diff --git "a/app.py" "b/app.py" --- "a/app.py" +++ "b/app.py" @@ -1,27 +1,26 @@ """ -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 +Rahbar v8.2 — Pakistan AI Civic Complaint Platform +Changes from v8.1: + ✅ Issue photo embedded in PDF report (Section B) + ✅ All Pakistan provinces + cities + rural areas/tehsils (700+ locations) + ✅ Chatbot "Play Answer" TTS fixed — reads last assistant message correctly + ✅ Chatbot source references hidden from display (shown only internally) + ✅ Voice send in chatbot fully working + ✅ All other functions identical to v8.1 """ import os, io, re, uuid, base64, datetime, urllib.parse from PIL import Image import gradio as gr -# ── ReportLab imports ────────────────────────────────────────── +# ── 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) + Table, TableStyle, HRFlowable, Image as RLImage) GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY", "") GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "") @@ -29,17 +28,10 @@ GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "") complaint_log = [] # ══════════════════════════════════════════════════════════════ -# GPS / IP GEOLOCATION (pure Python — no JS, no Selenium) +# GPS / IP GEOLOCATION # ══════════════════════════════════════════════════════════════ def get_location_from_ip(): - """ - 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: r = requests.get("https://ipinfo.io/json", timeout=5) if r.status_code == 200: @@ -47,53 +39,33 @@ def get_location_from_ip(): loc = data.get("loc", "") if loc and "," in loc: lat, lon = map(float, loc.split(",")) - city = data.get("city", "Unknown") - region = data.get("region", "Unknown") - return lat, lon, city, region + return lat, lon, data.get("city","Unknown"), data.get("region","Unknown") except Exception: pass - - # ── Provider 2: ip-api.com (fallback) ─────────────────── try: r = requests.get("http://ip-api.com/json/", timeout=5) if r.status_code == 200: data = r.json() if data.get("status") == "success": - return ( - float(data["lat"]), - float(data["lon"]), - data.get("city", "Unknown"), - data.get("regionName", "Unknown"), - ) + return float(data["lat"]), float(data["lon"]), data.get("city","Unknown"), data.get("regionName","Unknown") except Exception: pass - - return None # Both providers failed + return None def gps_locate_and_update(city_value): - """ - 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, 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).*" - ) + status = (f"📍 Location detected: **{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 = CITY_COORDS.get(city_value, (31.5204, 74.3587)) - status = ( - "⚠️ Could not detect location automatically. " - "Showing city centre. Please enter your street/area manually." - ) + clat, clon = CITY_COORDS.get(city_value, (30.3753, 69.3451)) + 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 @@ -102,86 +74,56 @@ def gps_locate_and_update(city_value): # RAG KNOWLEDGE BASE # ══════════════════════════════════════════════════════════════ RAG_DOCUMENTS = [ - { - "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", - }, + {"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 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":"g3","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":"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 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":"p2","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":"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. 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":"w2","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":"r1","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":"r2","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":"r3","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"}, ] # ══════════════════════════════════════════════════════════════ @@ -195,19 +137,12 @@ class RAGEngine: self._initialized = False def initialize(self): - if self._initialized: - return True + if self._initialized: return True try: from sklearn.feature_extraction.text import TfidfVectorizer - corpus = [ - 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 - ) + corpus = [f"{d['title']} {d['content']} {' '.join(d.get('laws',[]))} {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) self.doc_matrix = self.vectorizer.fit_transform(corpus) self._initialized = True return True @@ -237,128 +172,157 @@ class RAGEngine: 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"], - } + keywords = {"Garbage":["garbage","waste","sanitation","trash","1139"], + "Pot Hole":["pothole","pot hole","road","nha"], + "Pipe Leakage":["water","wasa","pipe","leakage","contaminated"]} found_cat = None for cat, kws in keywords.items(): - if any(kw in q for kw in kws): - found_cat = cat; break + 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) + 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, 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") + ctx += (f"[{i}] {doc['title']}\nContent: {doc['content'][:400]}\n" + f"Laws: {', '.join(doc['laws'][:2])}\nHelpline: {doc['hotline']} | Response: {doc['response_time']}\n\n") return ctx rag_engine = RAGEngine() rag_engine.initialize() # ══════════════════════════════════════════════════════════════ -# STATIC DATA +# STATIC DATA — ALL PAKISTAN (provinces + cities + rural areas) # ══════════════════════════════════════════════════════════════ -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 coordinates for map centering CITY_COORDS = { - "Lahore": (31.5204, 74.3587), - "Karachi": (24.8607, 67.0011), - "Islamabad": (33.6844, 73.0479), - "Rawalpindi": (33.5651, 73.0169), - "Faisalabad": (31.4181, 73.0776), - "Multan": (30.1575, 71.5249), - "Peshawar": (34.0151, 71.5249), - "Quetta": (30.1798, 66.9750), + # Punjab + "Lahore":(31.5204,74.3587),"Faisalabad":(31.4181,73.0776),"Rawalpindi":(33.5651,73.0169), + "Gujranwala":(32.1877,74.1945),"Multan":(30.1575,71.5249),"Sialkot":(32.4945,74.5229), + "Bahawalpur":(29.3956,71.6836),"Sargodha":(32.0836,72.6711),"Sahiwal":(30.6706,73.1064), + "Sheikhupura":(31.7167,73.9850),"Jhang":(31.2681,72.3181),"Kasur":(31.1167,74.4500), + "Okara":(30.8138,73.4544),"Gujrat":(32.5736,74.0789),"Wazirabad":(32.4435,74.1199), + "Jhelum":(32.9425,73.7257),"Khushab":(32.2979,72.3549),"Mianwali":(32.5856,71.5435), + "Bhakkar":(31.6276,71.0652),"Muzaffargarh":(30.0694,71.1933),"Dera Ghazi Khan":(30.0564,70.6349), + "Layyah":(30.9597,70.9397),"Rajanpur":(29.1040,70.3305),"Lodhran":(29.5337,71.6316), + "Vehari":(30.0449,72.3517),"Pakpattan":(30.3438,73.3881),"Toba Tek Singh":(30.9709,72.4827), + "Chiniot":(31.7189,72.9787),"Hafizabad":(32.0710,73.6880),"Narowal":(32.0966,74.8716), + "Chakwal":(32.9310,72.8524),"Attock":(33.7667,72.3583),"Rawala Kot":(33.8579,73.7610), + "Khanewal":(30.3011,71.9323),"Bahawalnagar":(29.9908,73.2548),"Nankana Sahib":(31.4502,73.7129), + "Mandi Bahauddin":(32.5865,73.4909),"Phool Nagar":(31.1669,74.0158), + # Rural Punjab + "Pindi Bhattian":(31.8953,73.2720),"Kot Addu":(30.4695,70.9636),"Sadiqabad":(28.3090,70.1310), + "Ahmadpur East":(29.1438,71.2601),"Kabirwala":(30.4021,71.8741),"Hasilpur":(29.6967,72.5596), + "Jampur":(29.6435,70.5927),"Liaquatpur":(28.9191,70.9550),"Yazman":(29.1179,71.7444), + "Uch Sharif":(29.2341,71.0918),"Chishtian":(29.7986,72.8543),"Mailsi":(29.8012,72.1671), + "Burewala":(30.1682,72.6809),"Kamalia":(30.7265,72.6466),"Jaranwala":(31.3342,73.4153), + "Pattoki":(31.0220,73.8549),"Chunian":(30.9609,73.9788),"Chichawatni":(30.5365,72.6918), + "Dinga":(32.6422,73.7220),"Khanpur":(28.6470,70.6618), + # Sindh + "Karachi":(24.8607,67.0011),"Hyderabad":(25.3960,68.3578),"Sukkur":(27.7052,68.8574), + "Larkana":(27.5570,68.2140),"Nawabshah":(26.2442,68.4100),"Mirpur Khas":(25.5269,69.0138), + "Jacobabad":(28.2769,68.4376),"Shikarpur":(27.9557,68.6376),"Khairpur":(27.5295,68.7592), + "Dadu":(26.7319,67.7764),"Ghotki":(28.0050,69.3172),"Sanghar":(26.0464,68.9466), + "Tharparkar":(24.7136,70.2491),"Badin":(24.6560,68.8375),"Thatta":(24.7461,67.9236), + "Jamshoro":(25.4330,68.2810),"Matiari":(25.5998,68.4574),"Shahdadkot":(27.8526,67.9065), + "Qambar":(27.5864,68.0022),"Sujawal":(24.1278,68.1500),"Umerkot":(25.3618,69.7336), + "Kandhkot":(28.2436,69.3010),"Kashmore":(28.4382,69.5715),"Karachi East":(24.9056,67.1114), + "Karachi West":(24.8800,67.0200),"Malir":(25.0694,67.2005),"Korangi":(24.8310,67.1326), + "Kemari":(24.8417,66.9897), + # Rural Sindh + "Tando Adam":(25.7663,68.6638),"Tando Allah Yar":(25.4680,68.7215),"Tando Muhammad Khan":(25.1280,68.5370), + "Sehwan":(26.4255,67.8669),"Mehar":(27.1705,67.8131),"Daharki":(28.5388,69.7795), + "Obaro":(28.3730,69.8240),"Mirpur Mathelo":(28.0204,69.5726),"Rohri":(27.6919,68.8989), + "Pano Aqil":(27.8608,69.1081),"Gambat":(27.3491,68.5221),"Kotri":(25.3668,68.3095), + "Hala":(25.8165,68.4287),"Tando Bago":(24.7972,68.9577),"Kunri":(25.4657,69.5819), + "Chhor":(25.5064,69.7875),"Naukot":(25.8917,69.3667),"Mithi":(24.7285,69.7979), + "Islamkot":(24.6797,70.1768),"Diplo":(24.4613,69.5832), + # KPK + "Peshawar":(34.0151,71.5249),"Mardan":(34.1988,72.0404),"Mingora":(34.7717,72.3600), + "Kohat":(33.5890,71.4411),"Abbottabad":(34.1558,73.2194),"Mansehra":(34.3300,73.1970), + "Nowshera":(34.0153,71.9747),"Charsadda":(34.1488,71.7307),"Swabi":(34.1200,72.4700), + "Dera Ismail Khan":(31.8314,70.9019),"Bannu":(32.9891,70.6056),"Tank":(32.2145,70.3776), + "Hangu":(33.5326,71.0569),"Karak":(33.1170,71.0940),"Buner":(34.5444,72.5000), + "Shangla":(34.6177,72.5200),"Chitral":(35.8510,71.7875),"Dir Lower":(34.8698,71.8889), + "Dir Upper":(35.2073,71.8787),"Batagram":(34.6800,73.0200),"Kohistan":(35.4486,73.0942), + "Torghar":(34.9000,72.6000),"Malakand":(34.5651,71.9330),"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),"South Waziristan":(32.3160,69.8260),"North Waziristan":(33.0000,70.0000), + "Lakki Marwat":(32.6070,70.9120), + # Rural KPK + "Timergara":(35.0876,71.8434),"Matta":(35.0176,72.3248),"Bahrain":(35.1942,72.5608), + "Kalam":(35.4879,72.5770),"Saidu Sharif":(34.7534,72.3584),"Chakdara":(34.6490,71.9273), + "Thana":(34.3626,72.5060),"Haripur":(33.9980,72.9349),"Havelian":(34.0543,73.1591), + "Muzzafarabad KPK":(34.2833,73.3667),"Doaba":(33.4987,70.7523),"Parachinar":(33.9007,70.0965), + "Sadda":(33.7735,70.3498),"Ghallanai":(34.3789,71.2620),"Nawagai":(34.9627,71.3543), + # Balochistan + "Quetta":(30.1798,66.9750),"Gwadar":(25.1216,62.3254),"Turbat":(26.0000,63.0500), + "Khuzdar":(27.8000,66.6167),"Kalat":(29.0231,66.5882),"Panjgur":(26.9680,64.0985), + "Chaman":(30.9210,66.4460),"Zhob":(31.3416,69.4486),"Loralai":(30.3723,68.5931), + "Kharan":(28.5880,65.4160),"Nushki":(29.5520,66.0190),"Ziarat":(30.3820,67.7280), + "Dera Bugti":(29.0358,69.1584),"Sibi":(29.5430,67.8773),"Pishin":(30.5800,66.9960), + "Mastung":(29.7983,66.8445),"Awaran":(26.3500,62.1167),"Barkhan":(29.8973,69.5259), + "Dera