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import gradio as gr
import tensorflow as tf
import gdown
from PIL import Image
import pillow_avif
input_shape = (32, 32, 3)
resized_shape = (224, 224, 3)
num_classes = 10
labels = {
0: "plane",
1: "car",
2: "bird",
3: "cat",
4: "deer",
5: "dog",
6: "frog",
7: "horse",
8: "ship",
9: "truck",
}
# Download the model file
def download_model():
url = "https://drive.google.com/uc?id=1fclkgIgUo26g014beN8UhCJ2TfvGG2sG"
output = "modelV2Lmixed.keras"
gdown.download(url, output, quiet=False)
return output
model_file = download_model()
# Load the model
model = tf.keras.models.load_model(model_file)
# Perform image classification for single class output
# def predict_class(image):
# img = tf.cast(image, tf.float32)
# img = tf.image.resize(img, [input_shape[0], input_shape[1]])
# img = tf.expand_dims(img, axis=0)
# prediction = model.predict(img)
# class_index = tf.argmax(prediction[0]).numpy()
# predicted_class = labels[class_index]
# return predicted_class
# Perform image classification for multy class output
def predict_class(image):
img = tf.cast(image, tf.float32)
img = tf.image.resize(img, [input_shape[0], input_shape[1]])
img = tf.expand_dims(img, axis=0)
prediction = model.predict(img)
return prediction[0]
# UI Design for single class output
# def classify_image(image):
# predicted_class = predict_class(image)
# output = f"<h2>Predicted Class: <span style='text-transform:uppercase';>{predicted_class}</span></h2>"
# return output
# UI Design for multy class output
def classify_image(image):
results = predict_class(image)
print("results is ...", results)
output = {labels.get(i): float(results[i]) for i in range(len(results))}
print("output is ...", output)
result = output if max(output.values()) >=0.98 else {"NO_CIFAR10_CLASS": 1}
return result
inputs = gr.components.Image(type="pil", label="Upload an image")
# outputs = gr.outputs.HTML() #uncomment for single class output
outputs = gr.components.Label(num_top_classes=4)
title = "<h1 style='text-align: center;'>Image Classifier</h1>"
description = "Upload an image and get the predicted class."
# css_code='body{background-image:url("file=wave.mp4");}'
gr.Interface(fn=classify_image,
inputs=inputs,
outputs=outputs,
title=title,
examples=[["00_plane.jpg"], ["01_car.jpg"], ["02_house.jpg"], ["03_cat.jpg"], ["04_deer.jpg"]],
# css=css_code,
description=description).launch()
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