Update app.py

app.py

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+import gradio as gr
+import numpy as np
+import joblib
+from tensorflow.keras.utils import load_img, img_to_array
+
+# Load the pre-trained model
+model = joblib.load("flower.pkl")
+
+# Define the class names (must match the training dataset order)
+class_names = ['Daisy', 'Rose', 'Sunflower', 'Tulip', 'Dandelion']
+
+# Function to preprocess the image and make predictions
+def predict_flower(image):
+    # Resize and preprocess the image
+    img = image.resize((150, 150))  # Resize image to match the model input
+    img_array = np.array(img) / 255.0  # Normalize pixel values to [0, 1]
+    img_array = img_array.reshape((1, 150, 150, 3))  # Add batch dimension
+    
+    # Make prediction
+    predictions = model.predict(img_array)
+    predicted_class = class_names[np.argmax(predictions)]
+    return f"The predicted flower is: {predicted_class}"
+
+# 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)."
+
+gr_interface = gr.Interface(
+    fn=predict_flower,  # Function to process predictions
+    inputs=gr.Image(type="pil"),  # Input: Image (PIL format)
+    outputs=gr.Textbox(),  # Output: Textbox for predicted class
+    title=title,
+    description=description
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    gr_interface.launch()