app.py

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

labels = ['Banana', 'Coconut', 'Eggplant', 'Mango', 'Melon', 'Orange', 'Pineapple', 'Watermelon']

def predict_pokemon_type(uploaded_file):
    if uploaded_file is None:
        return "No file uploaded.", None, "No prediction"

    model = tf.keras.models.load_model('fruits-xception-model.keras')

    # Load the image from the file path
    with Image.open(uploaded_file) as img:
        img = img.resize((150, 150))
        img_array = np.array(img)

        prediction = model.predict(np.expand_dims(img_array, axis=0))
        
        # Identify the most confident prediction
        confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}

        return img, confidences

# Define the Gradio interface
iface = gr.Interface(
    fn=predict_pokemon_type,  # Function to process the input
    inputs=gr.File(label="Upload File"),  # File upload widget
    outputs=["image", "text"],  # Output types for image and text
    title="Fruit Classifier",  # Title of the interface
    description="Upload a picture of a Fruit (preferably a Banana, Coconut, Eggplant, Mango, Melon, Orange, Pineapple or Watermelon) to see what fruit it is and the models confidence level. Accuracy: 0.8997 - Loss: 0.4229 on Test Data"  # Description of the interface
)

# Launch the interface
iface.launch()