Update app.py

app.py

@@ -3,30 +3,26 @@ from transformers import AutoModelForImageClassification, AutoImageProcessor
 from PIL import Image
 import torch
 
-# 你的模型 ID
 model_id = "SEAR01/FER_model"
-processor = AutoImageProcessor.from_pretrained(model_id)
-model = AutoModelForImageClassification.from_pretrained(model_id)
+try:
+    processor = AutoImageProcessor.from_pretrained(model_id)
+    model = AutoModelForImageClassification.from_pretrained(model_id, trust_remote_code=True)  # 加這行允許自訂模型
+except Exception as e:
+    raise ValueError(f"Model load failed: {e}. Check repo files.")
+
 emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']
 
 def predict_emotion(image):
     if image is None:
-        return "Please upload an image."
+        return "Upload an image."
     inputs = processor(images=image, return_tensors="pt")
     with torch.no_grad():
         outputs = model(**inputs)
         predicted = outputs.logits.argmax(-1).item()
     emotion = emotion_labels[predicted]
     confidence = torch.softmax(outputs.logits, dim=-1)[0][predicted].item()
-    return f"Detected emotion: {emotion}\nConfidence: {confidence:.2f}"
+    return f"Emotion: {emotion} (Confidence: {confidence:.2f})"
 
-# Gradio 介面
-iface = gr.Interface(
-    fn=predict_emotion,
-    inputs=gr.Image(type="pil", label="Upload a face image"),
-    outputs=gr.Textbox(label="Prediction"),
-    title="FER Mental Health Detector",
-    description="Upload an image to detect emotions and assess mental health risk."
-)
+iface = gr.Interface(fn=predict_emotion, inputs=gr.Image(type="pil"), outputs="text", title="FER Demo")
 if __name__ == "__main__":
     iface.launch()