doenig

app.py

@@ -2,8 +2,10 @@ import gradio as gr
 import numpy as np
 import pickle
 from PIL import Image
+import os
+import random
 
-# Load the model
+# 1. Load the model
 with open('model.pkl', 'rb') as f:
     model_params = pickle.load(f)
 
@@ -12,6 +14,7 @@ b1 = model_params['b1']
 W2 = model_params['W2']
 b2 = model_params['b2']
 
+# 2. Define helper functions
 def ReLu(Z):
     return np.maximum(Z, 0)
 
@@ -40,6 +43,7 @@ def preprocess_image(image):
     
     return img_array.T  # Transpose to match the shape (784, 1)
 
+# 3. Define prediction function
 def predict_digit(image):
     X = preprocess_image(image)
     
@@ -51,13 +55,43 @@ def predict_digit(image):
     
     return int(prediction[0])
 
-# Gradio interface
-iface = gr.Interface(
-    fn=predict_digit,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Label(num_top_classes=1),
-    title="Handwritten Digit Recognition",
-    description="Upload an image of a handwritten digit (0-9) and the model will predict which digit it is."
-)
+# 4. Load sample images
+sample_images = []
+sample_dir = "sample_images"  # Make sure this directory exists in your Space
+for filename in os.listdir(sample_dir):
+    if filename.endswith((".png", ".jpg", ".jpeg")):
+        img_path = os.path.join(sample_dir, filename)
+        sample_images.append(img_path)
 
-iface.launch()
+# 5. Define function to select random image
+def select_random_image():
+    return random.choice(sample_images)
+
+# 6. Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Handwritten Digit Recognition")
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(type="pil", label="Input Image")
+            upload_button = gr.UploadButton("Upload Image", file_types=["image"])
+            sample_button = gr.Button("Use Random Sample Image")
+        
+        with gr.Column():
+            output_label = gr.Label(label="Prediction")
+            predict_button = gr.Button("Predict")
+    
+    upload_button.upload(fn=lambda file: file.name, inputs=upload_button, outputs=input_image)
+    sample_button.click(fn=select_random_image, inputs=None, outputs=input_image)
+    predict_button.click(fn=predict_digit, inputs=input_image, outputs=output_label)
+    
+    gr.Markdown("## Sample Images")
+    with gr.Row():
+        for img_path in sample_images[:5]:  # Display first 5 sample images
+            gr.Image(img_path, show_label=False, height=100)
+    with gr.Row():
+        for img_path in sample_images[5:10]:  # Display next 5 sample images
+            gr.Image(img_path, show_label=False, height=100)
+
+# 7. Launch the app
+demo.launch()