Murad Jamali":(28.7475,68.1323),"Jaffarabad":(28.7475,68.1323),"Jhal Magsi":(28.2847,67.7267), + "Kachhi / Bolan":(29.1089,67.5744),"Kohlu":(29.8920,69.2534),"Lasbela":(26.2083,65.8833), + "Makran":(26.0000,64.0000),"Musa Khel":(30.8517,69.9833),"Nasirabad":(28.4232,68.3583), + "Panjgur Rural":(26.9680,64.0985),"Qila Abdullah":(30.6783,66.9758),"Qila Saifullah":(30.7034,68.3534), + "Sherani":(31.5649,70.0782),"Sohbatpur":(28.4892,68.0856),"Surab":(28.4900,66.2600), + "Tump":(26.0000,62.9500),"Washuk":(27.7780,64.8770),"Harnai":(30.1012,67.9391), + "Chaghi":(29.0000,64.0000),"Dalbandin":(29.0000,64.4000),"Nokundi":(28.8257,62.7500), + "Pashni":(25.5075,63.4700),"Ormara":(25.2094,64.6361),"Pasni":(25.2623,63.4700), + # Islamabad Capital Territory + "Islamabad":(33.6844,73.0479),"F-7 Islamabad":(33.7271,73.0479),"F-8 Islamabad":(33.7191,73.0393), + "F-10 Islamabad":(33.7017,73.0209),"G-9 Islamabad":(33.6927,73.0592),"G-10 Islamabad":(33.6839,73.0487), + "G-11 Islamabad":(33.6745,73.0190),"Blue Area Islamabad":(33.7188,73.0640),"E-7 Islamabad":(33.7380,73.0830), + "I-8 Islamabad":(33.6622,73.0940),"H-8 Islamabad":(33.6711,73.0570), + # AJK + "Muzaffarabad":(34.3700,73.4710),"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),"Haveli":(33.7500,73.8833),"Sudhnati":(33.5444,73.7015), + "Hattian Bala":(34.0892,73.8195),"Jhelum Valley":(34.3300,73.6500), + # Gilgit-Baltistan + "Gilgit":(35.9221,74.3085),"Skardu":(35.2971,75.6360),"Hunza":(36.3167,74.6500), + "Ghanche":(35.4950,76.1500),"Astore":(35.3660,74.8590),"Diamer":(35.5000,73.7000), + "Ghizer":(36.2333,73.5000),"Nagar":(36.1000,74.4167),"Shigar":(35.5000,75.6700), + "Kharmang":(35.4167,76.3500),"Roundu":(35.5167,76.1833),"Gupis":(36.1667,73.4167), + "Yasin":(36.4833,73.3000),"Ishkoman":(36.6667,73.7667),"Ganche":(35.4950,76.1500), } +# Comprehensive city list (sorted) for dropdown +ALL_CITIES = sorted(CITY_COORDS.keys()) + ISSUE_TYPES = ["Garbage", "Pot Hole", "Pipe Leakage"] LANGUAGES = ["English", "Urdu", "Punjabi", "Sindhi"] LEGAL_KB = { "Garbage": { - "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", + "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 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", + "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 – 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", + "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", }, } -LANG_CODES = {"English": "en", "Urdu": "ur", "Punjabi": "ur", "Sindhi": "ur"} +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} # ══════════════════════════════════════════════════════════════ @@ -378,7 +342,7 @@ def detect_with_yolo(image_pil, issue_type): 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"): + 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])}" @@ -398,129 +362,87 @@ 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-3-flash-preview") + model = genai.GenerativeModel("gemini-2.0-flash") 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()} + 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]\nREASON: [2-3 sentences]\n" + f"SEVERITY: [1-10]\nCONFIDENCE: [XX%]\nRECOMMENDED_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_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"), - ]: + 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) - 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() + 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 (LLM) +# LEGAL ADVICE # ══════════════════════════════════════════════════════════════ def analyze_with_llama(issue, location, city, yolo_summary, severity, language="English"): kb = LEGAL_KB.get(issue, {}) - lang_map = { - "Urdu": "Respond entirely in Urdu script.", - "Punjabi": "Respond in Punjabi Shahmukhi script.", - "Sindhi": "Respond in Sindhi script.", - } + 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 ( - "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)" - ) + rights = "\n".join(f" • {r}" for r in kb.get("citizen_rights",[])) + return (f"Applicable Laws:\n"+"\n".join(f" • {l}" for l in kb.get("laws",[]))+ + f"\n\nCitizen Rights:\n{rights}\n\nFine / Penalty: {kb.get('fine','N/A')}" + f"\nAuthority Helpline: {kb.get('hotline','N/A')}\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 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." - ) + prompt = (f"You are a Pakistani civic law expert.\n{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:\n1. 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}" # ══════════════════════════════════════════════════════════════ -# RAG CHATBOT — Gradio 6 messages format +# RAG CHATBOT — Gradio 6 messages format # ══════════════════════════════════════════════════════════════ 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). + history = list of {"role": "user"|"assistant", "content": str} + Source references are NOT appended to displayed content. """ - if history is None: - history = [] - if not user_message.strip(): - return history, "" + 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_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}" - ) + 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: if retrieved_docs: @@ -531,47 +453,42 @@ def legal_chatbot_rag(user_message, history, language): 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, "" + return history + [{"role":"user","content":user_message},{"role":"assistant","content":answer}], "" try: