image_pil.save(buf, format="JPEG") + prompt = ( + f"You are a STRICT Pakistani Civic Issue Inspector.\n" + f"REPORTED ISSUE: '{issue}' | CITY: {city} | LOCATION: {location}\n" + f"DETECTION: {yolo_summary}\n" + f"Garbage=actual waste/litter, Pot Hole=visible road hole, Pipe Leakage=water from pipe.\n" + f"Respond ONLY in this format:\n" + f"STATUS: [APPROVED or REJECTED]\n" + f"REASON: [2-3 sentences]\n" + f"SEVERITY: [1-10]\n" + f"CONFIDENCE: [XX%]\n" + f"RECOMMENDED_ACTION: [one sentence]" + ) + image_part = {"mime_type": "image/jpeg", + "data": base64.b64encode(buf.getvalue()).decode()} + return model.generate_content([prompt, image_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")]: +def parse_gemini_response(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() + r[key] = v.upper() if key == "status" else (int(v) if key == "severity" else 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 # ══════════════════════════════════════════════════════════════ -# LEGAL ADVICE +# LEGAL ADVICE (LLM) # ══════════════════════════════════════════════════════════════ -def get_legal_advice(issue, location, city, yolo_s, severity, language="English"): +def analyze_with_llama(issue, location, city, yolo_summary, 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.") + lang_map = { + "Urdu": "Respond entirely in Urdu script.", + "Punjabi": "Respond in Punjabi Shahmukhi script.", + "Sindhi": "Respond in Sindhi script.", + } + lang_instruction = lang_map.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)") + rights = "\n".join(f" • {r}" for r in kb.get("citizen_rights", [])) + return ( + "Applicable Laws:\n" + "\n".join(f" • {l}" for l in kb.get("laws", [])) + + f"\n\nCitizen Rights:\n{rights}" + f"\n\nFine / Penalty: {kb.get('fine', 'N/A')}" + f"\nAuthority Helpline: {kb.get('hotline', 'N/A')}" + f"\nRequired Response Time: {kb.get('response', 'N/A')}" + f"\n\nEscalation: {kb.get('escalation', 'N/A')}" + "\n\n(Configure API key for AI-generated 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( + client = Groq(api_key=GROQ_API_KEY) + prompt = ( + f"You are a Pakistani civic law expert.\n" + f"{lang_instruction}\n" + f"Complaint: {issue} in {location}, {city} | Severity: {severity}/10\n" + f"Applicable Laws: {', '.join(kb.get('laws', []))}\n" + f"Required Response Time: {kb.get('response', '72 hours')}\n\n" + f"Provide:\n" + f"1. Specific legal rights (cite law names/sections)\n" + f"2. Exact numbered steps to file a formal complaint\n" + f"3. What to do if authority does not respond in time\n" + f"4. Possible compensation or legal action available\n" + f"5. Relevant helplines and escalation contacts\n" + f"Keep it concise and practical for an ordinary Pakistani citizen." + ) + resp = client.chat.completions.create( model="llama-3.3-70b-versatile", - messages=[{"role":"user","content":prompt}], max_tokens=700) + 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}" # ══════════════════════════════════════════════════════════════ -# CHATBOT +# RAG CHATBOT — Gradio 6 messages format # ══════════════════════════════════════════════════════════════ -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}") +def legal_chatbot_rag(user_message, history, language): + """ + history is a list of {"role": "user"|"assistant", "content": str} + (Gradio 6 messages format — no type= parameter needed on Chatbot). + """ + if history is None: + history = [] + if not user_message.strip(): + return history, "" + + retrieved_docs = rag_engine.retrieve(user_message, top_k=3) + rag_context = rag_engine.format_context(retrieved_docs) + + lang_map = { + "Urdu": "Respond entirely in Urdu script.", + "Punjabi": "Respond in Punjabi Shahmukhi script.", + "Sindhi": "Respond in Sindhi script.", + } + lang_instruction = lang_map.get(language, "Respond in clear professional English.") + + system_content = ( + f"You are Rahbar Legal Assistant — a civic rights advisor for Pakistani citizens.\n" + f"{lang_instruction}\n" + f"Only discuss: water, pipe leakage, WASA, garbage, roads, potholes, Pakistani civic law.