Delete Untitled-1.py

Untitled-1.py

@@ -1,60 +0,0 @@
-# %%
-import gradio as gr
-import numpy as np
-from tensorflow.keras.models import load_model
-from tensorflow.keras.applications.resnet50 import preprocess_input
-from PIL import Image
-
-# Load the pre-trained Keras model
-model = load_model('pokemon-model.keras')
-
-# Define the class labels
-class_labels = ['Bulbasaur', 'Glumanda', 'Pikachu']  # Ensure this matches the training order
-
-# Define the image processing and prediction function
-def predict_image(img):
-    # Ensure the image is a PIL image
-    if not isinstance(img, Image.Image):
-        img = Image.fromarray(img)
-    
-    # Resize the image to the size expected by ResNet50
-    img = img.resize((224, 224))
-    
-    # Convert the image to a numpy array
-    img_array = np.array(img)
-    
-    # Convert the image array to a batch of size 1 (1, 224, 224, 3)
-    img_array = np.expand_dims(img_array, axis=0)
-    
-    # Preprocess the image array using ResNet50's preprocessing
-    img_array = preprocess_input(img_array)
-
-    # Make prediction
-    prediction = model.predict(img_array)
-    
-    # Get the label with the highest probability
-    predicted_index = int(np.argmax(prediction))
-    predicted_label = class_labels[predicted_index]
-    
-    return predicted_label
-
-# Create the Gradio interface with multiple examples
-iface = gr.Interface(
-    fn=predict_image, 
-    inputs=gr.Image(image_mode='RGB'),
-    outputs='label',
-    examples=[['00000015.jpg'], ['20.png'], ['glumanda.jpg'], ['j67j7.png'], ['pikachu.jpg']],
-    title="Pokémon Classification",
-    description="Upload an image of a Pokémon to classify it using the pre-trained model."
-)
-
-# Launch the interface inline in the Jupyter Notebook
-iface.launch(inline=True)
-
-
-# %%
-# Print model summary to verify input shape
-print(model.summary())
-
-
-