First import of classifier.

app.py

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+import json
+
+import gradio as gr
+import numpy
+import onnxruntime as ort
+from PIL import Image
+
+ort_sess = ort.InferenceSession('tiny_letter_classifier_v2_q8quant.onnx')
+
+# force reload now!
+
+def get_bounds(img):
+    # Assumes a BLACK BACKGROUND!
+    # White letters on a black background!
+    left = img.shape[1]
+    right = 0
+    top = img.shape[0]
+    bottom = 0
+    min_color = numpy.min(img)
+    max_color = numpy.max(img)
+    mean_color = 0.5*(min_color+max_color)
+    # Do this the dumb way.
+    for y in range(0, img.shape[0]):
+        for x in range(0, img.shape[1]):
+            if img[y,x] > mean_color:
+                left = min(left, x)
+                right = max(right, x)
+                top = min(top, y)
+                bottom = max(bottom, y)
+    return (top, bottom, left, right)
+
+def resize_maxpool(img, out_width: int, out_height: int):
+    out = numpy.zeros((out_height, out_width), dtype=img.dtype)
+    scale_factor_y = img.shape[0] // out_height
+    scale_factor_x = img.shape[1] // out_width
+    for y in range(0, out.shape[0]):
+        for x in range(0, out.shape[1]):
+            out[y,x] = numpy.max(img[y*scale_factor_y:(y+1)*scale_factor_y, x*scale_factor_x:(x+1)*scale_factor_x])
+    return out
+
+def process_input(input_msg):
+    img = input_msg["composite"]
+    # Image is inverted.  255 is white, 0 is what's drawn.
+    img_mean = 0.5 * (numpy.max(img) + numpy.min(img))
+    img = 1.0 * (img < img_mean)  # Invert the image and convert to a float.
+    #crop_area = get_bounds(img)
+    #img = img[crop_area[0]:crop_area[1]+2, crop_area[2]:crop_area[3]+2]
+    img = resize_maxpool(img, 32, 32)
+    img = numpy.expand_dims(img, axis=0)  # Unsqueeze
+    return img
+
+def softmax(arr):
+    arr = arr - numpy.max(arr)
+    return numpy.exp(arr) / numpy.sum(numpy.exp(arr), axis=-1)
+    
+def normalize(arr):
+    arr = numpy.atleast_2d(arr)
+    if arr.shape[0] == 1:
+        magnitude = arr @ arr.T
+    elif arr.shape[1] == 1:
+        magnitude = arr.T @ arr
+    return arr / magnitude
+
+def predict(input_img):
+    img = process_input(input_img)
+    class_preds = ort_sess.run(None, {'input': img.astype(numpy.float32)})[0]
+    class_preds = softmax(class_preds)[0]
+    class_idx_to_name = list("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz")
+    max_class_idx = numpy.argmax(class_preds)
+    
+    text_out = json.dumps({class_idx_to_name[i]: "#"*int(10*j) for i,j in enumerate(class_preds)}, indent=2)
+    return Image.fromarray(numpy.clip((img[0] * 254), 0, 255).astype(numpy.uint8)), f"Pred: {class_idx_to_name[max_class_idx]}: {class_preds[max_class_idx]}", text_out
+    #return sim[0][0], text_out
+
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        #gr.Sketchpad(image_mode='L', type='numpy'),
+        #gr.ImageEditor(
+        gr.Sketchpad(
+            width=320, height=320, 
+            canvas_size=(320, 320),
+            sources = ["upload", "clipboard"], # Webcam
+            layers=False,
+            image_mode='L', 
+            type='numpy', 
+        ),
+    ],
+    outputs=["image", "text", "text"],
+)
+
+demo.launch(share=True)
+

requirements.txt

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+onnxruntime==1.21.1
+numpy==1.26.4
+gradio==5.29.0

tiny_letter_classifier_v2_q8quant.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1c3c80f832dd0d13970592e1d008a9b5eb26b22381a349ea41df48225606b190
+size 2845664