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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_76.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)}
# Gradio interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(
label="Input Image",
sources="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,share=True)
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