Update app.py

app.py

@@ -1,42 +1,53 @@
+___all___ = ['learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
+
+from fastai.vision.all import *
 import gradio as gr
-import tensorflow as tf
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing import image as keras_image
-import numpy as np
 
 # Load the trained model
-model = load_model('race_prediction_model.h5')
+learn = load_learner('second_model.pkl')
 
 # Define the categories based on your model's output
-categories = [
-    'Black', 'Indian', 'Southeast Asian', 
-    'East Asian', 'White', 'Middle Eastern', 'Latino_Hispanic'
-]  # Replace with your actual categories
+categories = learn.dls.vocab
 
 # Define the function to classify images
 def classify_image(img):
-    img = img.resize((224, 224))
-    img_array = keras_image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0)
-    img_array /= 255.0
-
-    predictions = model.predict(img_array)
-    return {category: float(pred) for category, pred in zip(categories, predictions[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")
 examples = ['example1.jpeg', 'example2.jpeg', 'example3.jpeg']  # Replace with your example images
 
-# Define the Gradio interface
-intf = 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
-)
-
-# Launch the interface
-intf.launch(share=True, inline=False)
+# Define a theme selector
+theme_selector = gr.Dropdown(choices=['default', 'dark', 'huggingface', 'compact'], label="Choose Theme", value='default')
+
+# Function to create and launch the Gradio interface
+def create_interface(theme):
+    return gr.Interface(
+        fn=classify_image, 
+        inputs=image, 
+        outputs=label, 
+        title="Image Classifier", 
+        description="Upload an image to classify it based on the trained model.",
+        examples=examples,
+        layout="horizontal",
+        theme=theme
+    )
+
+# Create the initial interface
+intf = create_interface('default')
+
+# 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
+
+# Launch the initial interface
+intf.launch(share=True, inline=False)
+
+# Add a separate interface for theme selection
+theme_intf = gr.Interface(fn=update_theme, inputs=theme_selector, outputs=None, live=True)
+theme_intf.launch(share=True, inline=False)