Update app.py

app.py

@@ -14,15 +14,21 @@ spec = importlib.util.spec_from_file_location("modeling", class_path)
 modeling = importlib.util.module_from_spec(spec)
 spec.loader.exec_module(modeling)
 
-# Initialize the model
 from modeling import clip_lora_model
-emotion_model = clip_lora_model().to(device)
 
-# Load pretrained weights
+# Emotions model
+emotion_model = clip_lora_model().to(device)
 emotion_model_path = hf_hub_download(repo_id="PerceptCLIP/PerceptCLIP_Emotions", filename="perceptCLIP_Emotions.pth")
 emotion_model.load_state_dict(torch.load(emotion_model_path, map_location=device))
 emotion_model.eval()
 
+# Memorability model
+mem_model = clip_lora_model(output_dim=1).to(device)
+mem_model_path = hf_hub_download(repo_id="PerceptCLIP/PerceptCLIP_Memorability", filename="perceptCLIP_Memorability.pth")
+mem_model.load_state_dict(torch.load(mem_model_path, map_location=device))
+mem_model.eval()
+
+
 # Emotion label mapping
 idx2label = {
     0: "amusement",
@@ -76,10 +82,7 @@ def predict_emotion(image):
     emoji = emotion_emoji.get(emotion, "❓")  
     return f"{emotion} {emoji}", f"{mem_score:.4f}"
     
-mem_model = clip_lora_model(output_dim=1).to(device)
-mem_model_path = hf_hub_download(repo_id="PerceptCLIP/PerceptCLIP_Memorability", filename="perceptCLIP_Memorability.pth")
-mem_model.load_state_dict(torch.load(mem_model_path, map_location=device))
-mem_model.eval()
+
 
 # Example images 
 example_images = [