Update app.py

app.py

@@ -2,24 +2,20 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 
+model = tf.keras.models.load_model(r'C:\Users\thesu\anaconda3\envs\bird_modelV2.h5')
 
-
-
-model = tf.keras.models.load_model('bird_modelv2.h5')
-
-
-
-
-
-
-
-
-
-
-model = 
+label = ['Asian-Green-Bee-Eater', 'Brown-Headed-Barbet', 'Cattle-Egret', 'Common-Kingfisher', 'Common-Myna', 'Common-Rosefinch', 'Common-Tailorbird', 'Coppersmith-Barbet', 'Forest-Wagtail', 'Gray-Wagtail', 'Hoopoe', 'House-Crow', 'Indian-Grey-Hornbill', 'Indian-Peacock', 'Indian-Pitta', 'Indian-Roller', 'Jungle-Babbler', 'Northern-Lapwing', 'Red-Wattled-Lapwing', 'Ruddy-Shelduck', 'Rufous-Treepie', 'Sarus-Crane', 'White-Breasted-Kingfisher', 'White-Breasted-Waterhen', 'White-Wagtail']
 
 def image_classifier(inp):
-    return {'cat': 0.3, 'dog': 0.7}
-
-demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
-demo.launch()
+    img = tf.image.resize(inp, (224, 224))
+    img = tf.keras.preprocessing.image.img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    
+    predictions = model.predict(img)[0]
+    
+    return {label[i]: float(predictions[i]) for i in range(25)}
+    
+   
+
+demo = gr.Interface(fn=image_classifier, inputs='image',outputs=gr.Label(num_top_classes=5))
+demo.launch(debug=True)