app

app.py

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+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model as lm
+from PIL import Image
+import plotly.graph_objects as go
+
+# Load your trained CIFAR model
+model = lm('models/build_model_1_v2.keras')
+
+# Define the CIFAR-10 class names
+class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
+
+
+# Function to preprocess the image and predict the class
+def classify_image(image):
+    # Ensure the image is in the right format
+    image = Image.fromarray(image).convert('RGB')
+
+    # Resize the image to (32, 32) as CIFAR-10 uses 32x32 images
+    image = image.resize((32, 32))
+
+    # Convert the image to an array and preprocess it
+    image_array = np.array(image).astype(np.float32) / 255.0  # Normalize to [0, 1]
+
+    # Expand dimensions to match model input shape (1, 32, 32, 3)
+    image_array = np.expand_dims(image_array, axis=0)
+
+    # Get predictions
+    predictions = model.predict(image_array)[0]  # Get the prediction array for the first image
+
+    # Get the predicted class and its confidence
+    predicted_class_idx = np.argmax(predictions)
+    predicted_class = class_names[predicted_class_idx]
+    predicted_confidence = predictions[predicted_class_idx] * 100  # Convert to percentage
+
+    # Print predicted class and confidence
+    predicted_info = f"Predicted Class: {predicted_class} with {predicted_confidence:.2f}% confidence."
+
+    # Create a Plotly bar chart for class confidence levels
+    fig = go.Figure(go.Bar(
+        x=predictions * 100,  # Convert probabilities to percentages
+        y=class_names,
+        orientation='h',
+        marker=dict(color='skyblue'),
+        text=[f"{conf:.1f}%" for conf in predictions * 100],  # Show percentage labels
+        hoverinfo="text"
+    ))
+
+    # Update layout for better presentation
+    fig.update_layout(
+        title="Class Confidence Levels",
+        xaxis_title="Confidence (%)",
+        yaxis_title="Classes",
+        xaxis=dict(range=[0, 100]),  # Set x-axis to 0-100%
+        yaxis=dict(categoryorder='total ascending'),  # Sort bars by confidence
+        bargap=0.2
+    )
+
+    return predicted_info, fig
+
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="numpy"),
+    outputs=["text", gr.Plot()],
+    title="CIFAR Image Classification",
+    description="Upload an image, and the model will classify it as one of the CIFAR-10 classes."
+)
+
+# Launch the interface
+interface.launch()

build_model_1_v2.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:16d160cfb01eefa350299b6db302e13b2040efb574a41963caeb998d88f53a97
+size 2301984