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requirements.txt

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+gradio
+huggingface_hub
+tensorflow
+numpy
+pillow

threadcheckerv1_gui.py

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+# -*- coding: utf-8 -*-
+"""threadcheckerv1_gui.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/145qYaJaBGKmbGoSNYSsFlMASN1PUKEgF
+"""
+
+import numpy as np
+from PIL import Image
+import gradio as gr
+import huggingface_hub
+from tensorflow.keras.models import load_model
+
+# Load model from Hugging Face Hub
+repo_id = "ddecosmo/thread_checker_v1"
+model_filename = "thread_checker_model.keras"
+model_path = huggingface_hub.hf_hub_download(repo_id=repo_id, filename=model_filename)
+model = load_model(model_path)
+
+# Example images (replace with actual files in your repo if desired)
+example_images = [
+    "/content/sample_data/threaded_example.jpg",
+    "/content/sample_data/unthreaded_example.jpg"
+]
+
+def predict_image(image):
+    """
+    Predicts the class of an image using the loaded Keras model and returns
+    confidence scores for all classes and the final determination.
+    """
+    img_width, img_height = model.input_shape[1:3]
+    image = image.resize((img_width, img_height))
+    image = np.array(image).astype("float32") / 255.0
+    image = np.expand_dims(image, axis=0)
+
+    predictions = model.predict(image)
+    confidence_scores = predictions[0]
+
+    predicted_class_index = np.argmax(confidence_scores)
+    class_labels = ["0.125", "0.25", "0.375"]
+    final_determination = class_labels[predicted_class_index]
+
+    return (
+        float(confidence_scores[0]),
+        float(confidence_scores[1]),
+        float(confidence_scores[2]),
+        final_determination,
+    )
+
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Number(label="Confidence (0.125)"),
+        gr.Number(label="Confidence (0.25)"),
+        gr.Number(label="Confidence (0.375)"),
+        gr.Textbox(label="Final Determination"),
+    ],
+    examples=example_images,
+)
+
+if __name__ == "__main__":
+    iface.launch()