\n" + f"Always cite specific laws and provide helpline numbers. Max 250 words per response.\n\n" + f"Knowledge Base:\n{rag_context}" + ) + 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}], "" + if retrieved_docs: + doc = retrieved_docs[0] + answer = (f"**{doc['title']}**\n\n{doc['content'][:500]}\n\n" + f"Helpline: {doc['hotline']} | Response Time: {doc['response_time']}\n" + f"Laws: {', '.join(doc['laws'][:2])}\n\n" + f"_(Configure API key for full AI-powered responses)_") + else: + answer = "I can help with water, garbage, and road issues in Pakistan. Please ask a specific civic question." + new_history = history + [ + {"role": "user", "content": user_message}, + {"role": "assistant", "content": answer}, + ] + return new_history, "" + 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])}_" + client = Groq(api_key=GROQ_API_KEY) + api_messages = [{"role": "system", "content": system_content}] + # Replay last 8 turns + for msg in history[-16:]: + api_messages.append({"role": msg["role"], "content": msg["content"]}) + api_messages.append({"role": "user", "content": user_message}) + resp = client.chat.completions.create( + model="llama-3.3-70b-versatile", + messages=api_messages, + max_tokens=500 + ) + answer = resp.choices[0].message.content.strip() + if retrieved_docs: + refs = [f"[{d['title'][:40]}]" for d in retrieved_docs[:2]] + answer += f"\n\n_Sources: {' | '.join(refs)}_" except Exception as e: - ans = f"Error: {e}" - return history+[{"role":"user","content":user_message},{"role":"assistant","content":ans}], "" + answer = f"Sorry, there was an error: {e}" + + new_history = history + [ + {"role": "user", "content": user_message}, + {"role": "assistant", "content": answer}, + ] + return new_history, "" -def read_last_answer(history, language): - """Find last assistant message and convert to speech.""" - if not history: return None +def chatbot_tts_output(history, language): + if not history: + return None + # history is list of dicts in messages format 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) + if msg.get("role") == "assistant": + text = re.sub(r'_Sources:.*?_', '', msg["content"], flags=re.DOTALL).strip() + 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, "" - # ══════════════════════════════════════════════════════════════ # TTS # ══════════════════════════════════════════════════════════════ def make_tts(text, language): 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) + lang_code = LANG_CODES.get(language, "en") + tts = gTTS(text=str(text)[:600], lang=lang_code, slow=False) path = f"/tmp/tts_{uuid.uuid4().hex[:8]}.mp3" - tts.save(path); return path + 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 + 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 +def stt(audio_file): + if audio_file is None: + return "No audio received. Please record or upload audio first." + + def ensure_wav(path): + if path.lower().endswith(".wav"): + return path try: from pydub import AudioSegment - out=p+"_c.wav"; AudioSegment.from_file(p).export(out,format="wav"); return out - except: return p + out = path + "_converted.wav" + AudioSegment.from_file(path).export(out, format="wav") + return out + except Exception: + return path + 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" + client = Groq(api_key=GROQ_API_KEY) + wav_path = ensure_wav(audio_file) + with open(wav_path, "rb") as f: + result = client.audio.transcriptions.create( + model="whisper-large-v3", file=f, response_format="text" + ) + text = result if isinstance(result, str) else result.text + return text.strip() or "No speech detected in audio." + except Exception as e: + groq_err = str(e) + else: + groq_err = "API key not configured" + 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) + wav_path = ensure_wav(audio_file) + recognizer = sr.Recognizer() + with sr.AudioFile(wav_path) as src: + recognizer.adjust_for_ambient_noise(src, duration=0.3) + audio_data = recognizer.record(src) + return recognizer.recognize_google(audio_data) except