Delete app (1).py

app (1).py

@@ -1,35 +0,0 @@
-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()