Delete chess-figures-classification.py

chess-figures-classification.py

@@ -1,45 +0,0 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-
-
-model_path = "chess-predict-model_transferlearning.keras"
-model = tf.keras.models.load_model(model_path)
-
-# Define the core prediction function
-def predict_figure(image):
-    # Preprocess image
-    print(type(image))
-    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
-    image = image.resize((150, 150)) #resize the image to 150x150
-    image = np.array(image)
-    image = np.expand_dims(image, axis=0) # same as image[None, ...]
-    
-    # Predict
-    prediction = model.predict(image)
-    
-    # Apply softmax to get probabilities for each class
-    prediction = tf.nn.softmax(prediction)
-    
-    # Create a dictionary with the probabilities for each Pokemon
-    bishop = np.round(float(prediction[0][0]), 2)
-    king = np.round(float(prediction[0][1]), 2)
-    knight = np.round(float(prediction[0][2]), 2)
-    pawn = np.round(float(prediction[0][3]), 2)
-    queen = np.round(float(prediction[0][4]), 2)
-    rook = np.round(float(prediction[0][5]), 2)
-
-    
-    return {'Bishop': bishop, 'King': king, 'Knight': knight, 'Pawn': pawn, 'Queen': queen, 'Rook': rook}
-
-input_image = gr.Image()
-iface = gr.Interface(
-    fn=predict_figure,
-    inputs=input_image, 
-    outputs=gr.Label(),
-    description="A simple mlp classification model for image classification using the mnist dataset.")
-iface.launch(share=True)
-
-
-