Exception as e2: - return f"Transcription failed. Primary: {groq_err}. Fallback: {e2}" + return f"Transcription failed. Error: {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") + kb = LEGAL_KB.get(issue, {}) + rights = "\n".join(f" - {r}" for r in kb.get("citizen_rights", [])) + out = f"## Legal Reference: {issue}\n\n### Applicable Laws\n" + for law in kb.get("laws", []): + out += f" - {law}\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### Official Helpline\n**{kb.get('hotline','N/A')}**\n" + f"\n### Mandatory Response Time\n{kb.get('response','N/A')}\n" + f"\n### Citizen Rights\n{rights}\n" + f"\n### Escalation Path\n{kb.get('escalation','N/A')}\n" + f"\n---\n*Source: {kb.get('dataset_ref','Pakistani civic law databases')}*" + ) return out # ══════════════════════════════════════════════════════════════ -# ADMIN +# ADMIN STATS # ══════════════════════════════════════════════════════════════ 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=[] + total = len(complaint_log) + if total == 0: + return "No complaints filed yet.", "" + counts = {"Garbage": 0, "Pot Hole": 0, "Pipe Leakage": 0} + cities, severities = {}, [] 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" + issue = c.get("issue", "") + counts[issue] = counts.get(issue, 0) + 1 + city = c.get("city", "Unknown") + cities[city] = cities.get(city, 0) + 1 + severities.append(c.get("severity", 5)) + avg_sev = sum(severities) / len(severities) if severities else 0 + top_city = max(cities, key=cities.get) if cities else "N/A" + stats_md = ( + f"## Dashboard Summary\n" + f"| Metric | Value |\n|--------|-------|\n" + f"| Total Complaints | **{total}** |\n" + f"| Average Severity | **{avg_sev:.1f}/10** |\n" + f"| Most Active City | **{top_city}** |\n\n" + f"### By Issue Type\n| Issue | Count |\n|-------|-------|\n" + f"| Garbage | {counts['Garbage']} |\n" + f"| Pot Hole | {counts['Pot Hole']} |\n" + f"| Pipe Leakage | {counts['Pipe Leakage']} |\n\n" + f"### By City\n" + ) + for city, cnt in sorted(cities.items(), key=lambda x: -x[1]): + stats_md += f"| {city} | {cnt} |\n" + log_md = "## 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 + log_md += (f"**{c['id']}** | {c['timestamp']} | {c['city']}, {c['location']} | " + f"{c['issue']} | Severity {c['severity']}/10 | {c.get('name','N/A')}\n\n") + return stats_md, log_md -def sev_label(s): return "LOW" if s<=3 else ("MEDIUM" if s<=6 else ("HIGH" if s<=8 else "CRITICAL")) +def severity_label(score): + if score <= 3: return "LOW" + if score <= 6: return "MEDIUM" + if score <= 8: return "HIGH" + return "CRITICAL" def update_areas(city): - """Not used anymore — we use free-text location instead of fixed areas.""" - return city + areas = CITIES_AREAS.get(city, ["Enter area"]) + return gr.Dropdown(choices=areas, value=areas[0]) # ══════════════════════════════════════════════════════════════ -# PDF GENERATION (ReportLab — professional, no grid lines) +# PLOTLY MAP — Scattermap (not Scattermapbox, Gradio 6 safe) # ══════════════════════════════════════════════════════════════ -def generate_pdf(cid, ts, name, cnic, phone, city, location, issue_type, - language, severity, g_status, g_reason, g_conf, kb, - description, advice): +def create_map(city, location_text="", lat=None, lon=None): + """Return a Plotly figure using Scattermap (non-deprecated API).""" 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) - - 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 + import plotly.graph_objects as go + except ImportError: + return None - 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 + clat, clon = CITY_COORDS.get(city, (31.5204, 74.3587)) + mlat = lat if lat is not None else clat + mlon = lon if lon is not None else clon + label = location_text if location_text.strip() else city - 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 + fig = go.Figure(go.Scattermap( + lat=[mlat], + lon=[mlon], + mode="markers+text", + marker=dict(size=16, color="#e8410a"), + text=[label], + textposition="top right", + hovertemplate=f"{label}
