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Add app.py and requirements.txt
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import datasets
import gradio as gr
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
import torch
dataset = datasets.load_dataset('beans')
extractor = AutoFeatureExtractor.from_pretrained("rahult/bean_classification")
model = AutoModelForImageClassification.from_pretrained("rahult/bean_classification")
model.eval()
labels = dataset['train'].features['labels'].names
def classify(im):
features = extractor(im, return_tensors='pt')
with torch.no_grad():
logits = model(**features).logits
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", allow_flagging='manual')
interface.launch()