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| import os | |
| import json | |
| import requests | |
| import joblib | |
| import re | |
| from fastapi import FastAPI, Request | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from apify_client import ApifyClient | |
| from google import genai | |
| # --- 1. KONFIGURASI API --- | |
| APIFY_TOKEN = os.getenv("APIFY_TOKEN") | |
| GROQ_API_KEY = os.getenv("GROQ_API_KEY") | |
| GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") | |
| if not GROQ_API_KEY: | |
| print("⚠️ WARNING: GROQ_API_KEY belum di-set!") | |
| if not GEMINI_API_KEY: | |
| print("⚠️ WARNING: GEMINI_API_KEY belum di-set!") | |
| if not APIFY_TOKEN: | |
| print("⚠️ WARNING: APIFY_TOKEN belum di-set!") | |
| GROQ_URL = "https://api.groq.com/openai/v1/chat/completions" | |
| apify_client = ApifyClient(APIFY_TOKEN) | |
| app = FastAPI() | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=["*"], | |
| allow_credentials=True, | |
| allow_methods=["*"], | |
| allow_headers=["*"], | |
| ) | |
| # --- 2. MUAT MODEL SVM (LOKAL) --- | |
| print("Memuat model SVM dan TF-IDF Vectorizer...") | |
| try: | |
| model_svm = joblib.load('model_sentimen_smart_ayce.pkl') | |
| vectorizer = joblib.load('vectorizer_smart_ayce.pkl') | |
| print("✅ Model SVM berhasil dimuat!") | |
| except Exception as e: | |
| print(f"❌ ERROR: Gagal memuat model SVM: {e}") | |
| # --- ASPECT KEYWORD MAP (Smart Aspect Mapping) --- | |
| ASPECT_KEYWORDS = { | |
| "Kualitas Makanan": ["enak", "lezat", "makanan", "rasa", "menu", "segar", "matang", "mentah", "asin", "hambar", "bumbu", "daging", "seafood", "sushi", "dessert"], | |
| "Pelayanan": ["pelayan", "staf", "staff", "karyawan", "ramah", "jutek", "cepat", "responsif", "waitress", "waiter", "server"], | |
| "Kebersihan": ["bersih", "kotor", "higien", "AC", "toilet", "tempat", "meja", "lantai", "nyaman", "jorok", "bau"], | |
| "Harga": ["mahal", "murah", "harga", "worth", "ekonomis", "sebanding", "value", "bayar", "biaya", "promo"], | |
| "Variasi Menu": ["variasi", "pilihan", "beragam", "banyak menu", "macam", "jenis", "lengkap", "terbatas", "sedikit"], | |
| "Waktu Tunggu": ["antri", "antre", "lama", "tunggu", "queue", "booking", "reservasi", "penuh", "ramai"], | |
| } | |
| # --- FUNGSI PEMBERSIH TEKS (Untuk SVM) --- | |
| def bersihkan_teks(teks): | |
| teks = str(teks).lower() | |
| teks = re.sub(r'http\S+', '', teks) | |
| teks = re.sub(r'[^a-z\s]', '', teks) | |
| teks = re.sub(r'\s+', ' ', teks).strip() | |
| return teks | |
| # --- 3. FUNGSI ANALISIS SENTIMEN (FAST-SVM) --- | |
| def analyze_sentiment(ulasan_list): | |
| aspek_list = ["Kualitas Makanan", "Pelayanan", "Kebersihan", "Harga", "Variasi Menu", "Waktu Tunggu"] | |
| raw_scores = {k: {"pos": 0, "neu": 0, "neg": 0} for k in aspek_list} | |
| for teks in ulasan_list[:100]: | |
| try: | |
| teks_bersih = bersihkan_teks(teks) | |
| if not teks_bersih: | |
| continue | |
| teks_vektor = vectorizer.transform([teks_bersih]) | |
| sentiment = model_svm.predict(teks_vektor)[0] | |
| teks_lower = teks.lower() | |
| matched_aspects = [ | |
| asp for asp, keywords in ASPECT_KEYWORDS.items() | |
| if any(kw in teks_lower for kw in keywords) | |
| ] | |
| if not matched_aspects: | |
| matched_aspects = aspek_list | |
| for asp in matched_aspects: | |
| raw_scores[asp][sentiment] += 1 | |
| except Exception as e: | |
| print(f"Sentiment Error: {e}") | |
| return raw_scores | |
| # --- 4. FUNGSI REKOMENDASI DENGAN FALLBACK --- | |
| def get_advanced_insights(ulasan_list): | |
| gabungan_ulasan = " | ".join(ulasan_list[:50]) | |
| prompt = f""" | |
| Kamu adalah Konsultan Operasional Restoran. Tugasmu adalah membaca ulasan pelanggan berikut dan memberikan saran perbaikan kepada PEMILIK / MANAJEMEN restoran. | |
| Ulasan Pelanggan: | |
| {gabungan_ulasan} | |
| ATURAN MUTLAK: | |
| 1. Bagian "rekomendasi" HARUS ditujukan kepada MANAJEMEN restoran. | |
| 2. DILARANG KERAS memberikan rekomendasi bergaya promosi untuk pelanggan. | |
| 3. Batasi maksimal 30 kata kunci per kategori, dan maksimal 5 rekomendasi aksi. | |
| Kamu WAJIB mengembalikan respon HANYA dalam format JSON murni ini: | |
| {{ | |
| "kata_kunci_positif": ["kata1", "kata2"], | |
| "kata_kunci_negatif": ["kata1", "kata2"], | |
| "rekomendasi": [ | |
| {{"judul": "Tindakan Manajerial", "deskripsi": "Deskripsi tindakan untuk staf/manajemen", "prioritas": "High/Medium/Low"}} | |
| ] | |
| }} | |
| """ | |
| #PERCOBAAN 1: MENGGUNAKAN GROQ (Llama 3.1) | |
