app.py

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

# Load your custom regression model
model_path = "trainpokemon_model_transferlearning.weights.h5"
model_path = "pokemon_model_transferlearning.keras"

#model.load_weights(model_path)
model = tf.keras.models.load_model(model_path)

labels = ['Ditto','Venomoth','Venusaur']

# Define regression function
def predict_regression(image):
    # Preprocess image
    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
    image = image.resize((150, 150))#.convert('L') #resize the image to 28x28 and converts it to gray scale
    image = np.array(image)
    print(image.shape)
    # Predict
    prediction = model.predict(image[None, ...])  # Assuming single regression value
    confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
    return confidences

# Create Gradio interface
input_image = gr.Image()
output_text = gr.Textbox(label="Predicted Pokemon")
interface = gr.Interface(fn=predict_regression, 
                         inputs=input_image, 
                         outputs=gr.Label(),
                         examples=["pokemons/train/Ditto/00000008.jpg", "images/Venomoth.jpeg", "images/Venusaur.jpeg"],   
                         description="A simple mlp classification model for image classification using the mnist dataset.")
interface.launch()