First commit, test

app.py

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+''' ----------------------------------------
+* Creation Time : Sun Aug 28 21:38:58 2022
+* Last Modified : Sun Aug 28 21:41:36 2022
+* Author : Charles N. Christensen
+* Github : github.com/charlesnchr
+----------------------------------------'''
+
+from turtle import title
+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import io
+import base64
+
+
+model = tf.keras.models.load_model("./tf_model.h5")
+
+
+def predict(image):
+    img = np.array(image)
+    original_shape = img.shape[:2]
+
+    im = tf.image.resize(img, (128, 128))
+    im = tf.cast(im, tf.float32) / 255.0
+    pred_mask = model.predict(im[tf.newaxis, ...])
+
+
+    # take the best performing class for each pixel
+    # the output of argmax looks like this [[1, 2, 0], ...]
+    pred_mask_arg = tf.argmax(pred_mask, axis=-1)
+
+
+    # convert the prediction mask into binary masks for each class
+    binary_masks = {}
+
+    # when we take tf.argmax() over pred_mask, it becomes a tensor object
+    # the shape becomes TensorShape object, looking like this TensorShape([128])
+    # we need to take get shape, convert to list and take the best one
+
+    rows = pred_mask_arg[0][1].get_shape().as_list()[0]
+    cols = pred_mask_arg[0][2].get_shape().as_list()[0]
+
+    for cls in range(pred_mask.shape[-1]):
+
+        binary_masks[f"mask_{cls}"] = np.zeros(shape = (pred_mask.shape[1], pred_mask.shape[2])) #create masks for each class
+
+        for row in range(rows):
+
+            for col in range(cols):
+
+                if pred_mask_arg[0][row][col] == cls:
+
+                    binary_masks[f"mask_{cls}"][row][col] = 1
+                else:
+                    binary_masks[f"mask_{cls}"][row][col] = 0
+
+        mask = binary_masks[f"mask_{cls}"]
+        mask *= 255
+
+    mask = np.array(Image.fromarray(mask).convert("L"))
+    mask = tf.image.resize(mask[..., tf.newaxis], original_shape)
+    mask = tf.cast(mask, tf.uint8)
+    mask = mask.numpy().squeeze()
+
+    return mask
+
+
+title = '<h1 style="text-align: center;">Segment Pets</h1>'
+
+description = """
+## About
+This space demonstrates the use of a semantic segmentation model to segment pets and classify them
+according to the pixels.
+## 🚀 To run
+Upload a pet image and hit submit or select one from the given examples
+"""
+
+inputs = gr.inputs.Image(label="Upload a pet image", type = 'pil', optional=False)
+outputs = [
+    gr.outputs.Image(label="Segmentation")
+    # , gr.outputs.Textbox(type="auto",label="Pet Prediction")
+]
+
+examples = [
+    "./examples/dogcat.jpeg",
+]
+
+
+
+interface = gr.Interface(fn=predict,
+    inputs=inputs,
+    outputs=outputs,
+    title = title,
+    description=description,
+    examples=examples
+    )
+interface.launch()

requirements.txt

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+huggingface_hub
+tensorflow
+pillow

tf_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0258ea75c11d977fae78f747902e48541c5e6996d3d5c700175454ffeb42aa0f
+size 63661584