Update app.py

app.py

@@ -8,6 +8,7 @@ import os
 
 model = tf.keras.models.load_model("fine_tuned_resnet50.h5")
 img_dim = (224, 224)
+lung_cancer_labels = ["Benign", "Malignant", "Normal"]
 
 # returning classifiers output
 def predict(img):
@@ -16,7 +17,9 @@ def predict(img):
     img_array = np.expand_dims(img_array, axis=0)
 
     prediction = model.predict(img_array)
-    return prediction
+    class_index = np.argmax(prediction)
+    confidence = np.max(prediction)
+    return f"{lung_cancer_labels[class_index]} (Confidence: {confidence:.2f})"
 
 with gr.Blocks() as demo:
     gr.Markdown("# Lung Cancer Classifier")