Upload app.py

app.py

@@ -0,0 +1,29 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import pickle
+
+# Load necessary objects from the pickle file
+with open("C:\Users\Administrator\Downloads\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()