Open In Colab

Creating a Transformers demo with Gradio

References

Example 1: Using the Transformers pipeline 

import gradio as gr
from transformers import pipeline

pipe = pipeline("text-classification", model="lewtun/xlm-roberta-base-finetuned-marc-en")

pipe("The Lord of the Rings is waaay too long to read!!")

label2emoji = {"terrible": "💩", "poor": "😾", "ok": "🐱", "good": "😺", "great": "😻"}

def predict(text):
    preds = pipe(text)[0]
    return label2emoji[preds["label"]], round(preds["score"], 5)

predict("I love this soccer ball")

gradio_ui = gr.Interface(
    fn=predict,
    title="Predicting review scores from customer reviews",
    description="Enter some review text about an Amazon product and check what the model predicts for it's star rating.",
    inputs=[
        gr.inputs.Textbox(lines=5, label="Paste some text here"),
    ],
    outputs=[
        gr.outputs.Textbox(label="Label"),
        gr.outputs.Textbox(label="Score"),
    ],
    examples=[
        ["My favourite book is Cryptonomicon!"], ["私の好きな本は「クリプトノミコン」です"]
    ],
)

gradio_ui.launch(debug=True)

Example 2: Using the Inference API from the Hugging Face Hub 

from huggingface_hub import InferenceApi

text = "My favourite book is Cryptonomicon!"
inference = InferenceApi("lewtun/xlm-roberta-base-finetuned-marc-en")
preds = inference(inputs=text)
preds[0]

sorted_preds = sorted(preds[0], key=lambda d: d['score'], reverse=True) 
sorted_preds

def inference_predict(text):
    inference = InferenceApi("lewtun/xlm-roberta-base-finetuned-marc-en")
    preds = inference(inputs=text)
    sorted_preds = sorted(preds[0], key=lambda d: d['score'], reverse=True)[0]
    return label2emoji[sorted_preds["label"]], round(sorted_preds["score"], 5)

inference_predict(text)

gradio_ui = gr.Interface.load(
    name="lewtun/xlm-roberta-base-finetuned-marc",
    src="huggingface",
    fn=inference_predict,
    title="Review analysis",
    description="Enter some text and check if model detects it's star rating.",
    inputs=[
        gr.inputs.Textbox(lines=5, label="Paste some text here"),
    ],
    outputs=[
        gr.outputs.Textbox(label="Label"),
        gr.outputs.Textbox(label="Score"),
    ],
    examples=[
        ["My favourite book is Cryptonomicon!"], ["私の好きな本は「クリプトノミコン」です"]
    ],
)

gradio_ui.launch(debug=True)