Update app.py

app.py

@@ -1,5 +1,4 @@
 import os
-# Set the Keras backend to JAX (must be done before importing keras)
 os.environ["KERAS_BACKEND"] = "jax"
 
 from fastapi import FastAPI, File, UploadFile
@@ -11,11 +10,14 @@ from PIL import Image
 import io
 from huggingface_hub import hf_hub_download
 
-
-# Initialize FastAPI
 app = FastAPI()
 
-# Load model and emotion config
+# Add root route for testing
+@app.get("/")
+def read_root():
+    return {"message": "Facial Emotion API is running 🚀"}
+
+# Load model and config
 model = None
 desired_emotions = ['happy', 'sad', 'neutral']
 original_emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
@@ -24,36 +26,28 @@ desired_indices = [original_emotion_labels.index(emotion) for emotion in desired
 @app.on_event("startup")
 def load_emotion_model():
     global model
-    try:
-        print("🔄 Downloading model from HuggingFace Hub...")
-        model_path = hf_hub_download(repo_id="Shees7/facial_model", filename="emotion_model.keras")
-        print("✅ Model file downloaded at:", model_path)
-        model = keras.saving.load_model(model_path)
-        print("✅ Model loaded successfully.")
-    except Exception as e:
-        print("❌ Failed to load model:", str(e))
-
+    print("🔄 Downloading model from HuggingFace Hub...")
+    model_path = hf_hub_download(repo_id="Shees7/facial_model", filename="emotion_model.keras")
+    print("✅ Model file downloaded at:", model_path)
+    model = keras.saving.load_model(model_path)
+    print("✅ Model loaded successfully.")
 
 def preprocess_face(image_bytes):
-    try:
-        np_img = np.array(Image.open(io.BytesIO(image_bytes)).convert('RGB'))
-        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-        gray = cv2.cvtColor(np_img, cv2.COLOR_RGB2GRAY)
-        faces = face_cascade.detectMultiScale(gray, 1.1, 4)
+    np_img = np.array(Image.open(io.BytesIO(image_bytes)).convert('RGB'))
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    gray = cv2.cvtColor(np_img, cv2.COLOR_RGB2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.1, 4)
 
-        if len(faces) == 0:
-            return None
-
-        x, y, w, h = faces[0]
-        face = np_img[y:y+h, x:x+w]
-        face_resized = cv2.resize(face, (224, 224))
-        face_normalized = face_resized / 255.0
-        face_expanded = np.expand_dims(face_normalized, axis=0)
-        return face_expanded
-    except Exception as e:
-        print("❌ Error during preprocessing:", str(e))
+    if len(faces) == 0:
         return None
 
+    x, y, w, h = faces[0]
+    face = np_img[y:y+h, x:x+w]
+    face_resized = cv2.resize(face, (224, 224))
+    face_normalized = face_resized / 255.0
+    face_expanded = np.expand_dims(face_normalized, axis=0)
+    return face_expanded
+
 @app.post("/predict")
 async def predict_emotion(file: UploadFile = File(...)):
     if model is None: