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import datasets | |
import transformers | |
import torch | |
import gradio as gr | |
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") | |
model = model.eval() # set to eval mode for predictions | |
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 | |
# gradio app | |
interface = gr.Interface(fn = classify, | |
inputs = "image", | |
outputs = "label", | |
title = "Leaf Health Classifier", | |
examples =["beans1.jpg", "beans2.jpg", "beans3.jpg"], | |
description = "A fine tuned ViT based image classfier which returns the health of a bean leaf") | |
interface.launch(debug=True) | |