Update app.py

app.py

@@ -1,29 +1,24 @@
 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):
-    # 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)))
+    pred, idx, probs = learn.predict(img)
+    return {cat: float(prob) for cat, prob in zip(categories, probs)}
 
-image = gr.Image(type='pil')
-label = gr.Label()
-examples = ['laughing.jpg', 'dancing.jpg', 'drinking.jpg']
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(shape=(192, 192)),
+    outputs=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)
+demo.launch()