Update app.py

app.py

@@ -1,65 +1,27 @@
+# examples = [["Examples/img1.png"], ["Examples/img2.png"],["Examples/img3.png"], ["Examples/img4.png"]]
+
 import gradio as gr
 import tensorflow as tf
 import numpy as np
 from tensorflow.keras.models import load_model
-import tensorflow_addons as tfa
-import os
-import numpy as np
-
-
-HEIGHT,WIDTH=224,224
-IMG_SIZE=224
-model=load_model('Models/best_model1.h5')
-
-# def classify_image(inp):
-#   np.random.seed(143)
-#   inp = inp.reshape((-1, HEIGHT,WIDTH, 3))
-#   inp = tf.keras.applications.nasnet.preprocess_input(inp) 
-#   prediction = model.predict(inp)
-#   ###label = dict((v,k) for k,v in labels.items())
-#   predicted_class_indices=np.argmax(prediction,axis=1)
-#   result = {}
-#   for i in range(len(predicted_class_indices)):
-#       if predicted_class_indices[i] < NUM_CLASSES:
-#           result[labels[predicted_class_indices[i]]]= float(predicted_class_indices[i])
-#   return result 
 
-# def classify_image(inp):
-#     np.random.seed(143)
-#     labels = {'Cat': 0, 'Dog': 1}
-#     NUM_CLASSES = 2
-#     #inp = inp.reshape((-1, HEIGHT, WIDTH, 3))
-#     #inp = tf.keras.applications.nasnet.preprocess_input(inp)
-#     prediction = model.predict(inp)
-#     predicted_class_indices = np.argmax(prediction, axis=1)
-
-#     label_order = ["Cat","Dog"]
-
-#     result = {label: float(f"{prediction[0][labels[label]]:.6f}") for label in label_order}
-
-#     return result
+HEIGHT, WIDTH = 224, 224
+IMG_SIZE = 224
+model = load_model('Models/best_model1.h5')
 
 def classify_image(inp):
-    NUM_CLASSES=2
-    # Resize the image to the required size
-    labels = ['Cat','Dog']
+    NUM_CLASSES = 2
+    labels = ['Cat', 'Dog']
     inp = tf.image.resize(inp, [IMG_SIZE, IMG_SIZE])
-    inp = inp.numpy()
-    inp = inp.reshape((-1, IMG_SIZE, IMG_SIZE, 3))
+    inp = inp.numpy().reshape((-1, IMG_SIZE, IMG_SIZE, 3))
     inp = tf.keras.applications.vgg16.preprocess_input(inp)
     prediction = model.predict(inp).flatten()
-    return {labels[i]: f"{prediction[i]:.6f}" for i in range(NUM_CLASSES)}
+    return {labels[i]: float(prediction[i]) for i in range(NUM_CLASSES)}  # Fixed: return floats
 
-image = gr.Image(height=HEIGHT,width=WIDTH,label='Input')
+image = gr.Image(height=HEIGHT, width=WIDTH, label='Input')
 label = gr.Label(num_top_classes=2)
 
-examples = [
-    ["Examples/img1.png"],
-    ["Examples/img2.png"],
-    ["Examples/img3.png"],
-    ["Examples/img4.png"]    
-]
-
+examples = [["Examples/img1.png"], ["Examples/img2.png"],["Examples/img3.png"], ["Examples/img4.png"]]
 
 gr.Interface(
     fn=classify_image, 
@@ -67,4 +29,4 @@ gr.Interface(
     outputs=label, 
     title='Smart Pet Classifier',
     examples=examples
-).launch(debug=False)
+).launch(debug=False)