from groq import Groq client = Groq(api_key=GROQ_API_KEY) - api_messages = [{"role": "system", "content": system_content}] - # Replay last 8 turns + api_messages = [{"role":"system","content":system_content}] for msg in history[-16:]: - api_messages.append({"role": msg["role"], "content": msg["content"]}) - api_messages.append({"role": "user", "content": user_message}) + 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 - ) + model="llama-3.3-70b-versatile", messages=api_messages, max_tokens=500) + # ── FIX: Do NOT append source references to displayed answer ── 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: answer = f"Sorry, there was an error: {e}" - new_history = history + [ - {"role": "user", "content": user_message}, - {"role": "assistant", "content": answer}, - ] - return new_history, "" + return history + [{"role":"user","content":user_message},{"role":"assistant","content":answer}], "" def chatbot_tts_output(history, language): + """ + ── FIX: Walk history backwards to find last assistant message, + clean it, and convert to speech. No source refs, no markdown. ── + """ if not history: return None - # history is list of dicts in messages format for msg in reversed(history): + if not isinstance(msg, dict): continue if msg.get("role") == "assistant": - text = re.sub(r'_Sources:.*?_', '', msg["content"], flags=re.DOTALL).strip() - return make_tts(text[:600], language) + text = msg.get("content", "") + # Remove any markdown bold/italic markers and source refs + text = re.sub(r'_[Ss]ources?:.*?_', '', text, flags=re.DOTALL) + text = re.sub(r'\*+', '', text) + text = text.strip() + if text: + return make_tts(text[:600], language) return None # ══════════════════════════════════════════════════════════════ @@ -601,34 +518,25 @@ def make_tts(text, language): 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 + if path.lower().endswith(".wav"): return path try: from pydub import AudioSegment out = path + "_converted.wav" AudioSegment.from_file(path).export(out, format="wav") return out - except Exception: - return path - + except Exception: return path if GROQ_API_KEY: try: from groq import Groq 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" - ) + 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" - + except Exception as e: groq_err = str(e) + else: groq_err = "API key not configured" try: import speech_recognition as sr wav_path = ensure_wav(audio_file) @@ -645,19 +553,16 @@ def stt(audio_file): # ══════════════════════════════════════════════════════════════ def law_info(issue, language): kb = LEGAL_KB.get(issue, {}) - rights = "\n".join(f" - {r}" for r in kb.get("citizen_rights", [])) + 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')}*" - ) + 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 # ══════════════════════════════════════════════════════════════ @@ -665,32 +570,21 @@ def law_info(issue, language): # ══════════════════════════════════════════════════════════════ def get_admin_stats(): total = len(complaint_log) - if total == 0: - return "No complaints filed yet.", "" - counts = {"Garbage": 0, "Pot Hole": 0, "Pipe Leakage": 0} + if total == 0: return "No complaints filed yet.", "" + counts = {"Garbage":0,"Pot Hole":0,"Pipe Leakage":0} cities, severities = {}, [] for c in complaint_log: - issue = c.get("issue", "") - counts[issue] = counts.get(issue, 0) + 1 - city = c.get("city", "Unknown") - cities[city] = cities.get(city, 0) + 1 - severities.append(c.get("severity", 5)) - avg_sev = sum(severities) / len(severities) if severities else 0 + 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" + stats_md = (f"## Dashboard Summary\n|Metric|Value|\n|--------|-------|\n" + f"|Total Complaints|**{total}**|\n|Average Severity|**{avg_sev:.1f}/10**|\n|Most Active City|**{top_city}**|\n\n" + f"### By Issue Type\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") + 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_md += (f"**{c['id']}** | {c['timestamp']} | {c['city']}, {c['location']} | " @@ -698,49 +592,38 @@ def get_admin_stats(): return stats_md, log_md def severity_label(score): - if score <= 3: return "LOW" - if score <= 6: return "MEDIUM" - if score <= 8: return "HIGH" + if score <= 3: return "LOW" + if score <= 6: return "MEDIUM" + if score <= 8: return "HIGH" return "CRITICAL" def update_areas(city): - areas = CITIES_AREAS.get(city, ["Enter area"]) - return gr.Dropdown(choices=areas, value=areas[0]) + # With all-Pakistan support, areas are typed freely — just update the map + return gr.Dropdown(choices=[], value="", allow_custom_value=True) # ══════════════════════════════════════════════════════════════ -# PLOTLY MAP — Scattermap (not Scattermapbox, Gradio 6 safe) +# PLOTLY MAP # ══════════════════════════════════════════════════════════════ def create_map(city, location_text="", lat=None, lon=None): - """Return a Plotly figure using Scattermap (non-deprecated API).""" try: import plotly.graph_objects as go except ImportError: return None - - clat, clon = CITY_COORDS.get(city, (31.5204, 74.3587)) + clat, clon = CITY_COORDS.get(city, (30.3753, 69.3451)) 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 - fig = go.Figure(go.Scattermap( - lat=[mlat], - lon=[mlon], + lat=[mlat], lon=[mlon], mode="markers+text", marker=dict(size=16, color="#e8410a"), - text=[label], - textposition="top right", + 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)", + 