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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=True,
examples=samples,
)
interface.launch() |