PYTHON #1

.env

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+MODEL_NAME=adibrino/LAPOR-AI
+ALLOWED_ORIGINS=https://lalim.vercel.app,http://localhost:8000,http://127.0.0.1:8000
+SERVICE_CODES_MAP='{"DPRKPCK": "Perumahan Rakyat, Kawasan Permukiman dan Cipta Karya", "DPUBM": "Pekerjaan Umum Bina Marga", "DPUSDA": "Pekerjaan Umum Sumber Daya Air", "DLH": "Lingkungan Hidup", "DINSOS": "Sosial", "BPBD": "Penanggulangan Bencana Daerah", "DISHUB": "Perhubungan", "DINKES": "Kesehatan", "SATPOLPP": "Satuan Polisi Pamong Praja", "DISKOMINFO": "Komunikasi dan Informatika", "DISNAKERTRANS": "Tenaga Kerja dan Transmigrasi", "DIPERTAKP": "Pertanian dan Ketahanan Pangan", "DISNAK": "Peternakan", "DKP": "Kelautan dan Perikanan", "DINDIK": "Pendidikan", "DISBUDPAR": "Kebudayaan dan Pariwisata", "DISPERINDAG": "Perindustrian dan Perdagangan", "DPMPTSP": "Penanaman Modal dan Pelayanan Terpadu Satu Pintu", "DISKOPUKM": "Koperasi, Usaha Kecil dan Menengah", "DISPORA": "Kepemudaan dan Olahraga", "DISPERPUSIP": "Perpustakaan dan Kearsipan", "BAPPEDA": "Perencanaan Pembangunan Daerah", "BAPENDA": "Pajak dan Pendapatan Daerah", "DP3AK": "Pemberdayaan Perempuan, Perlindungan Anak dan Kependudukan"}'
+IS_PRODUCTION=false

.vscode/settings.json

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+{
+    "python.analysis.typeCheckingMode": "strict"
+}

Dockerfile

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+FROM python:3.9-slim
+
+RUN apt-get update && apt-get install -y curl && rm -rf /var/lib/apt/lists/*
+
+RUN curl -fsSL https://ollama.com/install.sh | sh
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+COPY --chown=user . $HOME/app
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+ENV MODEL_NAME="adibrino/LAPOR-AI"
+
+RUN chmod +x start.sh
+
+EXPOSE 7860
+
+CMD ["./start.sh"]

