import gradio as gr
import datasets
import torch
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
from transformers import ViTImageProcessor, ViTForImageClassification

dataset = datasets.load_dataset("beans")
image_processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224")

extractor = AutoFeatureExtractor.from_pretrained("saved_model_files", from_pt=True)
model = AutoModelForImageClassification.from_pretrained("saved_model_files")
model.config.to_json_file("./saved_model_files/config.json")



labels = dataset['train'].features['labels'].names

def classify(im):
      features = image_processor(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")

interface.launch(debug=True)