added initial files

animalImageGAN_full.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:494ad8f4fc71738510d6accdb1fe1b47a50f4d18b5fccfdc58c107b927ee6e4d
+size 12669054

app.py

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+
+import onnx
+import onnxruntime as ort
+import numpy as np
+
+MODEL_PATH = r"./"
+model_name = "animalImageGAN_full.onnx"
+ONNX_MODEL_PATH = MODEL_PATH+model_name
+
+onnx_model = onnx.load(ONNX_MODEL_PATH)
+onnx.checker.check_model(onnx_model)
+
+rng = np.random.default_rng()
+desired_mean = 0
+desired_variance = 1
+
+generator_input_size = 50
+latent_space_samples = np.random.rand(generator_input_size,1,1).astype(np.float32)
+ort_sess = ort.InferenceSession(ONNX_MODEL_PATH)
+
+import gradio as gr
+
+
+def generateImage():
+    random_input = rng.random((generator_input_size, 1, 1),dtype=np.float32)
+    current_mean = np.mean(random_input)
+    current_variance = np.var(random_input)
+    scaled_values = (random_input - current_mean) / np.sqrt(current_variance)
+    random_input = scaled_values * np.sqrt(desired_variance) + desired_mean
+    
+    outputs = ort_sess.run(None, {'input': random_input})
+    output = outputs[0]
+    denorm_output = np.clip((output * 0.5) + 0.5,0,1)
+    #print("i: {}, min:{},max:{}".format(i,denorm_output.min(),denorm_output.max()))
+    return denorm_output.transpose(1,2,0)
+
+DESCRIPTION = "<div style='text-align:center'><h1 style='justify-content: center'>Animal Portrait Generator</h1>"
+DESCRIPTION += "<p>This is a model trained by using DCGAN</p>"
+DESCRIPTION += "<p>More details:</p>"
+DESCRIPTION += "<ul><li><a href='https://medium.com/@jiachiewloh/dcgan-animal-image-generator-85e466fb6254'>Article</a></li>"
+DESCRIPTION += "<li><a href='https://www.kaggle.com/code/jclohjc/animal-image-generator-dcgan'>Code</a></li></ul>"
+DESCRIPTION += "</div>"
+
+with gr.Blocks(css="#img_window {text-align:center; justify-content: center;}\
+               .image-container {margin: auto; height: 250px; width: 250px; !important}") as demo:
+    # with gr.Row():
+    #     gr.Markdown(DESCRIPTION)
+    #     with gr.Column():
+    #         img_window = gr.Image(interactive=False,height=250,width=250)
+    #         with gr.Row():
+    #             gr.Button("Generate").click(fn=generateImage,outputs=img_window)
+    #             gr.ClearButton().add(img_window)
+    
+    
+    gr.Markdown(DESCRIPTION)
+    img_window = gr.Image(interactive=False,elem_id="img_window")
+    with gr.Row():
+        gr.Button("Generate").click(fn=generateImage,outputs=img_window)
+        gr.ClearButton().add(img_window)
+demo.launch()

flagged/log.csv

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+output,flag,username,timestamp
+,,,2024-02-25 01:05:52.118984

requirements.txt

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+onnx
+onnxruntime
+numpy
+matplotlib