Update app.py

app.py

@@ -59,11 +59,11 @@ def emo_preprocess(image):
 
 # Inference function
 def predict_emotion(image):
-    # If the image is passed as a PIL Image, you can directly use it
+    # If the image is passed as a PIL Image
     if isinstance(image, Image.Image):  
         img = image.convert("RGB")
     else:
-        img = Image.open(image).convert("RGB")  # In case the input is a path or something else
+        img = Image.open(image).convert("RGB") 
     
     img = emo_preprocess(img)
 
@@ -72,10 +72,10 @@ def predict_emotion(image):
         predicted = outputs.argmax(1).item()
 
     emotion = idx2label[predicted]
-    emoji = emotion_emoji.get(emotion, "❓")  # Default to "?" if no emoji found
+    emoji = emotion_emoji.get(emotion, "❓")  
     return f"{emotion} {emoji}"
 
-# Example images (provide local file paths or URLs)
+# Example images 
 example_images = [
     "https://img.freepik.com/free-photo/emotive-excited-female-with-dark-skin-crisp-hair-keeps-hands-clenched-fists-exclaims-with-positiveness-as-achieved-success-her-career-opens-mouth-widely-isolated-white-wall_273609-16443.jpg",
     "https://t4.ftcdn.net/jpg/01/18/44/59/360_F_118445958_NtP7tIsD0CBPyG7Uad7Z2KxVWrsfCPjP.jpg", 
@@ -90,7 +90,7 @@ iface = gr.Interface(
     outputs=gr.Textbox(label="Emotion"),
     title="PerceptCLIP-Emotions",
     description="This model predicts the emotion evoked by an image and returns the corresponding emoji along with the emotion name.",
-    examples=example_images  # Add example images here
+    examples=example_images  
 )
 
 if __name__ == "__main__":