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Dockerfile

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+# Use official Python runtime
+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first (for better caching)
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY app.py .
+
+# Expose port 7860 (required by HF Spaces)
+EXPOSE 7860
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
+    CMD curl -f http://localhost:7860/health || exit 1
+
+# Run the application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "1"]

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import Optional
+import requests
+import torch
+from transformers import AutoTokenizer, BertForSequenceClassification
+from huggingface_hub import hf_hub_download
+
+import logging
+logger = logging.getLogger("app")
+logging.basicConfig(level=logging.INFO)
+
+# =====================================================
+# CONFIG
+# =====================================================
+HF_MODEL_REPO = "gaidasalsaa/indobertweet-xstress-model"
+BASE_MODEL = "indolem/indobertweet-base-uncased"
+PT_FILE = "model_indobertweet.pth"
+
+BEARER_TOKEN = "AAAAAAAAAAAAAAAAAAAAADXr5gEAAAAAnQZgkYRrC4iM5WTblBxDyt58oj8%3DriQZkuHuvRL6Suc3rmDhD3umqbHaxwim2Tfb34rfQpnKqf9Xhd"
+
+# =====================================================
+# GLOBAL MODEL STORAGE
+# =====================================================
+tokenizer = None
+model = None
+
+
+# =====================================================
+# LOAD MODEL 
+# =====================================================
+def load_model_once():
+    global tokenizer, model
+
+    if tokenizer is not None and model is not None:
+        logger.info("Model already loaded.")
+        return
+
+    logger.info("Starting model loading...")
+
+    device = "cpu"
+    logger.info(f"Using device: {device}")
+
+    # ---- load tokenizer ----
+    logger.info("Loading tokenizer...")
+    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+    logger.info("Tokenizer loaded")
+
+    # ---- download .pth ----
+    logger.info("Downloading best_indobertweet.pth...")
+    model_path = hf_hub_download(
+        repo_id=HF_MODEL_REPO,
+        filename=PT_FILE,
+    )
+    logger.info(f"Model file downloaded: {model_path}")
+
+    logger.info("Loading base model architecture...")
+    model = BertForSequenceClassification.from_pretrained(
+        BASE_MODEL,
+        num_labels=2,
+    )
+
+    logger.info("Loading fine-tuned weights (.pth)...")
+    state_dict = torch.load(model_path, map_location="cpu")
+
+    model.load_state_dict(state_dict, strict=True)
+    logger.info("Weights loaded successfully")
+
+    model.to(device)
+    model.eval()
+
+    logger.info("MODEL READY")
+
+
+# =====================================================
+# FASTAPI
+# =====================================================
+app = FastAPI(title="Stress Detection API")
+
+
+@app.on_event("startup")
+def startup_event():
+    logger.info("Starting model loading on startup...")
+    load_model_once()
+
+
+class StressResponse(BaseModel):
+    message: str
+    data: Optional[dict] = None
+
+
+# =====================================================
+# TWITTER API
+# =====================================================
+def get_user_id(username):
+    url = f"https://api.x.com/2/users/by/username/{username}"
+    headers = {"Authorization": f"Bearer {BEARER_TOKEN}"}
+    r = requests.get(url, headers=headers)
+    if r.status_code != 200:
+        return None, r.json()
+    return r.json()["data"]["id"], r.json()
+
+
+def fetch_tweets(user_id, limit=25):
+    url = f"https://api.x.com/2/users/{user_id}/tweets"
+    params = {"max_results": limit, "tweet.fields": "id,text,created_at"}
+    headers = {"Authorization": f"Bearer {BEARER_TOKEN}"}
+    r = requests.get(url, headers=headers, params=params)
+    if r.status_code != 200:
+        return None, r.json()
+    tweets = r.json().get("data", [])
+    return [t["text"] for t in tweets], r.json()
+
+
+# =====================================================
+# KEYWORD EXTRACTION
+# =====================================================
+def extract_keywords(tweets):
+    stress_words = [
+    "gelisah","cemas","tidur","takut","hati",
+    "resah","sampe","tenang","suka","mulu",
+    "sedih","ngerasa","gimana","gatau",
+    "perasaan","nangis","deg","khawatir",
+    "pikiran","harap","gabisa","bener","pengen",
+    "sakit","susah","bangun","biar","jam","kaya",
+    "bingung","mikir","tuhan","mikirin",
+    "bawaannya","marah","tbtb","anjir","cape",
+    "panik","enak","kali","pusing","semoga",
+    "kadang","langsung","kemarin","tugas",
+    "males"
+    ]
+
+    found = set()
+    for t in tweets:
+        lower = t.lower()
+        for word in stress_words:
+            if word in lower:
+                found.add(word)
+
+    return list(found)
+
+
+# =====================================================
+# INFERENCE
+# =====================================================
+def predict_stress(text):
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        padding=True,
+        max_length=128
+    )
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=1)[0]
+
+    label = torch.argmax(probs).item()
+    return label, float(probs[1])
+
+
+# =====================================================
+# API ROUTE
+# =====================================================
+@app.get("/analyze/{username}", response_model=StressResponse)
+def analyze(username: str):
+    user_id, _ = get_user_id(username)
+    if user_id is None:
+        return StressResponse(message="Failed to fetch profile", data=None)
+
+    tweets, _ = fetch_tweets(user_id)
+    if not tweets:
+        return StressResponse(message="No tweets available", data=None)
+
+    labels = [predict_stress(t)[0] for t in tweets]
+
+    stress_percentage = round(sum(labels) / len(labels) * 100, 2)
+
+    if stress_percentage <= 25:
+        status = 0
+    elif stress_percentage <= 50:
+        status = 1
+    elif stress_percentage <= 75:
+        status = 2
+    else:
+        status = 3
+
+    keywords = extract_keywords(tweets)
+
+    return StressResponse(
+        message="Analysis complete",
+        data={
+            "username": username,
+            "total_tweets": len(tweets),
+            "stress_level": stress_percentage,
+            "keywords": keywords,
+            "stress_status": status
+        }
+    )

requirements.txt

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+fastapi==0.115.6
+uvicorn[standard]==0.34.0
+torch==2.5.1
+transformers==4.48.0
+huggingface-hub==0.27.1
+requests==2.32.3
+pydantic==2.10.6