Go

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import os
+
+# Load labels
+with open('tensorflow/labels.txt', 'r') as f:
+    labels = f.read().splitlines()
+
+# Load TensorFlow model
+model = tf.keras.models.load_model('tensorflow/model.pb')
+
+def preprocess_image(image):
+    # Resize image to the size expected by the model
+    target_size = (224, 224)  # Replace with your model's expected input size
+    image = image.resize(target_size)
+    image = np.array(image)
+    image = image / 255.0  # Normalize if required
+    return image
+
+def predict(image):
+    image = preprocess_image(image)
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    predictions = model.predict(image)
+    predicted_index = np.argmax(predictions, axis=1)[0]
+    predicted_label = labels[predicted_index]
+    confidence = predictions[0][predicted_index] * 100
+    return {predicted_label: round(confidence, 2)}
+
+# Define Gradio interface
+title = "JunkWaxHero 🦸‍♂️ - Baseball Card Set Identifier"
+description = "Upload an image of a baseball card, and JunkWaxHero will identify the set it belongs to with high accuracy."
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs=gr.outputs.Label(num_top_classes=1),
+    title=title,
+    description=description,
+    examples=[
+        ["examples/card1.jpg"],
+        ["examples/card2.jpg"],
+        ["examples/card3.jpg"]
+    ],
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch()

onnx/LICENSE

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+    MIT License
+
+    Copyright (c) Microsoft Corporation.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all
+    copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+    SOFTWARE

onnx/README.md

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+# Custom Vision Export Object Detection Models
+This model is exported from [Custom Vision Service](https://customvision.ai)
+
+Please visit our [Sample scripts respository](https://github.com/Azure-Samples/customvision-export-samples).
+
+## Prerequisites
+(For TensorFlow Lite model) TensorFlow Lite 2.1 or newer
+
+## Input specification
+This model expects 320x320, 3-channel RGB images. Pixel values need to be in the range of [0-255].
+
+## Output specification
+There are three outputs from this model.
+
+* detected_boxes
+The detected bounding boxes. Each bounding box is represented as [x1, y1, x2, y2] where (x1, y1) and (x2, y2) are the coordinates of box corners.
+* detected_scores
+Probability for each detected boxes.
+* detected_classes
+The class index for the detected boxes.
+
+# Reference
+* [Custom Vision Service documentation](https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/)
+* [Sample scripts](https://github.com/Azure-Samples/customvision-export-samples)

onnx/cvexport.manifest

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+{
+  "DomainType": "ObjectDetection",
+  "Platform": "ONNX",
+  "Flavor": null,
+  "ExporterVersion": "2.0",
+  "ExportedDate": "2025-01-10T18:55:56.72257Z",
+  "IterationId": "159e9c3e-6435-4b9b-826d-ca39f5889fbc",
+  "ModelFileName": "model.onnx",
+  "LabelFileName": "labels.txt",
+  "MetadataPropsFileName": "metadata_properties.json",
+  "ModelFileSHA1": "949c71d256808ed71dafaeb424f54bd94051857e",
+  "SchemaVersion": "1.0"
+}

onnx/labels.txt

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+1984 Topps
+1987 Topps
+1989 Bowman
+1989 Fleer
+1989 Upper Deck
+1990 Donruss
+1990 Topps
+1991 Upper Deck
+1993 Topps

onnx/metadata_properties.json

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+{
+    "CustomVision.Metadata.AdditionalModelInfo": "",
+    "CustomVision.Metadata.Version": "1.2",
+    "CustomVision.Postprocess.Method": "SSD",
+    "CustomVision.Postprocess.Yolo.Biases": "[]",
+    "CustomVision.Postprocess.Yolo.NmsThreshold": "0.0",
+    "CustomVision.Preprocess.CropHeight": "0",
+    "CustomVision.Preprocess.CropMethod": "NoCrop",
+    "CustomVision.Preprocess.CropWidth": "0",
+    "CustomVision.Preprocess.MaxDimension": "0",
+    "CustomVision.Preprocess.MaxScale": "0.0",
+    "CustomVision.Preprocess.MinDimension": "0",
+    "CustomVision.Preprocess.MinScale": "0.0",
+    "CustomVision.Preprocess.NormalizeMean": "[0.0, 0.0, 0.0]",
+    "CustomVision.Preprocess.NormalizeStd": "[1.0, 1.0, 1.0]",
+    "CustomVision.Preprocess.ResizeMethod": "Stretch",
+    "CustomVision.Preprocess.TargetHeight": "320",
+    "CustomVision.Preprocess.TargetWidth": "320",
+    "Image.BitmapPixelFormat": "Rgb8",
+    "Image.ColorSpaceGamma": "SRGB",
+    "Image.NominalPixelRange": "Normalized_0_1"
+}

