import gradio as gr
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from transformers import pipeline

def greet(name):
    return "Hello " + name + "!!"

def classify(text):
    return {"cat": 0.3, "dog": 0.7}

def predict_sentiment(text):
    if model == "finiteautomata/bertweet-base-sentiment-analysis":
        # pipe = pipeline("text-classification", model="finiteautomata/bertweet-base-sentiment-analysis")
        # out = pipe(text, return_all_scores=True)
        # return {pred["label"]: pred["score"] for pred in out[0]}
        return {"cathf": 0.3, "doghf": 0.7}
    elif model == "vader":
        # nltk.download('vader_lexicon')
        # sia = SentimentIntensityAnalyzer()
        # return sia.polarity_scores(text)
        return {"catv": 0.3, "dogv": 0.7}
        


demo = gr.Blocks()

with demo:
    gr.Markdown("A bunch of different Gradio demos in tabs.\n\nNote that generally, the code that is in each tab could be its own Gradio application!")
    with gr.Tabs():
        with gr.TabItem("Basic Hello"):
            gr.Markdown('The most basic "Hello World"-type demo you can write')
            interface = gr.Interface(fn=greet, inputs="text", outputs="text")
        with gr.TabItem("Label Output"):
            gr.Markdown("An example of a basic interface with a classification label as output")
            interface = gr.Interface(fn=classify, inputs="text", outputs="label")

        with gr.TabItem("Multiple Inputs"):
            gr.Markdown("A more complex interface for sentiment analysis with multiple inputs, including a dropdown, and some examples")
            demo = gr.Interface(
                fn=predict_sentiment,
                inputs=[
                    gr.Textbox(),
                    # gr.Dropdown(
                    #     ["finiteautomata/bertweet-base-sentiment-analysis", "vader"], label="Model"
                    # ),
                ],
                outputs="label"
            )

demo.launch()