README.md

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+---
+title: LAPOR AI SERVER
+emoji: 🏃
+colorFrom: purple
+colorTo: yellow
+sdk: docker
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import io
+import base64
+import json
+import ollama
+import time
+from typing import List, Optional, Union, Any, Dict
+from flask import Flask, request, jsonify, Response
+from flask_cors import CORS
+from PIL import Image
+from werkzeug.datastructures import FileStorage
+from dotenv import load_dotenv
+
+load_dotenv()
+
+ALLOWED_ORIGINS_RAW: Optional[str] = os.getenv("ALLOWED_ORIGINS", None)
+MODEL_NAME: Optional[str] = os.getenv("MODEL_NAME", None)
+IS_PRODUCTION: Optional[str] = os.getenv("IS_PRODUCTION", "false")
+
+try:
+    SERVICE_MAP_STR = os.getenv("SERVICE_CODES_MAP", "{}")
+    SERVICE_MAP = json.loads(SERVICE_MAP_STR)
+except json.JSONDecodeError:
+    raise EnvironmentError("SERVICE_CODES_MAP in .env is not valid JSON.")
+
+if not ALLOWED_ORIGINS_RAW:
+    raise EnvironmentError("The ALLOWED_ORIGINS environment variable is not set.")
+if not MODEL_NAME:
+    raise EnvironmentError("The MODEL_NAME environment variable is not set.")
+if not SERVICE_MAP:
+    raise EnvironmentError("The SERVICE_MAP environment variable is not set or empty.")
+
+ALLOWED_ORIGINS: Union[str, List[str]] = "*" if ALLOWED_ORIGINS_RAW == "*" else [origin.strip() for origin in ALLOWED_ORIGINS_RAW.split(",")]
+
+print(f"ALLOWED_ORIGINS (Parsed): {ALLOWED_ORIGINS}")
+print(f"MODEL_NAME: {MODEL_NAME}")
+print(f"IS_PRODUCTION: {IS_PRODUCTION}")
+print(f"SERVICE_MAP Loaded: {len(SERVICE_MAP)} items")
+print(json.dumps(SERVICE_MAP, indent=2, ensure_ascii=True))
+
+app = Flask(__name__)
+
+app.config['MAX_CONTENT_LENGTH'] = 128 * 1024 * 1024 
+
+CORS(app, resources={r"/api/*": {"origins": ALLOWED_ORIGINS}})
+
+def process_image_to_base64(image_file: FileStorage) -> Optional[str]:
+    try:
+        img = Image.open(image_file) # type: ignore
+        img = img.convert('RGB')
+        buffered = io.BytesIO()
+        img.save(buffered, format="JPEG")
+        return base64.b64encode(buffered.getvalue()).decode('utf-8')
+    except Exception as e:
+        print(f"Error processing image: {e}")
+        return None
+
+@app.route('/', methods=['GET'])
+def health_check() -> Response:
+    return Response("Python Backend is running.")
+
+@app.route('/api/analyze', methods=['POST'])
+def analyze() -> tuple[Response, int]:
+    text_laporan: str = request.form.get('laporan', '')
+    
+    if not text_laporan or len(text_laporan) < 10:
+        return jsonify({
+            "status": "error", 
+            "message": "Deskripsi laporan wajib diisi minimal 10 karakter."
+        }), 400
+
+    images: List[FileStorage] = request.files.getlist('images')
+    valid_images: List[FileStorage] = [img for img in images if img.filename != '']
+
+    if len(valid_images) < 1:
+        return jsonify({
+            "status": "error",
+            "message": "Wajib melampirkan minimal 1 foto bukti."
+        }), 400
+
+    base64_images: List[str] = []
+    for img_file in valid_images:
+        b64 = process_image_to_base64(img_file)
+        if b64:
+            base64_images.append(b64)
+    
+    max_retries = 3
+    last_exception = None
+    
+    for attempt in range(max_retries):
+        try:
+            print(f"Attempting AI Analysis... ({attempt + 1}/{max_retries})")
+            
+            if not MODEL_NAME:
+                 raise ValueError("Model name is missing")
+
+            response: Any = ollama.chat( # type: ignore
+                model=MODEL_NAME,
+                messages=[{
+                    'role': 'user',
+                    'content': text_laporan,
+                    'images': base64_images if base64_images else None
+                }],
+                format='json',
+                options={'temperature': 0.1}
+            )
+
+            if 'message' not in response or 'content' not in response['message']:
+                raise ValueError("Empty response structure from AI")
+
+            content_str = response['message']['content']
+            ai_content: Dict[str, Any] = json.loads(content_str)
+
+            required_keys = ["title", "category", "priority", "service_code"]
+            missing_keys = [key for key in required_keys if key not in ai_content]
+
+            if missing_keys:
+                raise ValueError(f"Missing keys in JSON: {missing_keys}")
+            
+            if not str(ai_content["title"]).strip():
+                raise ValueError("AI returned empty title")
+
+            service_code = ai_content["service_code"]
+            if service_code not in SERVICE_MAP:
+                raise ValueError(f"Invalid service_code: {service_code}. Not found in SERVICE_MAP.")
+
+            expected_category = SERVICE_MAP[service_code]
+            if ai_content["category"] != expected_category:
+                raise ValueError(f"Category mismatch. Got '{ai_content['category']}', expected '{expected_category}' for code {service_code}")
+
+            priority = str(ai_content["priority"]).lower()
+            if priority not in ['high', 'medium', 'low']:
+                raise ValueError(f"Invalid priority: {priority}")
+            
+            ai_content["priority"] = priority
+
+            data: dict[str, Any] = {
+                "status": "success",
+                "data": ai_content,
+                "meta": {
+                    "model": MODEL_NAME,
+                    'processing_time_sec': (response.get("total_duration", 0)) / 1e9,
+                    "images_analyzed": len(base64_images),
+                    "attempts": attempt + 1
+                }
+            }
+
+            print("AI Success:", json.dumps(data, indent=2, ensure_ascii=True))
+
+            return jsonify(data), 200
+
+        except Exception as e:
+            print(f"Attempt {attempt + 1} failed: {str(e)}")
+            last_exception = e
+            time.sleep(1)
+            continue
+    
+    return jsonify({
+        "status": "error", 
+        "message": f"AI Failed after {max_retries} attempts. Last Error: {str(last_exception)}"
+    }), 500
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860, debug=(IS_PRODUCTION == "false"))