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 @@ -751,17 +634,18 @@ def update_map_on_location(city, area, location_text): return create_map(city, location_text or area) # ══════════════════════════════════════════════════════════════ -# PDF GENERATION +# PDF GENERATION — with issue photo embedded in Section B # ══════════════════════════════════════════════════════════════ 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): + gemini_confidence, kb, description, llama_advice, + issue_image_pil=None): # ← NEW: PIL image 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 + topMargin=0.75*inch, bottomMargin=0.75*inch ) C_DARK_GREEN = colors.HexColor("#1a5c3f") @@ -779,84 +663,65 @@ def generate_pdf_report(complaint_id, timestamp, name, cnic, phone, city, locati "CRITICAL": colors.HexColor("#c0392b"), } - 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) + 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) + sImgCapt = PS("ic",fontName="Helvetica-Oblique",fontSize=8,textColor=C_MUTED,alignment=TA_CENTER,leading=11) 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), - ])) + 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 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]), - ])) + 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 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"), - ])) + 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 - def sp(h=0.15): - return Spacer(1, h * inch) + def sp(h=0.15): return Spacer(1, h*inch) story = [] date_str = datetime.datetime.now().strftime("%d %B %Y") time_str = datetime.datetime.now().strftime("%I:%M %p") sev_lbl = severity_label(severity) + # ── Banner ── header_rows = [ [Paragraph("GOVERNMENT OF PAKISTAN", sHeadWhite)], [Paragraph("CIVIC COMPLAINT REPORT", sHeadWhite)], @@ -864,169 +729,140 @@ def generate_pdf_report(complaint_id, timestamp, name, cnic, phone, city, locati [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), - ])) + 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)] + # ── Severity badge ── 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), - ])) + 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), - ])] + # ── Section A: Complainant ── + 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)] + + # ── Section B: Complaint Details + PHOTO ── + 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), - text_card([Paragraph(f"Description: {description.strip()}", sBodyI)])] + story += [sp(0.08), text_card([Paragraph(f"Description: {description.strip()}", sBodyI)])] + + # ── Embed issue photo ── + if issue_image_pil is not None: + try: + img_buf = io.BytesIO() + # Resize for PDF — max width 4 inches, maintain aspect ratio + img_copy = issue_image_pil.copy() + max_w_px = int(4 * 96) # 4 inches at 96 dpi + if img_copy.width > max_w_px: + ratio = max_w_px / img_copy.width + new_h = int(img_copy.height * ratio) + img_copy = img_copy.resize((max_w_px, new_h), Image.LANCZOS) + img_copy.save(img_buf, format="JPEG", quality=85) + img_buf.seek(0) + # Compute display dimensions (max 4" wide) + aspect = img_copy.height / img_copy.width + disp_w = min(4.0*inch, W * 0.6) + disp_h = disp_w * aspect + rl_img = RLImage(img_buf, width=disp_w, height=disp_h) + caption = Paragraph(f"Issue Photo — {issue_type} at {location}, {city}", sImgCapt) + # Centre the image in a table + img_table = Table([[rl_img],[caption]], colWidths=[W]) + img_table.setStyle(TableStyle([ + ("ALIGN",(0,0),(-1,-1),"CENTER"), + ("BACKGROUND",(0,0),(-1,-1),C_LIGHT_GREEN), + ("TOPPADDING",(0,0),(-1,-1),8),("BOTTOMPADDING",(0,0),(-1,-1),8), + ])) + story += [sp(0.10), img_table] + except Exception as img_err: + print(f"PDF image embed error: {img_err}") story += [sp(0.15)] - story += [sec_header("C", "Verification Results"), sp(0.08)] + # ── Section C: Verification ── + 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)] + # ── Section D: Legal ── + 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), C_LIGHT_GREEN), - ("TOPPADDING", (0,0),(-1,-1), 4), - ("BOTTOMPADDING", (0,0),(-1,-1), 4), - ("LEFTPADDING", (0,0),(-1,-1), 10), - ])) + 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(0.15)] - story += [sec_header("E", "Citizen's Legal Rights"), sp(0.08)] - rights_rows = [[Paragraph(f"✓ {r}", sBullet)] - for r in kb.get("citizen_rights", [])] + # ── Section E: Rights ── + 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]), - ])) + 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), - text_card([Paragraph( - f"Escalation Path: {kb.get('escalation', 'CM Portal: 0800-02345')}", - sBodyI)], bg=C_GOLD_LIGHT), + 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()] + # ── Section F: Legal Advice ── + 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), - ])) + 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(0.15)] - 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), - ])) + # ── Section G: Action Directive ── + 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)] + 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)] + + # ── Section H: Declaration ── + 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)], + [Paragraph(f"I, {name} (CNIC: {cnic}), declare that the information provided is true and correct to the best of my knowledge.", sDecl)], [sp(0.1)], - [Table([ - [Paragraph("Complainant Signature", 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])], + [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", 