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)