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()