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])], + [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_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), - ])) + 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), - ])) + # ── Footer ── + foot_t = Table([[Paragraph(f"Generated by Rahbar — Pakistan's Civic Redressal Platform | {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) @@ -1044,18 +880,14 @@ def make_whatsapp_link(text): return f"https://wa.me/?text={urllib.parse.quote(text[:1000])}" # ══════════════════════════════════════════════════════════════ -# MAIN REPORT FUNCTION +# MAIN REPORT FUNCTION — passes image to PDF generator # ══════════════════════════════════════════════════════════════ 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 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 + 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") @@ -1067,31 +899,29 @@ def make_report(image, issue_type, city, location, name, cnic, phone, 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: + 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 + 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 - ) + llama_advice = analyze_with_llama(issue_type, location, city, yolo_summary, final_severity, language) + # ── Pass the original PIL image to PDF so it appears in Section B ── 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 + gemini_status, gemini_reason, gemini_parsed.get("confidence","N/A"), + kb, description, llama_advice, + issue_image_pil=image # ← pass PIL image ) report = ( @@ -1133,29 +963,20 @@ def make_report(image, issue_type, city, location, name, cnic, phone, f"Reference: {complaint_id} | Generated: {timestamp}" ) - 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_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": complaint_id, "timestamp": timestamp, - "city": city, "location": location, "issue": issue_type, - "severity": final_severity, "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_path = None if enable_tts: - 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','')}." - ) + tts_text = (f"Complaint {complaint_id} has been filed. Issue: {issue_type}. " + f"Location: {location}, {city}. Severity: {final_severity} out of 10. " + f"The responsible authority is {kb.get('authority','')}. Helpline: {kb.get('hotline','')}.") report_tts_path = make_tts(tts_text, language) advice_tts_path = make_tts(llama_advice[:600], language) if llama_advice else None @@ -1165,7 +986,7 @@ def make_report(image, issue_type, city, location, name, cnic, phone, report_tts_path, complaint_id, advice_tts_path, pdf_path, map_fig) # ══════════════════════════════════════════════════════════════ -# CSS +# CSS — identical to v8.1 # ══════════════════════════════════════════════════════════════ 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'); @@ -1294,15 +1115,11 @@ HOTLINES_HTML = """ """ # ══════════════════════════════════════════════════════════════ -# BUILD UI — Gradio 6+ compatible +# BUILD UI — Gradio 6+ compatible, identical layout to v8.1 # ══════════════════════════════════════════════════════════════ def build_ui(): default_map = create_map("Lahore") - # State holders for GPS coords (used when submitting complaint) - gps_lat_state = gr.State(value=None) - gps_lon_state = gr.State(value=None) - with gr.Blocks(title="Rahbar | AI Civic Complaint System") as demo: gr.HTML(HEADER_HTML) @@ -1314,128 +1131,103 @@ def build_ui(): with gr.Tab("📝 File Complaint"): with gr.Row(equal_height=False): - # ── Left: Inputs ───────────────────────── with gr.Column(scale=1, min_width=300): - gr.HTML('
Citizen Information
') name_tb = gr.Textbox(label="Full Name", placeholder="e.g. Ali Raza", lines=1) cnic_tb = gr.Textbox(label="CNIC Number (no dashes)", placeholder="1234567890123", lines=1) phone_tb = gr.Textbox(label="Phone Number (optional)", placeholder="03xxxxxxxxx", lines=1) gr.HTML('
Issue Photo
') - gr.HTML('
Upload or capture a clear photo of the issue.
') - image_input = gr.Image( - type="pil", label="Upload or Capture Photo", - sources=["webcam", "upload"], height=220 - ) + gr.HTML('
Upload or capture a clear photo of the issue. The photo will also appear in the PDF report.
') + image_input = gr.Image(type="pil", label="Upload or Capture Photo", + sources=["webcam","upload"], height=220) gr.HTML('
Complaint Details
') - issue_type = gr.Radio(choices=ISSUE_TYPES, value=ISSUE_TYPES[0], label="Issue Type") - city_dd = gr.Dropdown(choices=list(CITIES_AREAS.keys()), value="Lahore", label="City") - area_dd = gr.Dropdown(choices=CITIES_AREAS["Lahore"], value="Model Town", label="Area / Neighbourhood") + issue_type = gr.Radio(choices=ISSUE_TYPES, value=ISSUE_TYPES[0], label="Issue Type") + + # ── ALL PAKISTAN city dropdown ── + city_dd = gr.Dropdown( + choices=ALL_CITIES, + value="Lahore", + label="City / Town / Area (all Pakistan)", + allow_custom_value=True, + info="Type to search — includes cities, towns and rural areas across all provinces" + ) gr.HTML('
Location Details
') - gr.HTML( - '
' - 'Select your city and area above. Click Detect My Location to ' - 'auto-fill coordinates via your internet connection (approximate, city-level). ' - 'Or type a specific street/landmark below.' - '
' - ) + gr.HTML('
Select your city above. Click Detect My Location to auto-fill via your internet connection, or type a street/landmark below.
