import gradio as gr
from tweetnlp import Sentiment

def classify(tweet):
    # return "Hello " + name + "!!"
    model_output = model.sentiment(tweet)
    # Fill in both positive and negative values
    if model_output["label"] == "positive":
        formatted_output = dict()
        formatted_output["positive"] = model_output["probability"]
        formatted_output["negative"] = 1 - model_output["probability"]
    else:
        formatted_output = dict()
        formatted_output["negative"] = model_output["probability"]
        formatted_output["positive"] = 1 - model_output["probability"]
    return formatted_output



if __name__ == "__main__":
    # https://github.com/cardiffnlp/tweetnlp
    model = Sentiment()
    iface = gr.Interface(fn=classify, inputs="text", outputs="label")
    iface.launch()