Lat: {mlat:.4f}
Lon: {mlon:.4f}", + )) + fig.update_layout( + map=dict( + style="open-street-map", + center=dict(lat=mlat, lon=mlon), + zoom=13, + ), + margin=dict(r=0, t=0, l=0, b=0), + height=320, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + ) + return fig + +def update_map_on_city(city): + return create_map(city) + +def update_map_on_location(city, area, location_text): + return create_map(city, location_text or area) + +# ══════════════════════════════════════════════════════════════ +# PDF GENERATION +# ══════════════════════════════════════════════════════════════ +def generate_pdf_report(complaint_id, timestamp, name, cnic, phone, city, location, + issue_type, language, severity, gemini_status, gemini_reason, + gemini_confidence, kb, description, llama_advice): + try: + pdf_path = f"/tmp/rahbar_report_{complaint_id}.pdf" + doc = SimpleDocTemplate( + pdf_path, pagesize=A4, + rightMargin=0.75*inch, leftMargin=0.75*inch, + topMargin=0.75*inch, bottomMargin=0.75*inch + ) + + C_DARK_GREEN = colors.HexColor("#1a5c3f") + C_MID_GREEN = colors.HexColor("#25a06b") + C_LIGHT_GREEN = colors.HexColor("#eaf5ef") + C_GOLD = colors.HexColor("#c8860a") + C_GOLD_LIGHT = colors.HexColor("#fef9ee") + C_TEXT = colors.HexColor("#0d2b1e") + C_MUTED = colors.HexColor("#5a8a6e") + C_WHITE = colors.white + SEV_COLORS = { + "LOW": colors.HexColor("#27ae60"), + "MEDIUM": colors.HexColor("#f39c12"), + "HIGH": colors.HexColor("#e67e22"), + "CRITICAL": colors.HexColor("#c0392b"), + } - def sp(h=0.15): return Spacer(1,h*inch) + def PS(name, **kw): + return ParagraphStyle(name, **kw) + + sHeadWhite = PS("hw", fontName="Helvetica-Bold", fontSize=18, textColor=C_WHITE, + alignment=TA_CENTER, leading=24, spaceAfter=2) + sSubWhite = PS("sw", fontName="Helvetica", fontSize=10, textColor=colors.HexColor("#b8e8cc"), + alignment=TA_CENTER, leading=14, spaceAfter=2) + sRefWhite = PS("rw", fontName="Helvetica", fontSize=8, textColor=colors.HexColor("#a8d8c0"), + alignment=TA_CENTER, spaceAfter=0) + sSecHead = PS("sec", fontName="Helvetica-Bold", fontSize=10, textColor=C_WHITE, + leading=14, spaceAfter=0) + sSevBadge = PS("sev", fontName="Helvetica-Bold", fontSize=11, textColor=C_WHITE, + alignment=TA_CENTER, leading=16) + sLabel = PS("lbl", fontName="Helvetica-Bold", fontSize=8.5, textColor=C_MUTED, leading=12) + sValue = PS("val", fontName="Helvetica", fontSize=9.5, textColor=C_TEXT, leading=14) + sBody = PS("bod", fontName="Helvetica", fontSize=9, textColor=C_TEXT, leading=13, spaceAfter=3) + sBodyI = PS("bi", fontName="Helvetica-Oblique", fontSize=9, textColor=colors.HexColor("#2d5a3e"), leading=13) + sBullet = PS("bul", fontName="Helvetica", fontSize=9, textColor=C_TEXT, leading=13, leftIndent=12) + sGoldDir = PS("gd", fontName="Helvetica-Bold", fontSize=10, textColor=C_WHITE, alignment=TA_CENTER, leading=15) + sFooter = PS("ft", fontName="Helvetica", fontSize=7.5, textColor=C_WHITE, alignment=TA_CENTER, leading=11) + sDecl = PS("dc", fontName="Helvetica", fontSize=9, textColor=C_TEXT, leading=13) + + W = 7.0 * inch + + def sec_header(letter, title): + t = Table([[Paragraph(f" {letter}. {title.upper()}", sSecHead)]], colWidths=[W]) + t.setStyle(TableStyle([ + ("BACKGROUND", (0,0),(-1,-1), C_DARK_GREEN), + ("TOPPADDING", (0,0),(-1,-1), 6), + ("BOTTOMPADDING", (0,0),(-1,-1), 6), + ("LEFTPADDING", (0,0),(-1,-1), 10), + ])) + return t - story=[] + def info_grid(pairs): + rows = [] + row = [] + for i, (lbl, val) in enumerate(pairs): + row.extend([Paragraph(lbl, sLabel), Paragraph(str(val), sValue)]) + if len(row) == 4 or i == len(pairs) - 1: + while len(row) < 4: + row.extend([Paragraph("", sLabel), Paragraph("", sValue)]) + rows.append(row) + row = [] + t = Table(rows, colWidths=[2.0*inch, 1.5*inch, 2.0*inch, 1.5*inch]) + t.setStyle(TableStyle([ + ("BACKGROUND", (0,0),(-1,-1), C_LIGHT_GREEN), + ("TOPPADDING", (0,0),(-1,-1), 5), + ("BOTTOMPADDING", (0,0),(-1,-1), 5), + ("LEFTPADDING", (0,0),(-1,-1), 6), + ("RIGHTPADDING", (0,0),(-1,-1), 