app-working.py

import gradio as gr
import tensorflow as tf
from PIL import Image
import numpy as np
import os
import cv2

model_path = "IM_417_128.keras"

print("Loading Model...")
model = tf.keras.models.load_model(model_path)
model.summary()

labelled_images = [f"labelled/{i}.png" for i in range(1, 418)]

# Define regression function
def predict_regression(image):
    # Preprocess image
    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
    image = image.resize((128, 128)).convert('L') #resize the image to 128x128 and converts it to gray scale

    image = np.array(image)
    # Predict
    predictions = model.predict(image[None, ...])  # Assuming single regression value
    predicted_output = np.argmax(predictions, axis=1)
    predicted_label = f"labelled/{str(predicted_output[0])}.png"
    print(f"prediction IM-417 character :: {predicted_label} :: source {predicted_output}")
    output_data = cv2.imread(predicted_label, cv2.COLOR_BGR2GRAY)
    return cv2.resize(output_data, (128, 128))

# Create Gradio interface
input_image = gr.Image()
output_image = gr.Image(height=150, width=150)
interface = gr.Interface(fn=predict_regression, 
                         inputs=input_image, 
                         outputs=output_image,
                         examples=labelled_images,   
                         description="A simple mlp classification model for image classification using the sign list from 'The Indus Scripts: Texts, Concordance and Tables'"
)
interface.launch()