Create app.py

app.py

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+from fastapi import FastAPI, HTTPException, Header
+from pydantic import BaseModel
+import numpy as np
+from tensorflow.keras.models import load_model
+from fastapi.middleware.cors import CORSMiddleware
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load model Keras untuk prediksi stres
+model = load_model("model_stresss.h5")
+labels = ['Tidak Stress', 'Sedikit Stress', 'Normal', 'Stress', 'Sangat Stress']
+
+# Load model Hugging Face
+model_dir = "Chipan/indobert-emotion"
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+model_bert = AutoModelForSequenceClassification.from_pretrained(model_dir)
+model_bert.eval()
+
+# Mapping label untuk emosi
+label_map = {0: "Bersyukur", 1: "Marah", 2: "Sedih", 3: "Senang", 4: "Stress"}
+
+# Setup FastAPI
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Data schemas
+class CheckInData(BaseModel):
+    mood: float
+    sleep: float
+    anxiety: float
+    exercise: float
+    support: float
+
+class TextInput(BaseModel):
+    text: str
+
+@app.post("/predict")
+def predict(data: CheckInData, authorization: str = Header(None)):
+    try:
+        raw = np.array([[data.mood, data.sleep, data.anxiety, data.exercise, data.support]])
+        prediction = model.predict(raw)
+        idx = int(np.argmax(prediction))
+        return {
+            "predicted_index": idx,
+            "predicted_label": labels[idx],
+            "raw_prediction": prediction.tolist()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
+
+@app.post("/analyze")
+def analyze_emotion(input: TextInput):
+    try:
+        inputs = tokenizer(input.text, return_tensors="pt", padding=True, truncation=True, max_length=128)
+        with torch.no_grad():
+            logits = model_bert(**inputs).logits
+            probs = F.softmax(logits, dim=1)
+            idx = int(torch.argmax(probs))
+        return {
+            "emotion": label_map.get(idx, "unknown"),
+            "confidence": round(probs[0, idx].item(), 4)
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Emotion analysis error: {str(e)}")
+
+@app.get("/")
+def root():
+    return {"status": "ok"}