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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
from huggingface_hub import hf_hub_download
import os
# Configuration
MODEL_REPO = "your_hf_username/cat-dog-classifier" # Replace with your HF username and repo
MODEL_FILE = "best_model.h5"
# Download model from Hugging Face Hub
def load_model_from_hf():
try:
if not os.path.exists(MODEL_FILE):
print("Downloading model from Hugging Face Hub...")
model_path = hf_hub_download(
repo_id=MODEL_REPO,
filename=MODEL_FILE,
cache_dir="."
)
os.system(f"cp {model_path} {MODEL_FILE}")
return tf.keras.models.load_model(MODEL_FILE)
except Exception as e:
raise gr.Error(f"Model loading failed: {str(e)}")
model = load_model_from_hf()
def classify_image(image):
try:
image = Image.fromarray(image) if isinstance(image, np.ndarray) else image
image = image.resize((150, 150))
image_array = np.array(image) / 255.0
image_array = np.expand_dims(image_array, axis=0)
prediction = model.predict(image_array)
confidence = float(prediction[0][0])
return {
"Dog": confidence,
"Cat": 1 - confidence
}
except Exception as e:
raise gr.Error(f"Classification error: {str(e)}")
# Custom CSS for better UI
css = """
.gradio-container {
background: linear-gradient(to right, #f5f7fa, #c3cfe2);
}
footer {
visibility: hidden
}
"""
# Build the interface
with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🐾 Cat vs Dog Classifier 🦮")
gr.Markdown("Upload an image to classify whether it's a cat or dog")
with gr.Row():
with gr.Column():
image_input = gr.Image(label="Upload Image", type="pil")
submit_btn = gr.Button("Classify", variant="primary")
with gr.Column():
label_output = gr.Label(label="Predictions", num_top_classes=2)
confidence_bar = gr.BarPlot(
x=["Cat", "Dog"],
y=[0.5, 0.5],
y_lim=[0,1],
title="Confidence Scores",
width=400,
height=300
)
# Example images
gr.Examples(
examples=[
["https://upload.wikimedia.org/wikipedia/commons/1/15/Cat_August_2010-4.jpg"],
["https://upload.wikimedia.org/wikipedia/commons/d/d9/Collage_of_Nine_Dogs.jpg"]
],
inputs=image_input
)
# Button action
submit_btn.click(
fn=classify_image,
inputs=image_input,
outputs=[label_output, confidence_bar],
api_name="classify"
)
if __name__ == "__main__":
demo.launch() |