Update app.py

app.py

@@ -4,25 +4,17 @@ import torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 # -------------------------------------------------
-# CONFIG (MentalBERT – gated & fine-tuned)
+# CONFIG
 # -------------------------------------------------
 MODEL_NAME = "j-hartmann/emotion-english-distilroberta-base"
-HF_TOKEN = True  # uses HF_TOKEN env variable
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # -------------------------------------------------
-# LOAD MODEL (ONCE)
+# LOAD MODEL (ONCE AT STARTUP)
 # -------------------------------------------------
-tokenizer = AutoTokenizer.from_pretrained(
-    MODEL_NAME,
-    token=HF_TOKEN
-)
-
-model = AutoModelForSequenceClassification.from_pretrained(
-    MODEL_NAME,
-    token=HF_TOKEN
-)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
 
 model.to(device)
 model.eval()
@@ -30,13 +22,26 @@ model.eval()
 # -------------------------------------------------
 # FASTAPI APP
 # -------------------------------------------------
-app = FastAPI(title="MentalBERT Emotion API")
+app = FastAPI(title="Emotion Detection API")
 
+# ✅ ROOT ROUTE (CRITICAL FOR HF SPACES)
+@app.get("/")
+def health():
+    """
+    Health / wake-up endpoint.
+    Hugging Face uses this to wake the Space.
+    """
+    return {"status": "ok"}
 
+# -------------------------------------------------
+# REQUEST SCHEMA
+# -------------------------------------------------
 class EmotionRequest(BaseModel):
     text: str
 
-
+# -------------------------------------------------
+# EMOTION ENDPOINT
+# -------------------------------------------------
 @app.post("/emotion")
 def classify_emotion(payload: EmotionRequest):
     text = payload.text.strip()
@@ -62,4 +67,4 @@ def classify_emotion(payload: EmotionRequest):
     return {
         "emotion": model.config.id2label[pred_id],
         "confidence": round(probs[0][pred_id].item(), 4),
-    }
+    }