6), + ("VALIGN", (0,0),(-1,-1), "TOP"), + ("ROWBACKGROUNDS",(0,0),(-1,-1), [C_LIGHT_GREEN, C_WHITE]), + ])) + return t - # 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)] + def text_card(paras, bg=None): + bg = bg or C_LIGHT_GREEN + rows = [[p] for p in paras] + t = Table(rows, 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), + ("VALIGN", (0,0),(-1,-1), "TOP"), + ])) + return t - # 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)] + def sp(h=0.15): + return Spacer(1, h * inch) - 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 = [] + date_str = datetime.datetime.now().strftime("%d %B %Y") + time_str = datetime.datetime.now().strftime("%I:%M %p") + sev_lbl = severity_label(severity) - story+=[sec("B","Complaint Details"),sp(0.08)] - story+=[grid([("Issue Type",issue_type),("Location",location[:50]),("Date",date_str),("Time",time_str)])] + header_rows = [ + [Paragraph("GOVERNMENT OF PAKISTAN", sHeadWhite)], + [Paragraph("CIVIC COMPLAINT REPORT", sHeadWhite)], + [Paragraph("Rahbar Digital Civic Redressal System", sSubWhite)], + [Paragraph(f"Reference: {complaint_id} | {date_str} at {time_str} | Language: {language}", sRefWhite)], + ] + h_t = Table(header_rows, colWidths=[W]) + h_t.setStyle(TableStyle([ + ("BACKGROUND", (0,0),(-1,-1), C_DARK_GREEN), + ("TOPPADDING", (0,0),(-1,-1), 10), + ("BOTTOMPADDING", (0,0),(-1,-1), 10), + ("LEFTPADDING", (0,0),(-1,-1), 14), + ("RIGHTPADDING", (0,0),(-1,-1), 14), + ])) + story += [h_t, sp(0.12)] + + sev_color = SEV_COLORS.get(sev_lbl, C_MID_GREEN) + sev_t = Table( + [[Paragraph(f"SEVERITY: {severity}/10 — {sev_lbl}", sSevBadge)]], + colWidths=[W] + ) + sev_t.setStyle(TableStyle([ + ("BACKGROUND", (0,0),(-1,-1), sev_color), + ("TOPPADDING", (0,0),(-1,-1), 8), + ("BOTTOMPADDING", (0,0),(-1,-1), 8), + ])) + story += [sev_t, sp(0.18)] + + story += [sec_header("A", "Complainant Information"), sp(0.08)] + story += [info_grid([ + ("Full Name", name), ("CNIC", cnic), + ("Phone", phone or "N/A"),("City", city), + ]), sp(0.15)] + + story += [sec_header("B", "Complaint Details"), sp(0.08)] + story += [info_grid([ + ("Issue Type", issue_type), ("Location", location), + ("Date Filed", date_str), ("Time Filed", 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)] + story += [sp(0.08), + text_card([Paragraph(f"Description: {description.strip()}", sBodyI)])] + story += [sp(0.15)] + + story += [sec_header("C", "Verification Results"), sp(0.08)] + ai_bg = colors.HexColor("#e6f7ed") if "APPROVED" in gemini_status else colors.HexColor("#fdecea") + story += [text_card([ + Paragraph(f"Status: {gemini_status} | Confidence: {gemini_confidence}", sBody), + Paragraph(f"Assessment: {gemini_reason}", sBody), + ], bg=ai_bg), sp(0.15)] + + story += [sec_header("D", "Legal Framework & Applicable Laws"), sp(0.08)] + story += [info_grid([ + ("Responsible Authority", kb.get("authority", "N/A")), + ("Official 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}. {law}", sBullet)] + for i, law 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)])) + lt = Table(law_rows, colWidths=[W]) + lt.setStyle(TableStyle([ + ("BACKGROUND", (0,0),(-1,-1), C_LIGHT_GREEN), + ("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 += [sp(0.15)] + + story += [sec_header("E", "Citizen's Legal Rights"), sp(0.08)] + rights_rows = [[Paragraph(f"✓ {r}", sBullet)] + for r in kb.get("citizen_rights", [])] + if rights_rows: + rt = Table(rights_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), [C_WHITE, C_LIGHT_GREEN]), + ])) 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 += [sp(0.08), + text_card([Paragraph( + f"Escalation Path: {kb.get('escalation', 'CM Portal: 0800-02345')}", + sBodyI)], bg=C_GOLD_LIGHT), + sp(0.15)] + + story += [sec_header("F", f"Legal Advice ({language})"), sp(0.08)] + advice_paras = [Paragraph(line.strip(), sBody) + for line in llama_advice.strip().split("\n") if line.strip()] + if advice_paras: + at = Table([[p] for p in advice_paras], colWidths=[W]) + at.setStyle(TableStyle([ + ("BACKGROUND", (0,0),(-1,-1), C_LIGHT_GREEN), + ("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)], + story += [sp(0.15)] + + story += [sec_header("G", "Mandatory Action Directive"), sp(0.08)] + dir_t = Table( + [[Paragraph(f"MANDATORY ACTION REQUIRED WITHIN: {kb.get('response','72 hours').upper()}", sGoldDir)]], + colWidths=[W] + ) + dir_t.setStyle(TableStyle([ + ("BACKGROUND", (0,0),(-1,-1), C_GOLD), + ("TOPPADDING", (0,0),(-1,-1), 9), + ("BOTTOMPADDING", (0,0),(-1,-1), 9), + ])) + story += [dir_t, sp(0.08)] + story += [info_grid([ + ("Responsible Authority", kb.get("authority","N/A")), + ("Official Helpline", kb.get("hotline","N/A")), + ("Citizen Portal", "citizenportal.gov.pk"), + ("CM Toll-Free", "0800-02345"), + ]), sp(0.18)] + + story += [sec_header("H", "Declaration & Official Use"), sp(0.08)] + inner_decl = [ + [Paragraph( + f"I, {name} (CNIC: {cnic}), declare that the information provided " + f"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])], + [Table([ + [Paragraph("Complainant Signature", sLabel), + Paragraph("Date", sLabel), + Paragraph("Reference No.", sLabel)], + [Paragraph("____________________________", sValue), + Paragraph(date_str, sValue), + Paragraph(complaint_id, sValue)], + ], 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)], + [Table([ + [Paragraph("Received By", sLabel), + Paragraph("Date of Receipt", sLabel), + Paragraph("Action Taken", sLabel), + Paragraph("Resolved On", sLabel)], + [Paragraph("______________", sValue), + Paragraph("______________", sValue), + Paragraph("______________", sValue), + Paragraph("______________", sValue)], + ], colWidths=[1.75*inch, 1.75*inch, 1.75*inch, 1.75*inch])], ] - 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)])) + decl_outer = Table(inner_decl, colWidths=[W]) + decl_outer.setStyle(TableStyle([ + ("BACKGROUND", (0,0),(-1,-1), C_LIGHT_GREEN), + ("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_outer, sp(0.18)] + + foot_t = Table( + [[Paragraph( + f"Generated by Rahbar — Pakistan's Civic Redressal Platform | " + f"{timestamp} | {complaint_id}", + sFooter)]], + colWidths=[W] + ) + foot_t.setStyle(TableStyle([ + ("BACKGROUND", (0,0),(-1,-1), C_DARK_GREEN), + ("TOPPADDING", (0,0),(-1,-1), 7), + ("BOTTOMPADDING", (0,0),(-1,-1), 7), + ])) story.append(foot_t) doc.build(story) - return path + return pdf_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 + return None # ══════════════════════════════════════════════════════════════ -# MAIN REPORT +# WHATSAPP LINK +# ══════════════════════════════════════════════════════════════ +def make_whatsapp_link(text): + return f"https://wa.me/?text={urllib.parse.quote(text[:1000])}" + +# ══════════════════════════════════════════════════════════════ +# 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 - - 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) + if image is None: + return None, "Please upload an image of the issue.", "", "", None, "", None, None, None + if not location.strip(): + return None, "Please enter the complaint 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 + + complaint_id = f"RB-{uuid.uuid4().hex[:8].upper()}" + timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + + annotated_img, yolo_summary, yolo_severity = detect_with_yolo(image, issue_type) + gemini_raw = analyze_with_gemini(image, issue_type, location, city, yolo_summary) + gemini_parsed = parse_gemini_response(gemini_raw) + gemini_status = gemini_parsed["status"] + gemini_reason = gemini_parsed["reason"] + + if gemini_status == "REJECTED": + return ( + annotated_img, + f"COMPLAINT REJECTED — Verification\n\nReason: {gemini_reason}\n" + f"Confidence: {gemini_parsed.get('confidence','N/A')}\n\n" + f"Please upload a clear image of the issue ({issue_type}).