Update app.py

app.py

@@ -1,62 +1,41 @@
 import gradio as gr
 import tensorflow as tf
-from tensorflow.keras.models import load_model
-import tensorflow_addons as tfa
-import os
 import numpy as np
+import os
 
+# Configuration
+HEIGHT, WIDTH = 224, 224
+NUM_CLASSES = 6
+LABELS = ["Burger King", "KFC", "McDonalds", "Other", "Starbucks", "Subway"]
 
-# labels= {'Burger King': 0, 'KFC': 1,'McDonalds': 2,'Other': 3,'Starbucks': 4,'Subway': 5}
-HEIGHT,WIDTH=224,224
-NUM_CLASSES=6
-
-model=load_model('best_model2.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 
-
-
-
+# Loading trained model
+model = tf.keras.models.load_model('best_model2.h5')
 
 def classify_image(inp):
     np.random.seed(143)
-    labels = {'Burger King': 1, 'KFC': 0, 'McDonalds': 2, 'Other': 3, 'Starbucks': 4, 'Subway': 5}
-    NUM_CLASSES = 6
+
+    # Preprocess input
     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 = ["Burger King", "KFC", "McDonalds", "Other", "Starbucks", "Subway"]
-
-    result = {label: float(f"{prediction[0][labels[label]]:.6f}") for label in label_order}
-
-    return result
-
-
-
-
-
-
-
- 
-
-
-
-    
-image = gr.Image(shape=(HEIGHT,WIDTH),label='Input')
-label = gr.Label(num_top_classes=4)
-
-gr.Interface(fn=classify_image, inputs=image, outputs=label, title='Brand Logo Detection').launch(debug=False)
-
 
+    # Prediction
+    prediction = model.predict(inp)
+    # Build a dict of label:confidence
+    return {LABELS[i]: float(f"{prediction[0][i]:.6f}") for i in range(NUM_CLASSES)}
+
+# Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(
+        label="Input Image",
+        source="upload",      # or "sketchpad", "webcam"
+        type="numpy",         # pass as numpy array to your function
+        height=HEIGHT,        # set display height :contentReference[oaicite:0]{index=0}
+        width=WIDTH           # set display width :contentReference[oaicite:1]{index=1}
+    ),
+    outputs=gr.Label(num_top_classes=4),
+    title="Brand Logo Detection"
+)
+
+if __name__ == "__main__":
+    iface.launch(debug=False)