Update app.py

app.py

@@ -1,64 +1,24 @@
 ___all___ = ['learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
 
-from tensorflow.keras.models import load_model
-from PIL import Image
-import numpy as np
+from fastai.vision.all import *
 import gradio as gr
 
-# Define the ETHNICITIES dictionary
-ETHNICITIES = {0: "White", 1: "Black", 2: "Asian", 3: "Indian", 4: "Hispanic"}
-
 # Load the trained model
-Model_L = load_model('model_L.h5')
+learn = load_learner('second_model.pkl')
 
 # Define the categories based on your model's output
-categories = list(ETHNICITIES.values())
+categories = learn.dls.vocab
 
 # Define the function to classify images
 def classify_image(img):
-    img = img.resize((48, 48))  # Resize the image to match model input shape
-    img = img.convert('L')  # Convert image to grayscale
-    img = np.array(img) / 255.0  # Normalize the image
-    img = img.reshape(-1, 48, 48, 1)  # Reshape the image to match model input shape
-    pred = Model_L.predict(img)
-    probs = pred[0]
+    pred, idx, probs = learn.predict(img)
     return dict(zip(categories, map(float, probs)))
 
 # Define the Gradio components
-image = gr.Image(type='pil', label='Upload an Image')
-label = gr.Label(label="Predictions")
+image = gr.Image(type='pil', label='Input Image')
+label = gr.Label()
 examples = ['example1.jpeg', 'example2.jpeg', 'example3.jpeg']  # Replace with your example images
 
-# Function to create and launch the Gradio interface
-def create_interface(theme='default'):
-    return gr.Interface(
-        fn=classify_image, 
-        inputs=image, 
-        outputs=label, 
-        title="Face to Race", 
-        description="Upload an image to classify it based on the trained model.",
-        examples=examples,
-        theme=theme
-    )
-
-# Create the initial interface
-intf = create_interface()
-
-# Define a callback to update the theme
-def update_theme(theme):
-    global intf
-    intf.close()  # Close the current interface
-    intf = create_interface(theme)  # Create a new interface with the selected theme
-    intf.launch(share=True, inline=False)  # Launch the new interface
-
-# Add a theme selector interface
-theme_selector = gr.Interface(
-    fn=update_theme, 
-    inputs=gr.Dropdown(choices=['default', 'dark', 'huggingface', 'compact'], label="Choose Theme", value='default'), 
-    outputs=None, 
-    live=True
-)
-
-# Launch the initial interface and the theme selector interface
-intf.launch(share=True, inline=False)
-theme_selector.launch(share=True, inline=False)
+# Create and launch the Gradio interface
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, title="Image Classifier", examples=examples)
+intf.launch(inline=False)