app.py

import os
import numpy as np
import cv2
import base64
import gradio as gr
from tensorflow import keras

# Load the model
model_path = 'sketch2draw_model.h5'  # Update with your model path
model = keras.models.load_model(model_path)

# Load class names for predictions
class_names = ['grass', 'dirt', 'wood', 'water', 'sky', 'clouds']

def predict(image):
    # Decode the image from base64
    image_data = np.frombuffer(base64.b64decode(image.split(",")[1]), np.uint8)
    image = cv2.imdecode(image_data, cv2.IMREAD_COLOR)

    # Resize and normalize the image
    image = cv2.resize(image, (128, 128))  # Resize as per your model's input
    image = np.expand_dims(image, axis=0) / 255.0  # Normalize if needed

    # Make prediction
    predictions = model.predict(image)
    predicted_class = class_names[np.argmax(predictions)]
    
    return predicted_class

# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("<h1>Sketch to Draw Model</h1>")
    
    with gr.Row():
        with gr.Column():
            canvas = gr.Sketchpad(shape=(400, 400), label="Draw Here", tool="brush")
            brush_color = gr.ColorPicker(value="black", label="Brush Color")
            clear_btn = gr.Button("Clear")
        
        with gr.Column():
            predict_btn = gr.Button("Predict")
            output_label = gr.Textbox(label="Predicted Texture")
    
    # Define the actions for buttons
    def clear_canvas():
        return np.zeros((400, 400, 3), dtype=np.uint8)

    clear_btn.click(fn=clear_canvas, inputs=None, outputs=canvas)
    
    predict_btn.click(fn=predict, inputs=canvas, outputs=output_label)

# Launch the Gradio app
demo.launch()