import gradio as gr
import onnxruntime as rt
from transformers import AutoTokenizer
import torch, json

tokenizer = AutoTokenizer.from_pretrained("roberta-base")

with open("recipe_types_encoded19.json", "r") as fp:
  encode_types = json.load(fp)

recipe = list(encode_types.keys())

inf_session = rt.InferenceSession('recipe-classifier.onnx')
input_name = inf_session.get_inputs()[0].name
output_name = inf_session.get_outputs()[0].name

def classify_recipe(description):
    input_ids = tokenizer(description)['input_ids'][:512]
    logits = inf_session.run([output_name], {input_name: [input_ids]})[0]
    logits = torch.FloatTensor(logits)
    probs = torch.sigmoid(logits)[0]
    result = {class_label: float(prob) for class_label, prob in zip(recipe,probs)}
    return result

label = gr.outputs.Label(num_top_classes=5)
iface = gr.Interface(fn=classify_recipe, inputs="text", outputs=label)
iface.launch(inline=False)