Update app.py

app.py

@@ -1,32 +1,29 @@
 from fastai.vision.all import *
 import gradio as gr
 import numpy as np
-
+from PIL import Image as PILImage
 
 learn = load_learner('model.pkl')
-
-
-
-categories = (['calling', 'clapping', 'cycling', 'dancing', 'drinking', 'eating', 'fighting', 'hugging', 
-              'laughing', 'listening_to_music', 'running', 'sitting', 'sleeping', 'texting', 'using_laptop'])
-
-
+categories = ['calling', 'clapping', 'cycling', 'dancing', 'drinking', 'eating', 'fighting', 'hugging', 'laughing', 'listening_to_music', 'running', 'sitting', 'sleeping', 'texting', 'using_laptop']
 
 def classify_image(img):
-    img = np.array(img)
-    img = PILImage.create(img).resize((192, 192))
-    pred,idx,probs = learn.predict(img)
-    return dict(zip(categories, map(float,probs)))
-
-
+    # Convert to RGB if the image is in RGBA mode
+    if img.mode == 'RGBA':
+        img = img.convert('RGB')
+    
+    # Resize the image
+    img = img.resize((192, 192))
+    
+    # Convert to fastai PILImage
+    fastai_img = PILImage.create(np.array(img))
+    
+    # Make prediction
+    pred, idx, probs = learn.predict(fastai_img)
+    return dict(zip(categories, map(float, probs)))
 
 image = gr.Image(type='pil')
 label = gr.Label()
 examples = ['laughing.jpg', 'dancing.jpg', 'drinking.jpg']
 
 intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-intf.launch(inline=False)
-
-
-
-
+intf.launch(inline=False)