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import gradio as gr | |
import tensorflow as tf | |
import gdown | |
from PIL import Image | |
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=12700bE-pomYKoVQ214VrpBoJ7akXcTpL" | |
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 | |
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 | |
# UI Design | |
def classify_image(image): | |
predicted_class = predict_class(image) | |
output = f"<h2>Predicted Class:</h2><p>{predicted_class}</p>" | |
return output | |
inputs = gr.inputs.Image(label="Upload an image") | |
outputs = gr.outputs.HTML() | |
title = "<h1 style='text-align: center;'>Image Classifier</h1>" | |
description = "Upload an image and get the predicted class." | |
gr.Interface(fn=classify_image, | |
inputs=inputs, | |
outputs=outputs, | |
title=title, | |
examples=["00_plane.jpg", "01_car.jpg", "02_bird.jpg", "03_cat.jpg", "04_deer.jpg"], | |
css="body {background-image: url('file=wave.mp4')}", | |
description=description).launch() | |