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import gradio as gr
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import load_img, img_to_array
# Load the trained model
MODEL_PATH = "best_model.weights.h5"
model = load_model(MODEL_PATH)
# Define the class names
class_names = [
"Bear", "Bird", "Cat", "Cow", "Deer",
"Dog", "Dolphin", "Elephant", "Giraffe",
"Horse", "Kangaroo", "Lion", "Panda",
"Tiger", "Zebra"
]
def classify_image(image):
img = image.resize((256, 256))
img_array = img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
predictions = model.predict(img_array)
predicted_class = class_names[np.argmax(predictions)]
return f"Predicted Class: {predicted_class}"
def instruction():
return (
"**Important Note:**\n\n"
"This model is specifically trained to classify images into the following **15 animal categories**:\n\n"
"- Bear\n"
"- Bird\n"
"- Cat\n"
"- Cow\n"
"- Deer\n"
"- Dog\n"
"- Dolphin\n"
"- Elephant\n"
"- Giraffe\n"
"- Horse\n"
"- Kangaroo\n"
"- Lion\n"
"- Panda\n"
"- Tiger\n"
"- Zebra\n\n"
"**Usage Limitation:**\n\n"
"- The model will only recognize images containing these animals.\n"
"- Uploading an image of an animal not listed above or a non-animal image may result in inaccurate or undefined predictions.\n\n"
"Ensure the uploaded image is clear, contains a single animal, and resembles the categories listed for the best results."
)
# Gradio Interface
with gr.Blocks() as app:
gr.Markdown("# Animal Classifier")
gr.Markdown(instruction())
with gr.Row():
with gr.Column():
image_input = gr.Image(label="Upload an Image", type="pil")
predict_button = gr.Button("Classify Image")
with gr.Column():
result_output = gr.Textbox(label="Prediction Result", lines=3)
predict_button.click(classify_image, inputs=image_input, outputs=result_output)
app.launch()
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