Upload app.py

app.py

@@ -1,8 +1,8 @@
+import numpy as np
+import gradio as gr
 import os
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 
-import numpy as np
-import gradio as gr
 from tensorflow.keras.models import load_model
 model = load_model('saved_model.keras')
 
@@ -12,7 +12,6 @@ def predict_number(image):
     image_grayScale= np.mean(image_rgb, axis=-1).astype(np.uint8)
 
     image_grayScale = image_grayScale.reshape(1, 280, 280, 1)
-    print(image_grayScale)
     preds = model.predict(image_grayScale)[0]
 
     return {str(i): float(preds[i]) for i in range(10)}
@@ -29,29 +28,33 @@ with gr.Blocks() as demo:
     3. **Click 'Clear canvas'** to clear the canvas.
     
     """)
-    sketch_pad = gr.Sketchpad(
-        label="Draw here (White on Black)",
-        layers=False,
-        canvas_size=(280, 280),
-        brush=gr.Brush(colors=["#FFFFFF"], default_color="#FFFFFF", default_size=10),
-        value={
-            "background": np.zeros((280, 280, 3), dtype=np.uint8),
-            "layers": [],
-            "composite": None
-        },
-        type="numpy"
-    )
+    with gr.Row():
+        with gr.Column():
+            sketch_pad = gr.Sketchpad(
+                label="Draw here (White on Black)",
+                layers=False,
+                canvas_size=(280, 280),
+                brush=gr.Brush(colors=["#FFFFFF"], default_color="#FFFFFF", default_size=10),
+                value={
+                    "background": np.zeros((280, 280, 3), dtype=np.uint8),
+                    "layers": [],
+                    "composite": None
+                },
+                type="numpy"
+            )
+        with gr.Column():
+            output = gr.Label(num_top_classes=3, label="Prediction")
 
     btn = gr.Button("Recognize", variant="primary")
     clear_btn = gr.Button("Clear canvas", variant="stop")
-    output = gr.Label(num_top_classes=3, label="Prediction")
+
 
     clear_btn.click(lambda: {"background": np.zeros((280, 280, 3), dtype=np.uint8), "layers": [], "composite": None},
                     outputs=sketch_pad)
     btn.click(predict_number, inputs=sketch_pad, outputs=output)
+
     gr.Markdown("""
     ## GitHub Repository
-    If you want to see the training process, data augmentation, and how the model was built,  
-        check out the **[main_analysis.ipynb](https://github.com/Arcus72/Image-Digit-Classification)** file in my GitHub repository.""")
+    If you want to see the training process, data augmentation, and how the model was built, check out the **[main_analysis.ipynb](https://github.com/Arcus72/Image-Digit-Classification)** file in my GitHub repository.""")
 
 demo.launch(theme=gr.themes.Soft())