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# here are some examples for sadness, joy, anger, and optimism. | |
import gradio as gr | |
# Load model directly | |
from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
from transformers import pipeline | |
tokenizer = AutoTokenizer.from_pretrained("barbieheimer/MND_TweetEvalBert_model") | |
model = AutoModelForSequenceClassification.from_pretrained("barbieheimer/MND_TweetEvalBert_model") | |
# We can now use the model in the pipeline. | |
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer) | |
def predict(prompt): | |
completion = classifier(prompt) | |
return completion[0]["label"], completion[0]["score"] | |
examples = [ | |
["The movie was a bummer."], | |
["I cannot wait to watch all these movies!"], | |
["The ending of the movie really irks me, gives me the ick fr."], | |
["The protagonist seems to have a lot of hope...."] | |
] | |
gr.Interface.load("models/barbieheimer/MND_TweetEvalBert_model", fn=predict, title="Sentiment Analysis", examples=examples, | |
inputs=gr.inputs.Textbox(lines=5, label="Paste an Article here."), | |
outputs=[gr.outputs.Textbox(label="Label"),gr.outputs.Textbox(label="Score")],).launch() |