Deployment of FastAPI model service

.gitattributes

@@ -1,35 +1 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+backend/src/models/*.pth filter=lfs diff=lfs merge=lfs -text

api_client.py

@@ -0,0 +1,15 @@
+# model-fastapi/api_client.py
+import tweepy
+import os # Import modul os untuk mengakses variabel lingkungan
+
+# Dapatkan token dari variabel lingkungan TWITTER_BEARER_TOKEN
+# Jika token tidak ditemukan, client tidak akan terinisialisasi
+BEARER_TOKEN = os.environ.get("AAAAAAAAAAAAAAAAAAAAAOGp3AEAAAAAMEaOafsh1pNGVFrK%2BN2atq0Cba4%3DE2Gw0MDFfJ1bE4veBIIxhOUqbaqQKOqRxMhGybH4FfOETDNpow") 
+
+if not BEARER_TOKEN:
+    raise ValueError("TWITTER_BEARER_TOKEN tidak ditemukan di environment variables.")
+
+client = tweepy.Client(bearer_token=BEARER_TOKEN) 
+
+def get_tweets_by_username(username: str, count: int = 100):
+    pass

main.py

@@ -0,0 +1,153 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import Dict, Optional, List
+import numpy as np # Untuk operasi np.argmax
+import os # Untuk os.environ (Bearer Token)
+
+# Pustaka ML
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Import dari file lokal
+from .api_client import get_tweets_by_username 
+from .preprocessing import preprocess_text
+
+app = FastAPI()
+
+# --- KONFIGURASI PATH ---
+MODEL_DIR = "./model_assets" 
+WEIGHTS_FILE = "./model_assets/best_indobertweet.pth" 
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# --- PEMUATAN MODEL ---
+# Logika 3 Tahap: 1. Muat Tokenizer, 2. Muat Struktur Model, 3. Muat Bobot .pth
+try:
+    print("Mencoba memuat model...")
+    # 1. Muat Tokenizer & Struktur
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
+    model = AutoModelForSequenceClassification.from_pretrained(MODEL_DIR, num_labels=2)
+    
+    # 2. Muat Bobot .pth
+    # Setting map_location ke 'cpu' memastikan bisa dimuat bahkan jika server tidak punya GPU
+    state_dict = torch.load(WEIGHTS_FILE, map_location=DEVICE) 
+    model.load_state_dict(state_dict)
+    
+    # 3. Finalisasi
+    model.to(DEVICE)
+    model.eval()
+    print(f"Model berhasil dimuat ke device: {DEVICE}")
+
+except Exception as e:
+    print(f"FATAL ERROR: Gagal memuat model. Pastikan file di {MODEL_DIR} sudah benar.")
+    print(e)
+    # Anda bisa memilih untuk raise error di sini untuk menghentikan server jika model gagal dimuat.
+
+# --- DEFINISI PYDANTIC MODELS ---
+
+# 1. Skema Permintaan (DATA YANG DITERIMA DARI Express.js)
+class StressRequest(BaseModel):
+    x_username: str
+    tweet_count: int = 100 
+
+# 2. Skema Data Hasil (DATA YANG DISIMPAN KE DB & DIKIRIM KE Frontend)
+class ResultData(BaseModel):
+    x_username: str
+    total_tweets: int
+    stress_level: int       # Skor 0-100 (Probabilitas positif dikalikan 100)
+    keywords: Dict[str, float] # Contoh Placeholder: Tren Kata
+    stress_status: int      # 0: Aman, 1: Rendah, 2: Sedang, 3: Tinggi
+
+# 3. Skema Respons API Akhir
+class APIResponse(BaseModel):
+    message: str
+    data: Optional[ResultData]
+
+# --- UTILITY FUNCTIONS ---
+
+def calculate_stress_status(stress_level: float) -> int:
+    """Mengkonversi skor probabilitas (0-100) menjadi status diskrit."""
+    if stress_level >= 75:
+        return 3  # Tinggi
+    elif stress_level >= 50:
+        return 2  # Sedang
+    elif stress_level >= 25:
+        return 1  # Rendah
+    else:
+        return 0  # Aman
+
+# --- ENDPOINT UTAMA ---
+
