Upload 7 files

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image as keras_image
+from tensorflow.image import resize
+
+# Load the trained model
+model = load_model("trained_model_10.h5")
+
+# CIFAR-10 labels
+label_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
+
+# Define the function to recognize images
+def recognize_image(image):
+    # Preprocess the image to fit the model input requirements
+    img = keras_image.img_to_array(image)
+    img = resize(img, (32, 32))
+    img = np.expand_dims(img, axis=0)
+    img = img / 255.0  # Normalizing if the model expects normalized input
+    
+    # Make predictions
+    pred = model.predict(img)
+    final_pred = np.argmax(pred, axis=1)
+    
+    # Create a dictionary mapping labels to their respective probabilities
+    result = {label_names[i]: float(pred[0][i]) for i in range(len(label_names))}
+    
+    return result
+
+# Define the input and output interfaces
+image_input = gr.Image()
+label_output = gr.Label(num_top_classes=5)
+
+# Example images
+examples = [
+    'image_1.jpeg',
+    'image_2.jpg',
+    'image_4.jpeg',
+    'image_5.jpg',
+    'image_7.jpg',
+    'image_8.jpeg'
+]
+
+# Create the Gradio interface
+iface = gr.Interface(fn=recognize_image, inputs=image_input, outputs=label_output, examples=examples)
+
+# Launch the interface
+iface.launch(inline=False)