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import datasets
import torch
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
dataset = datasets.load_dataset('beans') # This should be the same as the first line of Python code in this Colab notebook
feature_extractor = AutoFeatureExtractor.from_pretrained("saved_model_files")
model = AutoModelForImageClassification.from_pretrained("saved_model_files")
labels = dataset['train'].features['labels'].names
def classify(im):
features = feature_extractor(im, return_tensors='pt')
logits = model(features["pixel_values"])[-1]
probability = torch.nn.functional.softmax(logits, dim=-1)
probs = probability[0].detach().numpy()
confidences = {label: float(probs[i]) for i, label in enumerate(labels)}
return confidences
import gradio as gr
interface = gr.Interface(
classify,
inputs='image',
outputs='label',
)
interface.launch(debug=True)