|
import datasets |
|
from transformers import AutoFeatureExtractor, AutoModelForImageClassification |
|
import gradio as gr |
|
|
|
dataset = datasets.load_dataset('beans','full_size') |
|
|
|
extractor = AutoFeatureExtractor.from_pretrained("saved_model_files") |
|
model = AutoModelForImageClassification.from_pretrained("saved_model_files") |
|
|
|
labels = dataset['train'].features['labels'].names |
|
|
|
def classify(im): |
|
features = extractor(im, return_tensors='pt') |
|
with torch.no_grad(): |
|
logits = model(features["pixel_values"])[-1] |
|
logits = torch.nn.functional.softmax(logits, dim=-1) |
|
probability = torch.nn.functional.softmax(logits, dim=-1) |
|
probs = probability[0].detach().numpy() |
|
confidences = {label: float(probs[i]*100) for i, label in enumerate(labels)} |
|
print(confidences) |
|
return confidences |
|
|
|
|
|
interface = gr.Interface(fn=classify, |
|
inputs=gr.inputs.Image(type="pil"), |
|
outputs=gr.Label(num_top_classes=3), |
|
examples=["https://datasets-server.huggingface.co/assets/beans/--/default/validation/3/image/image.jpg", |
|
"https://datasets-server.huggingface.co/assets/beans/--/default/test/20/image/image.jpg", |
|
"https://datasets-server.huggingface.co/assets/beans/--/default/test/70/image/image.jpg"]) |
|
|
|
|
|
interface.launch() |
|
|