Update app.py

app.py

@@ -7,7 +7,7 @@ from tensorflow.keras.utils import load_img, img_to_array
 model = joblib.load("flower1.pkl")
 
 # Define the class names (must match the training dataset order)
-class_names = ['Daisy', 'Rose', 'Sunflower', 'Tulip', 'Dandelion']
+class_names = ["Rose", "Tulip", "Daisy", "Sunflower", "Daffodil","Lily","Orchid","Lotus","Jasmine","Marigold","Hibiscus","Tube rose","Gladiolus","Frangipani","Iris","Dahlia"]
 
 # Function to preprocess the image and make predictions
 def predict_flower(image):
@@ -23,7 +23,7 @@ def predict_flower(image):
 
 # Create the Gradio interface
 title = "Flower Classification"
-description = "Upload an image of a flower, and the model will predict the type of flower (Daisy, Rose, Sunflower, Tulip, or Dandelion)."
+description = "Upload an image of a flower, and the model will predict the type of flower (Daisy, Rose, Sunflower, Tulip etc.)."
 
 gr_interface = gr.Interface(
     fn=predict_flower,  # Function to process predictions