\n" + f"This complaint has NOT been saved.", + "", "", None, complaint_id, None, None, None + ) + + if gemini_status == "UNKNOWN" and "GOOGLE_API_KEY not set" in gemini_raw: + gemini_reason = "Verification skipped — API key not configured." + gemini_status = "APPROVED_WITH_WARNING" + + final_severity = gemini_parsed["severity"] if gemini_status == "APPROVED" else yolo_severity + kb = LEGAL_KB.get(issue_type, {}) + sev_lbl = severity_label(final_severity) + llama_advice = analyze_with_llama( + issue_type, location, city, yolo_summary, final_severity, 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) + pdf_path = generate_pdf_report( + complaint_id, timestamp, name, cnic, phone, city, location, + issue_type, language, final_severity, + gemini_status, gemini_reason, gemini_parsed.get("confidence", "N/A"), + kb, description, llama_advice + ) - 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"{'='*55}\n" + f"Complaint Number : {complaint_id}\n" + f"Date : {datetime.datetime.now().strftime('%d %B %Y')}\n" + f"Time : {datetime.datetime.now().strftime('%I:%M %p')}\n" + f"Language : {language}\n\n" + f"SECTION A — COMPLAINANT INFORMATION\n" + f"Full Name : {name}\n" + f"CNIC : {cnic}\n" + f"Phone : {phone if phone else 'Not provided'}\n" + f"City : {city}\n" + f"Location : {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"Issue Type : {issue_type}\n" + f"Location : {location}, {city}\n" + f"Date/Time : {timestamp}\n" + f"Severity : {final_severity}/10 [{sev_lbl}]\n" + f"Description:\n{description.strip() if description.strip() else '[No additional details provided]'}\n\n" + f"SECTION C — VERIFICATION RESULTS\n" + f"Status : {gemini_status}\n" + f"Confidence : {gemini_parsed.get('confidence','N/A')}\n" + f"Assessment : {gemini_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" + + f"Laws:\n" + "\n".join(f" - {l}" for l in kb.get("laws",[])) + + f"\nAuthority : {kb.get('authority','N/A')}\n" + f"Helpline : {kb.get('hotline','N/A')}\n" + f"Response : {kb.get('response','N/A')}\n" + f"Penalty : {kb.get('fine','N/A')}\n\n" + f"SECTION E — CITIZEN'S 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}" + f"\nEscalation : {kb.get('escalation','CM Portal: 0800-02345')}\n\n" + f"MANDATORY ACTION REQUIRED 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 that the information provided is accurate.\n" + f"Reference: {complaint_id} | Generated: {timestamp}" ) - 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])})" + wa_text = ( + f"Rahbar Civic Complaint\nID: {complaint_id}\nIssue: {issue_type}\n" + f"Location: {location}, {city}\nSeverity: {final_severity}/10\n" + f"Authority: {kb.get('authority','N/A')}\nHotline: {kb.get('hotline','N/A')}\nTime: {timestamp}" + ) + wa_md = f"[📲 Share on WhatsApp]({make_whatsapp_link(wa_text)})" - 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}) + complaint_log.append({ + "id": complaint_id, "timestamp": timestamp, + "city": city, "location": location, "issue": issue_type, + "severity": final_severity, "language": language, + "name": name, "cnic": cnic, "phone": phone, + }) - report_tts=None + report_tts_path = 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) - - advice_tts = make_tts(advice[:600], language) - map_fig = build_map_city(city) + tts_text = ( + f"Complaint {complaint_id} has been filed. " + f"Issue: {issue_type}. Location: {location}, {city}. " + f"Severity: {final_severity} out of 10. " + f"The responsible authority is {kb.get('authority','')}. " + f"Helpline: {kb.get('hotline','')}." + ) + report_tts_path = make_tts(tts_text, language) - return ann, report, wa_md, advice, report_tts, cid, advice_tts, pdf_path, map_fig + advice_tts_path = make_tts(llama_advice[:600], language) if llama_advice else None + map_fig = create_map(city, location) + return (annotated_img, report, wa_md, llama_advice, + report_tts_path, complaint_id, advice_tts_path, pdf_path, map_fig) # ══════════════════════════════════════════════════════════════ -# CSS — light + dark mode, both automatic and manual +# CSS # ══════════════════════════════════════════════════════════════ 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'); -/* 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; +:root { + --bg:#ffffff; --bg2:#f5f8f6; --bg3:#e8f3ec; --surface:#ffffff; + --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; + --radius:10px; --radius-lg:18px; --header-bg:linear-gradient(135deg,#14432e 