onnx/model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eba88166ae625c537736423715adc31baf4e7cb0dabbdd76f6ee6cbbcacb969f
+size 11670194

requirements.txt

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+gradio
+tensorflow==2.11.0  # Replace with your TensorFlow version
+Pillow
+numpy

tensorflow/LICENSE

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+    MIT License
+
+    Copyright (c) Microsoft Corporation.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all
+    copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+    SOFTWARE

tensorflow/README.md

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+# Custom Vision Export Object Detection Models
+This model is exported from [Custom Vision Service](https://customvision.ai)
+
+Please visit our [Sample scripts respository](https://github.com/Azure-Samples/customvision-export-samples).
+
+## Prerequisites
+(For TensorFlow Lite model) TensorFlow Lite 2.1 or newer
+
+## Input specification
+This model expects 320x320, 3-channel RGB images. Pixel values need to be in the range of [0-255].
+
+## Output specification
+There are three outputs from this model.
+
+* detected_boxes
+The detected bounding boxes. Each bounding box is represented as [x1, y1, x2, y2] where (x1, y1) and (x2, y2) are the coordinates of box corners.
+* detected_scores
+Probability for each detected boxes.
+* detected_classes
+The class index for the detected boxes.
+
+# Reference
+* [Custom Vision Service documentation](https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/)
+* [Sample scripts](https://github.com/Azure-Samples/customvision-export-samples)

tensorflow/cvexport.manifest

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+{
+  "DomainType": "ObjectDetection",
+  "Platform": "TensorFlow",
+  "Flavor": null,
+  "ExporterVersion": "2.1",
+  "ExportedDate": "2025-01-10T18:55:38.4488602Z",
+  "IterationId": "159e9c3e-6435-4b9b-826d-ca39f5889fbc",
+  "ModelFileName": "model.pb",
+  "LabelFileName": "labels.txt",
+  "MetadataPropsFileName": "metadata_properties.json",
+  "ModelFileSHA1": "7b0dd1f64b803acf61535d256e662ccafa5ae0e1",
+  "SchemaVersion": "1.0"
+}

tensorflow/labels.txt

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+1984 Topps
+1987 Topps
+1989 Bowman
+1989 Fleer
+1989 Upper Deck
+1990 Donruss
+1990 Topps
+1991 Upper Deck
+1993 Topps

tensorflow/metadata_properties.json

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+{
+    "CustomVision.Metadata.AdditionalModelInfo": "",
+    "CustomVision.Metadata.Version": "1.2",
+    "CustomVision.Postprocess.Method": "SSD",
+    "CustomVision.Postprocess.Yolo.Biases": "[]",
+    "CustomVision.Postprocess.Yolo.NmsThreshold": "0.0",
+    "CustomVision.Preprocess.CropHeight": "0",
+    "CustomVision.Preprocess.CropMethod": "NoCrop",
+    "CustomVision.Preprocess.CropWidth": "0",
+    "CustomVision.Preprocess.MaxDimension": "0",
+    "CustomVision.Preprocess.MaxScale": "0.0",
+    "CustomVision.Preprocess.MinDimension": "0",
+    "CustomVision.Preprocess.MinScale": "0.0",
+    "CustomVision.Preprocess.NormalizeMean": "[0.0, 0.0, 0.0]",
+    "CustomVision.Preprocess.NormalizeStd": "[1.0, 1.0, 1.0]",
+    "CustomVision.Preprocess.ResizeMethod": "Stretch",
+    "CustomVision.Preprocess.TargetHeight": "320",
+    "CustomVision.Preprocess.TargetWidth": "320",
+    "Image.BitmapPixelFormat": "Rgb8",
+    "Image.ColorSpaceGamma": "SRGB",
+    "Image.NominalPixelRange": "Normalized_0_1"
+}

tensorflow/model.pb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:33d1cd529b08c684ab287dd874d64c94275607cd0c0316c5cb32349e81b49d86
+size 11687739