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import numpy as np
import tensorflow as tf
import gradio as gr
from tensorflow.keras.preprocessing import image
from huggingface_hub import snapshot_download
import os
# Load the model from Hugging Face Hub
def load_model(repo_id):
download_dir = snapshot_download(repo_id)
model_path = os.path.join(download_dir, "full_model.weights.h5")
model = tf.keras.models.load_model(model_path)
return model
# Function to preprocess the uploaded image
def preprocess_image(img, target_size=(224, 224)):
img = img.resize(target_size) # Resize to match model input
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
img_array = tf.keras.applications.efficientnet.preprocess_input(img_array)
return img_array
# Perform inference
def predict(image_input):
class_names = ["Defective Tyre", "Good Tyre"]
# Preprocess image
img_array = preprocess_image(image_input)
# Get prediction
prediction = model.predict(img_array)[0][0] # Scalar sigmoid output
predicted_class_idx = int(prediction >= 0.5) # 0 if <0.5, 1 if >=0.5
predicted_class = class_names[predicted_class_idx] # Get class name
return f"Predicted Class: {predicted_class} (Confidence: {prediction:.5f})"
# Hugging Face Model Repository ID
REPO_ID = "skngew/9053220B" # my actual repo ID
# Load the model
model = load_model(REPO_ID)
# Student ID
student_id = "Student ID: 9053220B"
# Markdown description to show classification threshold
threshold_info = """
### EfficientNetB0 (Feature Extraction)
### Classification Threshold:
- A tyre is classified as **Good** if the confidence score is **≥ 0.5**.
- A tyre is classified as **Defective** if the confidence score is **< 0.5**.
"""
# Create the Gradio interface
interface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil", label="Upload an Image"),
outputs=gr.Textbox(label="Prediction"),
title="Binary Classification: Good vs. Defective Tyre",
description=student_id,
allow_flagging="never",
examples=[], #Can add examples here
)
# Add the threshold information markdown
with gr.Blocks() as app:
gr.Markdown(threshold_info) # Display threshold info
interface.render()
# Launch the Gradio app
app.launch(share=True)
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