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from fastapi import FastAPI
from pydantic import BaseModel
from typing import Optional
import torch
import subprocess
import json
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 = "best_indobertweet.pth"
# =====================================================
# 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:
return
logger.info("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
logger.info("Downloading fine-tuned weights...")
model_path = hf_hub_download(repo_id=HF_MODEL_REPO, filename=PT_FILE)
logger.info("Loading base model architecture...")
model = BertForSequenceClassification.from_pretrained(
BASE_MODEL,
num_labels=2
)
logger.info("Loading weight .pth...")
state_dict = torch.load(model_path, map_location="cpu")
model.load_state_dict(state_dict, strict=True)
model.to("cpu")
model.eval()
logger.info("MODEL READY")
# =====================================================
# FASTAPI
# =====================================================
app = FastAPI(title="Stress Detection API")
@app.on_event("startup")
def startup_event():
load_model_once()
class StressResponse(BaseModel):
message: str
data: Optional[dict] = None
# =====================================================
# SNSCRAPE FETCH TWEETS
# =====================================================
def fetch_tweets_snscrape(username, limit=50):
tweets = []
try:
command = [
"snscrape",
"--jsonl",
"--max-results", str(limit),
f"twitter-user {username}"
]
result = subprocess.run(command, capture_output=True, text=True)
if result.returncode != 0:
return None
for line in result.stdout.splitlines():
item = json.loads(line)
if "content" in item:
tweets.append(item["content"])
return tweets
except Exception:
return None
# =====================================================
# KEYWORD EXTRACTION
# =====================================================
def extract_keywords(tweets):
stress_words = [
"capek", "cape", "capai", "letih", "lelah", "pusing",
"stress", "stres", "burnout", "kesal", "badmood",
"sedih", "tertekan", "muak", "bosan"
]
found = set()
for t in tweets:
lower = t.lower()
for word in stress_words:
if word in lower:
found.add(word)
return list(found)
# =====================================================
# MODEL 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
# =====================================================
# API ROUTE
# =====================================================
@app.get("/analyze/{username}", response_model=StressResponse)
def analyze(username: str):
tweets = fetch_tweets_snscrape(username)
if tweets is None or len(tweets) == 0:
return StressResponse(message="No tweets available", data=None)
labels = [predict_stress(t) 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, # kalau tidak ketemu => []
"stress_status": status
}
)
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