app.py

import gradio as gr
import tensorflow as tf
import numpy as np
from datasets import load_dataset
from PIL import Image

# Define the preprocessing function
def preprocess_inference_image(image):
    # Ensure image is a tensor
    image = tf.convert_to_tensor(image, dtype=tf.float32)
    # Convert image to RGB if it has 4 channels (RGBA)
    if tf.shape(image)[-1] == 4:
        image = image[..., :3]  # Select first 3 channels (RGB)
    # Normalize to [0,1]
    image = tf.image.convert_image_dtype(image, tf.float32)
    # Resize to MobileNetV3 input size
    image = tf.image.resize(image, [224, 224])
    # Add batch dimension
    image = tf.expand_dims(image, axis=0)
    return image

# Load the trained model
model = tf.keras.models.load_model('maize_disease_model.keras')

# Load the dataset to get label names
ds = load_dataset("aquib1011/maize-leaf-disease", cache_dir=None)
label_names = ds['train'].features['label'].names

def predict_maize_disease(image):
    # Convert PIL Image to numpy array
    image = np.array(image)
    # Apply preprocessing to the input image
    processed_image = preprocess_inference_image(image)

    # Make a prediction
    predictions = model.predict(processed_image)

    # Return the results as a dictionary for Gradio's Label component
    return {label_names[i]: float(predictions[0][i]) for i in range(len(label_names))}

# Create the Gradio interface
iface = gr.Interface(
    fn=predict_maize_disease,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(),
    title="Maize Leaf Disease Classifier",
    description="Upload an image of a maize leaf to get a prediction of the disease."
)

if __name__ == "__main__":
    iface.launch()