import gradio as gr
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer, AutoConfig
import numpy as np
from scipy.special import softmax
# Preprocess text (username and link placeholders)
def preprocess(text):
    new_text = []
    for t in text.split(" "):
        t = '@user' if t.startswith('@') and len(t) > 1 else t
        t = 'http' if t.startswith('http') else t
        new_text.append(t)
    return " ".join(new_text)

def inference(text):
    model_path="twitter-roberta-base-sentiment-latest"
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    config = AutoConfig.from_pretrained(model_path)
    # PT
    model = AutoModelForSequenceClassification.from_pretrained(model_path)

    text = preprocess(text)
    encoded_input = tokenizer(text, return_tensors='pt')
    output = model(**encoded_input)
    scores = output[0][0].detach().numpy()
    scores = softmax(scores)

    ranking = np.argsort(scores)
    ranking = ranking[::-1]
    for i in range(scores.shape[0]):
        l = config.id2label[ranking[i]]
        s = scores[ranking[i]]
        return f"{i+1}) {l} {np.round(float(s), 4)}"


title = "sentiment analysis:twitter-roberta-base-sentiment"
description = "这是twitter-roberta-base-sentiment的Gradio Demo。 上传你想要的图像或者点击下面的示例来加载它。"
article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>"
examples=[["Covid cases are increasing fast!"]]

demo = gr.Interface(
    fn=inference,
    inputs=[gr.inputs.Textbox()],
    outputs=gr.outputs.Textbox(),
    title=title,
    description=description,
    article=article,
    examples=examples)

demo.launch(server_name="0.0.0.0")