+@app.post("/api/predict_stress", response_model=APIResponse)
+def predict_stress(request: StressRequest):
+    username = request.x_username
+    tweet_count = request.tweet_count
+    
+    # Cek Bearer Token sebelum memanggil API Twitter
+    if not os.environ.get("TWITTER_BEARER_TOKEN"):
+        return APIResponse(
+            message="Error: TWITTER_BEARER_TOKEN tidak diatur sebagai environment variable.",
+            data=None
+        )
+
+    # 1. Ambil Tweet
+    raw_tweets = get_tweets_by_username(username, tweet_count)
+
+    if not raw_tweets:
+        return APIResponse(
+            message=f"Gagal mengambil tweet dari @{username}. Akun mungkin private atau tidak ditemukan.",
+            data=None
+        )
+
+    # 2. Pre-processing dan Inferensi
+    cleaned_texts = [preprocess_text(t) for t in raw_tweets]
+    
+    # Inisialisasi list untuk menyimpan probabilitas stress (kelas 1)
+    stress_probabilities = []
+    
+    with torch.no_grad():
+        for text in cleaned_texts:
+            # Tokenisasi
+            enc = tokenizer(
+                text,
+                truncation=True,
+                padding="max_length",
+                max_length=128, # Konsisten dengan training
+                return_tensors="pt"
+            )
+            
+            # Pindahkan ke device dan inferensi
+            input_ids = enc["input_ids"].to(DEVICE)
+            attention_mask = enc["attention_mask"].to(DEVICE)
+            
+            outputs = model(input_ids=input_ids, attention_mask=attention_mask)
+            logits = outputs.logits
+            
+            # Ambil probabilitas untuk kelas 1 (Stress)
+            probs = torch.softmax(logits, dim=1).cpu().numpy()[0]
+            stress_probabilities.append(probs[1]) 
+
+    # 3. Agregasi Hasil
+    if not stress_probabilities:
+        # Jika ada tweets tapi semuanya kosong setelah pre-processing
+        avg_stress_score = 0
+    else:
+        # Hitung rata-rata probabilitas stres dari semua tweet (skor 0.0 - 1.0)
+        avg_stress_score = np.mean(stress_probabilities) 
+
+    # Konversi ke skala 0-100
+    stress_level_100 = int(round(avg_stress_score * 100))
+    status = calculate_stress_status(stress_level_100)
+    
+    # 4. Susun Respons
+    result_data = ResultData(
+        x_username=username,
+        total_tweets=len(raw_tweets),
+        stress_level=stress_level_100,
+        keywords={"placeholder": 0.0}, # Implementasi penambangan keyword akan dilakukan belakangan
+        stress_status=status
+    )
+    
+    return APIResponse(
+        message=f"Analisis stres untuk @{username} berhasil. Ditemukan {result_data.total_tweets} tweets.",
+        data=result_data
+    )

preprocessing.py

@@ -0,0 +1,16 @@
+# model-fastapi/preprocessing.py
+import re
+import emoji
+
+def clean_text(t):
+    t = str(t).lower()
+    t = re.sub(r"http\S+|www\.\S+", "", t) 
+    t = re.sub(r"@\w+", "", t)             
+    t = re.sub(r"#(\w+)", r"\1", t)        
+    t = re.sub(r"\s+", " ", t).strip()     
+    # Jika Anda menggunakan emoji.replace_emoji() di Colab, pastikan versi Python Anda mendukungnya, atau gunakan pustaka yang diimpor.
+    # Contoh: t = emoji.replace_emoji(t, replace="")
+    return t
+
+def preprocess_text(text: str) -> str:
+    return clean_text(text)

requirements.txt

@@ -0,0 +1,8 @@
+fastapi
+uvicorn[standard]
+python-multipart
+transformers
+torch
+pandas
+tweepy 
+emoji