Update app.py

app.py

@@ -12,23 +12,18 @@ import os
 
 model_url = "https://huggingface.co/foryahasake/act-h5/resolve/main/bestXceptionPlusData.h5"
 model_filename = "bestXceptionPlusData.h5"
+from huggingface_hub import from_pretrained_keras
 
-if not os.path.exists(model_filename):
-    print("Downloading model file...")
-    response = requests.get(model_url)
-    with open(model_filename, "wb") as f:
-        f.write(response.content)
-    print("Model downloaded successfully!")
-
-model = tf_keras.models.load_model(model_filename, compile=False, custom_objects={'tf': tf})
+model = from_pretrained_keras( "foryahasake/act-h5")
 
 def inference(inp):
     if len(inp.shape) == 3:  # If the image is colored
         pil_gray = cv2.cvtColor(inp, cv2.COLOR_RGB2GRAY)
     else:
         pil_gray = inp
+    resized_img = cv2.resize(pil_gray, (48,48), interpolation=cv2.INTER_CUBIC)
 
-    model_input = pil_gray / 255.0
+    model_input = resized_img / 255.0
     model_input = np.expand_dims(model_input, axis=-1)  # Add channel dimension
     model_input = np.expand_dims(model_input, axis=0)   # Add batch dimension
 
@@ -42,4 +37,4 @@ def inference(inp):
     return predicted_emotion
 
 demo = gr.Interface(fn=inference, inputs="image", outputs="label")
-demo.launch()
+demo.launch()