add running code

README copy.md

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+---
+title: Semi-Supervised Contrastive Learning with SimCLR
+emoji: 👨‍🏫
+colorFrom: red
+colorTo: green
+sdk: gradio
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+# Configuration
+
+`title`: _string_
+Supervised Contrastive Learning
+
+`emoji`: _string_
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_
+Can be either `gradio`, `streamlit`, or `static`
+
+`sdk_version` : _string_
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_
+Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
+Path is relative to the root of the repository.
+
+`models`: _List[string]_
+HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
+Will be parsed automatically from your code if not specified here.
+
+`datasets`: _List[string]_
+HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
+Will be parsed automatically from your code if not specified here.
+
+`pinned`: _boolean_
+Whether the Space stays on top of your list.

app.py

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+import gradio as gr
+import tensorflow as tf
+from huggingface_hub import from_pretrained_keras
+import numpy as np
+
+model = from_pretrained_keras("keras-io/semi-supervised-classification-simclr")
+
+labels = ["airplane", "bird", "car", "cat", "deer", "dog", "horse", "monkey", "ship", "truck"]
+
+
+def infer(test_image):
+    image = tf.constant(test_image)
+    image = tf.reshape(image, [-1, 96, 96, 3])
+    pred = model.predict(image)
+    pred_list = pred[0, :]
+    pred_softmax = np.exp(pred_list)/np.sum(np.exp(pred_list))
+    softmax_list = pred_softmax.tolist()
+    return {labels[i]: softmax_list[i] for i in range(10)}
+
+
+image = gr.inputs.Image(shape=(96, 96))
+label = gr.outputs.Label(num_top_classes=3)
+
+
+article = """<center>
+Authors: <a href='https://twitter.com/johko990' target='_blank'>Johannes Kolbe</a> after an example by András Béres at 
+<a href='https://keras.io/examples/vision/semisupervised_simclr/' target='_blank'>keras.io</a>"""
+
+
+description = """Image classification with a model trained via Semi-supervised Contrastive Learning """
+
+
+Iface = gr.Interface(
+    fn=infer,
+    inputs=image,
+    outputs=label,
+    examples=[["examples/monkey.jpeg"], ["examples/titanic.jpg"], ["examples/truck.jpg"]],
+    title="Semi-Supervised Contrastive Learning Classification",
+    article=article,
+    description=description,
+).launch()

requirements.txt

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+tensorflow >=2.6.0
+gradio
+huggingface_hub
+jinja2