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Runtime error
| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| from transformers import DeiTFeatureExtractor, DeiTForImageClassification | |
| from hugsvision.inference.VisionClassifierInference import VisionClassifierInference | |
| from hugsvision.inference.TorchVisionClassifierInference import TorchVisionClassifierInference | |
| models_name = [ | |
| "VGG16", | |
| "DeiT", | |
| "ShuffleNetV2", | |
| "MobileNetV2", | |
| "DenseNet121", | |
| ] | |
| radio = gr.inputs.Radio(models_name, default="DenseNet121", type="value") | |
| def predict_image(image, model_name): | |
| image = Image.fromarray(np.uint8(image)).convert('RGB') | |
| model_path = "./models/" + model_name | |
| if model_name == "DeiT": | |
| model = VisionClassifierInference( | |
| feature_extractor = DeiTFeatureExtractor.from_pretrained(model_path), | |
| model = DeiTForImageClassification.from_pretrained(model_path), | |
| ) | |
| else: | |
| model = TorchVisionClassifierInference( | |
| model_path = model_path | |
| ) | |
| pred = model.predict_image(img=image, return_str=False) | |
| for key in pred.keys(): | |
| pred[key] = pred[key]/100 | |
| return pred | |
| id2label = ["akiec", "bcc", "bkl", "df", "mel", "nv", "vasc"] | |
| samples = [["images/" + p + ".jpg"] for p in id2label] | |
| print(samples) | |
| image = gr.inputs.Image(shape=(224, 224), label="Upload Your Image Here") | |
| label = gr.outputs.Label(num_top_classes=len(id2label)) | |
| interface = gr.Interface( | |
| fn=predict_image, | |
| inputs=[image,radio], | |
| outputs=label, | |
| capture_session=True, | |
| allow_flagging=False, | |
| thumbnail="ressources/thumbnail.png", | |
| article=""" | |
| <html style="color: white;"> | |
| <style type="text/css"> | |
| .tg {border-collapse:collapse;border-spacing:0;} | |
| .tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px; | |
| overflow:hidden;padding:10px 5px;word-break:normal;} | |
| .tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px; | |
| font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;} | |
| .tg .tg-v0zy{background-color:#efefef;color:#000000;font-weight:bold;text-align:center;vertical-align:top} | |
| .tg .tg-4jb6{background-color:#ffffff;color:#333333;text-align:center;vertical-align:top} | |
| </style> | |
| <table class="tg"> | |
| <thead> | |
| <tr> | |
| <th class="tg-v0zy">Model</th> | |
| <th class="tg-v0zy">Accuracy</th> | |
| <th class="tg-v0zy">Size</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr> | |
| <td class="tg-4jb6">VGG16</td> | |
| <td class="tg-4jb6">38.27%</td> | |
| <td class="tg-4jb6">512.0 MB</td> | |
| </tr> | |
| <tr> | |
| <td class="tg-4jb6">DeiT</td> | |
| <td class="tg-4jb6">71.60%</td> | |
| <td class="tg-4jb6">327.0 MB</td> | |
| </tr> | |
| <tr> | |
| <td class="tg-4jb6">DenseNet121</td> | |
| <td class="tg-4jb6">77.78%</td> | |
| <td class="tg-4jb6">27.1 MB</td> | |
| </tr> | |
| <tr> | |
| <td class="tg-4jb6">MobileNetV2</td> | |
| <td class="tg-4jb6">75.31%</td> | |
| <td class="tg-4jb6">8.77 MB</td> | |
| </tr> | |
| <tr> | |
| <td class="tg-4jb6">ShuffleNetV2</td> | |
| <td class="tg-4jb6">76.54%</td> | |
| <td class="tg-4jb6">4.99 MB</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| </html> | |
| """, | |
| theme="darkhuggingface", | |
| title="HAM10000: Training and using a TorchVision Image Classifier in 5 min to identify skin cancer", | |
| description="A fast and easy tutorial to train a TorchVision Image Classifier that can help dermatologist in their identification procedures Melanoma cases with HugsVision and HAM10000 dataset.", | |
| allow_screenshot=True, | |
| show_tips=False, | |
| encrypt=False, | |
| examples=samples, | |
| ) | |
| interface.launch() |