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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
import pandas as pd
import logging
# Disable GPU if not available (for Hugging Face Spaces)
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Configuration
MODEL_REPO = "Ahmedhassan54/Image-Classification-Model"
MODEL_FILE = "best_model.keras"
# Initialize model
model = None
def load_model():
global model
try:
logger.info("Downloading model...")
model_path = hf_hub_download(
repo_id=MODEL_REPO,
filename=MODEL_FILE,
cache_dir=".",
force_download=True
)
logger.info(f"Model path: {model_path}")
# Explicitly disable GPU
with tf.device('/CPU:0'):
model = tf.keras.models.load_model(model_path)
logger.info("Model loaded successfully!")
except Exception as e:
logger.error(f"Model loading failed: {str(e)}")
model = None
# Load model at startup
load_model()
def classify_image(image):
try:
if image is None:
return {"Cat": 0.5, "Dog": 0.5}, pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]})
# Convert to PIL Image if numpy array
if isinstance(image, np.ndarray):
image = Image.fromarray(image.astype('uint8'))
# Preprocess
image = image.resize((150, 150))
img_array = np.array(image) / 255.0
if len(img_array.shape) == 3:
img_array = np.expand_dims(img_array, axis=0)
# Predict
if model is not None:
with tf.device('/CPU:0'):
pred = model.predict(img_array, verbose=0)
confidence = float(pred[0][0])
else:
confidence = 0.75 # Demo value
results = {
"Cat": round(1 - confidence, 4),
"Dog": round(confidence, 4)
}
plot_data = pd.DataFrame({
'Class': ['Cat', 'Dog'],
'Confidence': [1 - confidence, confidence]
})
return results, plot_data
except Exception as e:
logger.error(f"Error: {str(e)}")
return {"Error": str(e)}, pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]})
# Interface
with gr.Blocks() as demo:
gr.Markdown("# 🐾 Cat vs Dog Classifier 🦮")
with gr.Row():
with gr.Column():
img_input = gr.Image(type="pil")
classify_btn = gr.Button("Classify", variant="primary")
with gr.Column():
label_out = gr.Label(num_top_classes=2)
plot_out = gr.BarPlot(
pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]}),
x="Class", y="Confidence", y_lim=[0,1]
)
classify_btn.click(
classify_image,
inputs=img_input,
outputs=[label_out, plot_out]
)
# Examples section
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=img_input,
outputs=[label_out, plot_out],
fn=classify_image,
cache_examples=True
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860) |