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from datasets import load_dataset | |
import gradio as gr | |
import torch | |
from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
# Define dataset, feature extractor, and model | |
dataset = 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 = 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 | |
interface = gr.Interface( | |
fn=classify, inputs="image", outputs="label", title="Bean leaf classification" | |
) | |
interface.launch(debug=True) | |