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import datasets | |
from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
import gradio as gr | |
dataset = datasets.load_dataset("beans") | |
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).logits | |
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]) for i, label in enumerate(labels)} | |
return confidences | |
# following dummy till i figure out how to upload custom saved model | |
def classify1(im): | |
label = {'leaf spot' : 0.9, 'rust' : 0.1} | |
return label | |
interface = interface = gr.Interface(classify1, inputs='image', outputs='label', title='Leaf Classification demo', | |
description='Demo of fine-tuning a ViT for image classification based on the bean dataset classification') # FILL HERE | |
interface.launch() |