| try: | |
| print("Mencoba Groq API...") | |
| headers = { | |
| "Authorization": f"Bearer {GROQ_API_KEY}", | |
| "Content-Type": "application/json" | |
| } | |
| payload = { | |
| "model": "llama-3.1-8b-instant", | |
| "messages": [ | |
| {"role": "system", "content": "You are a strict restaurant business consultant advising the management. Always output valid JSON."}, | |
| {"role": "user", "content": prompt} | |
| ], | |
| "response_format": {"type": "json_object"}, | |
| "temperature": 0.4, | |
| "max_tokens": 2048 | |
| } | |
| response = requests.post(GROQ_URL, headers=headers, json=payload, timeout=20) | |
| if response.status_code == 200: | |
| data = response.json() | |
| t = data["choices"][0]["message"]["content"] | |
| return json.loads(t) | |
| else: | |
| print(f"Groq Gagal ({response.status_code}). Beralih ke Gemini...") | |
| raise Exception("Groq Timeout/Error") | |
| except Exception as e: | |
| #PERCOBAAN 2 (FALLBACK): MENGGUNAKAN GEMINI (Google GenAI SDK) | |
| print(f"Groq Error ({e}), Menjalankan Fallback Gemini via SDK...") | |
| if not GEMINI_API_KEY: | |
| return fallback_response() | |
| try: | |
| # Menggunakan format baru dari Google GenAI SDK | |
| client = genai.Client(api_key=GEMINI_API_KEY) | |
| gemini_response = client.models.generate_content( | |
| model="gemini-3.5-flash", | |
| contents=f"System: You are a strict restaurant business consultant advising the management. Always output valid JSON.\n\nUser: {prompt}" | |
| ) | |
| # Bersihkan teks kalau seandainya AI membalas pakai format markdown (```json ... ```) | |
| bersih = gemini_response.text.strip() | |
| if bersih.startswith("```json"): | |
| bersih = bersih[7:-3].strip() | |
| elif bersih.startswith("```"): | |
| bersih = bersih[3:-3].strip() | |
| return json.loads(bersih) | |
| except Exception as gemini_e: | |
| print(f"Gemini Error: {gemini_e}") | |
| return fallback_response() | |
| def fallback_response(): | |
| return { | |
| "kata_kunci_positif": ["Sistem Antre"], | |
| "kata_kunci_negatif": ["Server Sibuk"], | |
| "rekomendasi": [{"judul": "Sistem Sedang Sibuk", "deskripsi": "AI Rekomendasi sedang mengalami antrean (Groq & Gemini down). Silakan evaluasi data sentimen di atas secara manual sementara waktu.", "prioritas": "Low"}] | |
| } | |
| # --- 5. ENDPOINT --- | |
| async def analyze_realtime(request: Request): | |
| try: | |
| body = await request.json() | |
| target_url = body.get("url") | |
| run = apify_client.actor("compass/google-maps-reviews-scraper").call( | |
| run_input={"startUrls": [{"url": target_url}], "maxReviews": 150, "language": "id"} | |
| ) | |
| ulasan_lengkap = [] | |
| ulasan_teks_saja = [] | |
| for item in apify_client.dataset(run["defaultDatasetId"]).iterate_items(): | |
| if item.get("text"): | |
| ulasan_teks_saja.append(item["text"]) | |
| ulasan_lengkap.append({ | |
| "nama": item.get("name", "Anonim"), | |
| "rating": item.get("stars", 5), | |
| "teks": item["text"] | |
| }) | |
| if not ulasan_teks_saja: | |
| return {"status": "error", "message": "Ulasan tidak ditemukan."} | |
| raw_scores = analyze_sentiment(ulasan_teks_saja) | |
| insights_data = get_advanced_insights(ulasan_teks_saja) | |
| sentiment_results = [] | |
| tp = tn = tneu = 0 | |
| for name, counts in raw_scores.items(): | |
| total = counts["pos"] + counts["neu"] + counts["neg"] | |
| tp += counts["pos"]; tn += counts["neg"]; tneu += counts["neu"] | |
| p = round((counts["pos"]/total)*100) if total > 0 else 0 | |
| n = round((counts["neg"]/total)*100) if total > 0 else 0 | |
| neu = 100 - p - n if total > 0 else 100 | |
| sentiment_results.append({"name": name, "positive": p, "neutral": neu, "negative": n}) | |
| grand = tp + tn + tneu | |
| if grand == 0: | |
| over_p, over_neu, over_n = 0, 100, 0 | |
| else: | |
| over_p = round((tp/grand)*100, 1); over_n = round((tn/grand)*100, 1) | |
| over_neu = round(100 - over_p - over_n, 1) | |
| return { | |
| "status": "success", | |
| "analysis": { | |
| "overall_sentiment": {"positive": over_p, "neutral": over_neu, "negative": over_n}, | |
| "sentiment_score": sentiment_results, | |
| "kata_kunci": { | |
| "positif": insights_data.get("kata_kunci_positif", []), | |
| "negatif": insights_data.get("kata_kunci_negatif", []) | |
| }, | |
| "contoh_review": ulasan_lengkap[:6], | |
| "rekomendasi_list": insights_data.get("rekomendasi", []) | |
| } | |
| } | |
| except Exception as e: | |
| return {"status": "error", "message": str(e)} |