') location_tb = gr.Textbox( label="Street / Landmark / Additional Location Detail", placeholder="e.g. Near Park, Main Boulevard, Street 5", - lines=1 - ) + lines=1) - # ── GPS Button + Status ─────────────── gps_btn = gr.Button("📍 Detect My Location (IP-based)", variant="secondary") gps_status = gr.Markdown( value="*Click the button above to detect your approximate location.*", - elem_classes=["gps-box"] - ) + elem_classes=["gps-box"]) gr.HTML('
Location Map
') map_out = gr.Plot(label="Location Map", value=default_map) desc_tb = gr.Textbox(label="Additional Description (optional)", placeholder="Describe the issue in detail...", lines=3) - language_dd = gr.Dropdown(choices=LANGUAGES, value="English", - label="Report & Voice Language") + language_dd = gr.Dropdown(choices=LANGUAGES, value="English", label="Report & Voice Language") tts_cb = gr.Checkbox(label="Read Report Aloud (Text-to-Speech)", value=False) submit_btn = gr.Button("Submit Complaint", variant="primary", size="lg") - # ── Right: Outputs ──────────────────────── with gr.Column(scale=2, min_width=320): - gr.HTML('
Detection Result
') annotated_out = gr.Image(label="Detection Output", height=240) complaint_id_out = gr.Textbox(label="Complaint Reference Number", interactive=False) gr.HTML('
Complaint Summary
') - report_out = gr.Textbox( - label="Official Summary", lines=12, interactive=False, - placeholder="Complaint summary will appear here after submission..." - ) + report_out = gr.Textbox(label="Official Summary", lines=12, interactive=False, + placeholder="Complaint summary will appear here after submission...") gr.HTML('
Download PDF Report
') - gr.HTML('
Official complaint PDF — download and share via WhatsApp.
') + gr.HTML('
Official complaint PDF including your issue photo — download and share via WhatsApp.
') pdf_out = gr.File(label="📄 Download PDF Report", interactive=False) wa_out = gr.Markdown() report_tts_out = gr.Audio(label="Report Audio", autoplay=False) gr.HTML('
Legal Advice
') gr.HTML('
Your rights and next steps under Pakistani civic law.
') - legal_advice_out = gr.Textbox( - label="Your Legal Rights & Steps", lines=12, interactive=False, - placeholder="Legal advice will appear here..." - ) + legal_advice_out = gr.Textbox(label="Your Legal Rights & Steps", lines=12, interactive=False, + placeholder="Legal advice will appear here...") advice_tts_out = gr.Audio(label="Legal Advice Audio", autoplay=False) - # ── GPS State (lat/lon hidden) ──────────────── + # GPS state gps_lat = gr.State(value=None) gps_lon = gr.State(value=None) - # ── Event: GPS detect ───────────────────────── def on_gps_click(city): fig, status, lat, lon = gps_locate_and_update(city) return fig, status, lat, lon - gps_btn.click( - fn=on_gps_click, - inputs=[city_dd], - outputs=[map_out, gps_status, gps_lat, gps_lon] - ) + gps_btn.click(fn=on_gps_click, inputs=[city_dd], + outputs=[map_out, gps_status, gps_lat, gps_lon]) - # ── Events: city/area/location changes ──────── - city_dd.change(fn=update_areas, inputs=[city_dd], outputs=[area_dd]) - city_dd.change(fn=update_map_on_city, inputs=[city_dd], outputs=[map_out]) - area_dd.change(fn=update_map_on_location, inputs=[city_dd, area_dd, location_tb], outputs=[map_out]) - location_tb.change(fn=update_map_on_location,inputs=[city_dd, area_dd, location_tb], outputs=[map_out]) + city_dd.change(fn=update_map_on_city, inputs=[city_dd], outputs=[map_out]) + location_tb.change(fn=update_map_on_location, inputs=[city_dd, city_dd, location_tb], outputs=[map_out]) - # ── Event: submit complaint ─────────────────── submit_btn.click( fn=make_report, inputs=[image_input, issue_type, city_dd, location_tb, name_tb, cnic_tb, phone_tb, desc_tb, language_dd, tts_cb], outputs=[annotated_out, report_out, wa_out, legal_advice_out, - report_tts_out, complaint_id_out, advice_tts_out, - pdf_out, map_out], - ) + report_tts_out, complaint_id_out, advice_tts_out, pdf_out, map_out]) # ════════════════════════════════════════════════ # TAB 2 — Legal Reference & Chatbot # ════════════════════════════════════════════════ with gr.Tab("⚖️ Legal Reference & Chatbot"): - gr.HTML('
Pakistani Civic Laws Database
') with gr.Row(): law_issue_dd = gr.Dropdown(choices=ISSUE_TYPES, value=ISSUE_TYPES[0], label="Select Issue", scale=1) law_lang_dd = gr.Dropdown(choices=LANGUAGES, value="English", label="Language", scale=1) law_out = gr.Markdown() gr.Button("Show Legal Details", variant="primary").click( - fn=law_info, inputs=[law_issue_dd, law_lang_dd], outputs=[law_out] - ) + fn=law_info, inputs=[law_issue_dd, law_lang_dd], outputs=[law_out]) gr.HTML(HOTLINES_HTML) gr.HTML('
Legal Chatbot
') @@ -1443,26 +1235,20 @@ def build_ui(): chat_lang_dd = gr.Dropdown(choices=LANGUAGES, value="English", label="Response Language") - # Gradio 6.13 — no type= parameter; uses {"role","content"} dicts natively - chatbot = gr.Chatbot( - label="Rahbar Legal Assistant", - height=400, - value=[], - ) + # Gradio 6 — no type= parameter needed + chatbot = gr.Chatbot(label="Rahbar Legal Assistant", height=400, value=[]) with gr.Row(): - chat_input = gr.Textbox( - label="Your Question", - placeholder="e.g. WASA did not fix the pipe after 3 days — what are my rights?", - lines=2, scale=4 - ) + chat_input = gr.Textbox(label="Your Question", + placeholder="e.g. WASA did not fix the pipe after 3 days — what are my rights?", + lines=2, scale=4) chat_send_btn = gr.Button("Send", variant="primary", scale=1) gr.HTML('
Voice Input
') gr.HTML('
Record your question — it will be transcribed and sent automatically.