0%,#0d2b1e 60%,#091a10 100%); } -/* 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; + --bg:#0c1a10; --bg2:#132118; --bg3:#1a3024; --surface:#0c1a10; + --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; +.dark-mode{ + --bg:#0c1a10; --bg2:#132118; --bg3:#1a3024; --surface:#0c1a10; + --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%); } *,*::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; -} -/* 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;} +body,.gradio-container{font-family:'Inter',sans-serif!important;background:var(--bg)!important;color:var(--txt)!important;transition:background .3s,color .3s;} +.rh-header{background:var(--header-bg);padding:28px 20px 22px;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,3.2rem)!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,1.1rem);color:#a8e8c4;margin:4px 0 6px;} +.rh-tag{font-size:.78rem;color:#5de3a3;letter-spacing:.1em;text-transform:uppercase;} +.top-bar{display:flex;flex-wrap:wrap;align-items:center;justify-content:space-between;padding:8px 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{font-size:.68rem;font-weight:600;letter-spacing:.06em;padding:3px 10px;border-radius:20px;text-transform:uppercase;background:var(--surface);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 */ +.badge-red{color:#ff8080;border-color:rgba(255,100,100,.4);} +.dark-btn{background:transparent;border:1px solid var(--border2);border-radius:20px;padding:4px 14px;cursor:pointer;color:var(--muted);font-size:.78rem;font-weight:500;font-family:'Inter',sans-serif;transition:all .2s;} +.dark-btn:hover{background:var(--bg3);color:var(--txt);} .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 */ +.gradio-container .tab-nav button{font-family:'Inter',sans-serif!important;font-weight:500!important;font-size:.84rem!important;color:var(--muted)!important;padding:12px 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;} +.sec-title{font-size:.68rem;font-weight:700;letter-spacing:.12em;text-transform:uppercase;color:var(--green3);margin-bottom:10px;padding-bottom:7px;border-bottom:1px solid var(--border);} 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 input,.gradio-container textarea{background:var(--surface)!important;border:1px solid var(--border2)!important;border-radius:var(--radius)!important;color:var(--txt)!important;font-family:'Inter',sans-serif!important;transition:border-color .2s,box-shadow .2s;} +.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(--surface)!important;border-color:var(--border2)!important;} +.gradio-container .block{background:var(--surface)!important;} +.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(--surface)!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 */ +.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);} +.gps-box{background:var(--bg3);border:1px solid var(--border2);border-left:4px solid var(--green3);border-radius:var(--radius);padding:10px 14px;font-size:.85rem;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;} .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 */ +.gradio-container .message.bot{background:var(--bg2)!important;color:var(--txt)!important;} ::-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;} -} +@media(max-width:640px){.rh-header{padding:16px 12px;}.gradio-container .tab-nav button{padding:10px 10px!important;font-size:.74rem!important;}} """ HEADER_HTML = """
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Rahbar | رہبر
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Pakistan's Civic Complaint Platform
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Know Your Rights — File With Confidence
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Rahbar
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Pakistan's AI-Powered Civic Complaint Platform
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Serving Citizens — Enforcing Rights
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