model/Modelfile

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+FROM gemma2:latest
+
+# 1. SET SYSTEM INSTRUCTION
+SYSTEM """
+Kamu adalah asisten AI backend untuk aplikasi pengaduan warga (Smart City).
+Tugasmu adalah menganalisis input laporan warga (Deskripsi, Lokasi, dan Deskripsi Visual Gambar/Video) lalu mengklasifikasikannya ke dalam format JSON yang ketat.
+
+### 1. REFERENSI MAPPING KATEGORI & KODE DINAS (WAJIB PATUH):
+Gunakan daftar ini untuk menentukan "category" dan "service_code". Jangan membuat kategori baru di luar daftar ini.
+
+- "Perumahan Rakyat, Kawasan Permukiman dan Cipta Karya" => DPRKPCK
+- "Pekerjaan Umum Bina Marga" => DPUBM
+- "Pekerjaan Umum Sumber Daya Air" => DPUSDA
+- "Lingkungan Hidup" => DLH
+- "Sosial" => DINSOS
+- "Penanggulangan Bencana Daerah" => BPBD
+- "Perhubungan" => DISHUB
+- "Kesehatan" => DINKES
+- "Satuan Polisi Pamong Praja" => SATPOLPP
+- "Komunikasi dan Informatika" => DISKOMINFO
+- "Tenaga Kerja dan Transmigrasi" => DISNAKERTRANS
+- "Pertanian dan Ketahanan Pangan" => DIPERTAKP
+- "Peternakan" => DISNAK
+- "Kelautan dan Perikanan" => DKP
+- "Pendidikan" => DINDIK
+- "Kebudayaan dan Pariwisata" => DISBUDPAR
+- "Perindustrian dan Perdagangan" => DISPERINDAG
+- "Penanaman Modal dan Pelayanan Terpadu Satu Pintu" => DPMPTSP
+- "Koperasi, Usaha Kecil dan Menengah" => DISKOPUKM
+- "Kepemudaan dan Olahraga" => DISPORA
+- "Perpustakaan dan Kearsipan" => DISPERPUSIP
+- "Perencanaan Pembangunan Daerah" => BAPPEDA
+- "Pajak dan Pendapatan Daerah" => BAPENDA
+- "Pemberdayaan Perempuan, Perlindungan Anak dan Kependudukan" => DP3AK
+
+### 2. LOGIKA PRIORITAS (PriorityEnum):
+- "high": Bahaya nyawa, kecelakaan, banjir besar, kebakaran, kekerasan fisik, atau kerusakan infrastruktur vital total.
+- "medium": Mengganggu aktivitas tapi tidak mematikan (macet, jalan berlubang sedang, sampah menumpuk, lampu jalan mati).
+- "low": Bersifat kosmetik, saran, pertanyaan administrasi, atau gangguan ringan.
+
+### 3. ATURAN OUTPUT:
+Hanya berikan output JSON mentah. Jangan ada teks pembuka/penutup.
+Format JSON wajib: { "title": string, "category": string, "priority": string, "service_code": string }
+"""
+
+# 2. SET PARAMETER (Opsional agar lebih kreatif atau kaku)
+PARAMETER temperature 0.1

requirements.txt

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+flask
+flask-cors
+ollama
+Pillow
+gunicorn
+python-dotenv

start.sh

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+#!/bin/bash
+
+echo "Starting Ollama Serve..."
+ollama serve &
+
+echo "Waiting for Ollama socket..."
+sleep 5
+
+echo "PRE-LOADING MODEL: $MODEL_NAME"
+ollama pull $MODEL_NAME
+
+echo "Model loaded. Starting Flask Server..."
+python app.py