app.py

from ast import Str
import gradio as gr
from tweetnlp import Sentiment, NER
from typing import Tuple, Dict
from statistics import mean

def clean_tweet(tweet: str, remove_chars: str = "@#") -> str:
    """Remove any unwanted characters
    Args:
        tweet (str): The raw tweet
        remove_chars (str, optional): The characters to remove. Defaults to "@#".
    Returns:
        str: The tweet with these characters removed
    """
    for char in remove_chars:
        tweet = tweet.replace(char, "")
    return tweet


def format_sentiment(model_output: Dict) -> Dict:
    """Format the output of the sentiment model
    Args:
        model_output (Dict): The model output
    Returns:
        Dict: The format for gradio
    """
    formatted_output = dict()
    if model_output["label"] == "positive":
        formatted_output["positive"] = model_output["probability"]
        formatted_output["negative"] = 1 - model_output["probability"]
    else:
        formatted_output["negative"] = model_output["probability"]
        formatted_output["positive"] = 1 - model_output["probability"]
    return formatted_output


def format_entities(model_output: Dict) -> Dict:
    """Format the output of the NER model
    Args:
        model_output (Dict): The model output
    Returns:
        Dict: The format for gradio
    """
    formatted_output = dict()
    for entity in model_output["entity_prediction"]:
        new_output = dict()
        name = " ".join(entity["entity"])
        entity_type = entity["type"]
        new_key = f"{name}:{entity_type}"
        new_value = mean(entity["probability"])
        formatted_output[new_key] = new_value
    return formatted_output


def classify(tweet: str) -> Tuple[Dict, Dict]:
    """Runs models
    Args:
        tweet (str): The raw tweet
    Returns:
        Tuple[Dict, Dict]: The formatted_sentiment and formatted_entities of the tweet
    """
    tweet = clean_tweet(tweet)
    # Get sentiment
    model_sentiment = se_model.sentiment(tweet)
    formatted_sentiment = format_sentiment(model_sentiment)
    # Get entities
    entities = ner_model.ner(tweet)
    formatted_entities = format_entities(entities)
    return formatted_sentiment, formatted_entities


if __name__ == "__main__":
    # https://github.com/cardiffnlp/tweetnlp
    se_model = Sentiment()
    ner_model = NER()

    # Get a few examples from: https://twitter.com/NFLFantasy
    examples = list()
    examples.append("Dameon Pierce is clearly the #Texans starter and he once again looks good")
    examples.append("Deebo Samuel had 150+ receiving yards in 4 games last year - the most by any receiver in the league.")

    iface = gr.Interface(fn=classify, inputs="text", outputs=["label", "label"], examples=examples)
    iface.launch()