Update app.py

app.py

@@ -1,54 +1,3 @@
-# import gradio as gr
-# import tensorflow as tf
-# import numpy as np
-# import os
-
-# # Configuration
-# HEIGHT, WIDTH = 224, 224
-# NUM_CLASSES = 6
-# LABELS = [ "McDonalds","Burger King","Subway", "Starbucks", "KFC","Other"]
-# from tensorflow_addons.metrics import F1Score
-# from keras.utils import custom_object_scope
-
-# with custom_object_scope({'Addons>F1Score': F1Score}):
-#     model = tf.keras.models.load_model('best_model.h5')
-
-
-# def classify_image(inp):
-#     np.random.seed(143)
-
-#     # Ensure input is resized to expected shape
-#     inp = tf.image.resize(inp, [HEIGHT, WIDTH])
-#     inp = tf.cast(inp, tf.float32)  # ensure correct dtype for preprocessing
-#     inp = tf.keras.applications.nasnet.preprocess_input(inp)
-#     inp = tf.expand_dims(inp, axis=0)  # make batch dimension
-
-#     # Prediction
-#     prediction = model.predict(inp)
-
-#     return {LABELS[i]: float(f"{prediction[0][i]:.6f}") for i in range(NUM_CLASSES)}
-
-
-
-
-# iface = gr.Interface(
-#     fn=classify_image,
-#     inputs=gr.Image(
-#         label="Input Image",
-#         source="upload",
-#         type="numpy",
-#         height=HEIGHT,
-#         width=WIDTH
-#     ),
-#     outputs=gr.Label(num_top_classes=4),
-#     title="Brand Logo Detection",
-#     examples=example_list
-# )
-
-# if __name__ == "__main__":
-#     iface.launch(debug=False,share=True)
-
-
 
 import gradio as gr
 import tensorflow as tf