Delete app.py

app.py

@@ -1,29 +0,0 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-import pickle
-
-# Load necessary objects from the pickle file
-with open('/content/drive/MyDrive/Oncoknow/Cancer /model_data_2.pkl', 'rb') as file:
-    pickle_data = pickle.load(file)
-
-# Load the model
-model =  pickle_data['model']
-class_names = ["CLL case lymphocytes", "Normal lymphocytes"]
-
-# Define function to preprocess and predict image
-def predict(image):
-    img_array = tf.keras.preprocessing.image.img_to_array(image)
-    img_array = tf.expand_dims(img_array, 0)  # Create a batch of images
-
-    predictions = model.predict(img_array)
-    predicted_class = class_names[np.argmax(predictions[0])]
-    confidence = round(100 * np.max(predictions[0]), 2)
-
-    return predicted_class + f" ({confidence}%)"
-
-# Define Gradio Interface
-image = gr.Image(type='pil', label="Upload Image")
-label = gr.Label(label="Predicted Class")
-
-gr.Interface(predict, inputs=image, outputs=label, title="Cancer Classification App").launch()