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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
# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Configuration
MODEL_REPO = "Ahmedhassan54/Image-Classification"
MODEL_FILE = "best_model.h5"
# Initialize model to None
model = None
def load_model():
global model
try:
logger.info("โณ Downloading model...")
model_path = hf_hub_download(
repo_id=MODEL_REPO,
filename=MODEL_FILE,
cache_dir=".",
force_download=True
)
logger.info(f"๐Ÿ“ Model path: {model_path}")
# Verify file exists
if not os.path.exists(model_path):
raise FileNotFoundError(f"Model file not found at {model_path}")
logger.info("๐Ÿ”„ Loading TensorFlow model...")
model = tf.keras.models.load_model(model_path)
logger.info("โœ… Model loaded successfully!")
except Exception as e:
logger.error(f"โŒ Model loading failed: {str(e)}")
model = None
raise gr.Error(f"Model loading failed. Check logs for details.")
# Load model when app starts
load_model()
def classify_image(image):
try:
if image is None:
raise gr.Error("Please upload an image first")
logger.info("๐Ÿ–ผ๏ธ Processing image...")
# Convert to PIL Image if numpy array
if isinstance(image, np.ndarray):
image = Image.fromarray(image)
# Resize and normalize
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)
logger.info(f"๐Ÿ“Š Input shape: {img_array.shape}")
if model is None:
raise gr.Error("Model not loaded - using demo mode")
return {"Cat": 0.5, "Dog": 0.5}, pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]})
pred = model.predict(img_array, verbose=0)
confidence = float(pred[0][0])
logger.info(f"๐Ÿ”ฎ Prediction confidence: {confidence}")
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"๐Ÿ’ฅ Classification error: {str(e)}")
raise gr.Error(f"Error processing image: {str(e)}")
css = """
.gradio-container { max-width: 900px; margin: auto; }
footer { visibility: hidden; }
.progress-bar { color: #ff4d4d !important; }
"""
with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# ๐Ÿพ Cat vs Dog Classifier ๐Ÿฆฎ
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")
with gr.Row():
submit_btn = gr.Button("Classify ๐Ÿš€", variant="primary")
clear_btn = gr.Button("Clear ๐Ÿ—‘๏ธ")
with gr.Column():
label_output = gr.Label(label="Predictions")
confidence_bar = gr.BarPlot(
pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]}),
x="Class", y="Confidence", y_lim=[0,1],
title="Confidence Scores", width=400, height=300
)
# Examples
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,
outputs=[label_output, confidence_bar],
fn=classify_image,
cache_examples=True
)
# Button actions
submit_btn.click(
fn=classify_image,
inputs=image_input,
outputs=[label_output, confidence_bar],
api_name="predict"
)
clear_btn.click(
fn=lambda: [None, pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]})],
inputs=None,
outputs=[image_input, confidence_bar]
)
if __name__ == "__main__":
demo.launch(debug=True)