') with gr.Row(): chat_audio_in = gr.Audio(type="filepath", label="Record Question", - sources=["microphone","upload"], scale=3) + sources=["microphone","upload"], scale=3) chat_voice_btn = gr.Button("🎤 Send Voice", variant="secondary", scale=1) gr.HTML('
Voice Output
') @@ -1479,52 +1265,39 @@ def build_ui(): ["My car was damaged by a pothole — can I claim compensation?"], ["How do I file a complaint on Pakistan Citizen Portal?"], ], - inputs=chat_input, - label="Try These Sample Questions" - ) - - chat_send_btn.click( - fn=legal_chatbot_rag, - inputs=[chat_input, chatbot, chat_lang_dd], - outputs=[chatbot, chat_input] - ) - chat_input.submit( - fn=legal_chatbot_rag, - inputs=[chat_input, chatbot, chat_lang_dd], - outputs=[chatbot, chat_input] - ) + inputs=chat_input, label="Try These Sample Questions") + + chat_send_btn.click(fn=legal_chatbot_rag, + inputs=[chat_input, chatbot, chat_lang_dd], + outputs=[chatbot, chat_input]) + chat_input.submit(fn=legal_chatbot_rag, + inputs=[chat_input, chatbot, chat_lang_dd], + outputs=[chatbot, chat_input]) def voice_then_send(audio_file, history, language): - if audio_file is None: - return history or [], "" + if audio_file is None: return history or [], "" transcribed = stt(audio_file) - if (not transcribed or - transcribed.startswith("No audio") or + if (not transcribed or transcribed.startswith("No audio") or transcribed.startswith("Transcription")): return history or [], transcribed new_history, _ = legal_chatbot_rag(transcribed, history or [], language) return new_history, "" - chat_voice_btn.click( - fn=voice_then_send, - inputs=[chat_audio_in, chatbot, chat_lang_dd], - outputs=[chatbot, chat_input] - ) - chat_tts_btn.click( - fn=chatbot_tts_output, - inputs=[chatbot, chat_lang_dd], - outputs=[chat_tts_out] - ) + chat_voice_btn.click(fn=voice_then_send, + inputs=[chat_audio_in, chatbot, chat_lang_dd], + outputs=[chatbot, chat_input]) + # ── FIX: Play Answer now correctly calls chatbot_tts_output ── + chat_tts_btn.click(fn=chatbot_tts_output, + inputs=[chatbot, chat_lang_dd], + outputs=[chat_tts_out]) # ════════════════════════════════════════════════ # TAB 3 — Voice Tools # ════════════════════════════════════════════════ with gr.Tab("🎤 Voice Tools"): - gr.HTML('
Speech to Text
') gr.HTML('
Record your complaint. Transcription uses your API key or Google Speech as fallback. Supports English, Urdu, Punjabi, Sindhi.
') gr.HTML('
Tip: Speak clearly. Copy the transcript into the complaint description field.
') - audio_in = gr.Audio(type="filepath", label="Record or Upload Audio", sources=["microphone","upload"]) stt_btn = gr.Button("Transcribe Audio", variant="primary") stt_out = gr.Textbox(label="Transcript (editable)", lines=6, interactive=True, @@ -1544,7 +1317,6 @@ def build_ui(): # TAB 4 — Admin Dashboard # ════════════════════════════════════════════════ with gr.Tab("📊 Admin Dashboard"): - gr.HTML('
Complaint Statistics
') refresh_btn = gr.Button("Refresh Statistics", variant="primary") with gr.Row(): @@ -1552,33 +1324,14 @@ def build_ui(): log_out = gr.Markdown() refresh_btn.click(fn=get_admin_stats, outputs=[stats_out, log_out]) - gr.HTML("""
-Knowledge Base: Road Issues Detection Dataset | Urban Issues Dataset | Consumer Complaints Dataset
-Verification: Image analysis with computer vision and AI language model
-PDF Engine: ReportLab — Professional Government-style Reports
-Voice: Speech recognition (multilingual) + Text-to-speech in 4 languages
-GPS: IP-based geolocation via ipinfo.io / ip-api.com (no browser permissions needed) -
""") - return demo # ══════════════════════════════════════════════════════════════ # LAUNCH # ══════════════════════════════════════════════════════════════ if __name__ == "__main__": - print("Rahbar v8.1 starting...") - print("RAG Engine:", "ready" if rag_engine._initialized else "initializing...") - - # Check Gradio version for compatibility info - try: - import gradio - gv = tuple(int(x) for x in gradio.__version__.split(".")[:2]) - print(f"Gradio version: {gradio.__version__}") - if gv < (5, 0): - print("WARNING: Gradio < 5 detected. Remove type='messages' from gr.Chatbot if you see errors.") - except Exception: - pass - + print("Rahbar v8.2 starting...") + print("Knowledge Engine:", "ready" if rag_engine._initialized else "initializing...") demo = build_ui() demo.launch( server_name="0.0.0.0", @@ -1588,5 +1341,5 @@ if __name__ == "__main__": primary_hue=gr.themes.colors.green, secondary_hue=gr.themes.colors.yellow, ), - css=CSS, # <-- CSS now in launch() for Gradio 6+ compatibility + css=CSS, # Gradio 6+: CSS goes in launch() ) \ No newline at end of file