Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline | |
| class EmotionClassifier: | |
| def __init__(self): # since we have defined the models below, this class will call itself. | |
| self.model = AutoModelForSequenceClassification.from_pretrained("barbieheimer/MND_TweetEvalBert_model") # specify the model from repo. | |
| self.tokenizer = AutoTokenizer.from_pretrained("barbieheimer/MND_TweetEvalBert_model") #need to spicify the tokeniser from repo too | |
| self.pipeline = pipeline( | |
| "text-classification", # specify pipeline task here. | |
| model=self.model, | |
| tokenizer=self.tokenizer, | |
| return_all_scores=True, # so that all emotional scores are displayed. | |
| ) | |
| # creating a prediction definition. | |
| def predict(self, input_text: str): # defining what the output will look like. | |
| pred = self.pipeline(input_text)[0] # processing text input. | |
| result = { | |
| "Anger 😠": pred[0]["score"], | |
| "Joy 😂": pred[1]["score"], | |
| "Optimism 😲": pred[2]["score"], | |
| "Sadness 😭": pred[3]["score"], | |
| } | |
| return result | |
| def main(): | |
| # call the emotionclassifier class to use our model, and now we can use the gradio UI. | |
| model = EmotionClassifier() | |
| iface = gr.Interface( | |
| fn=model.predict, # using the model to predict | |
| inputs=gr.inputs.Textbox( | |
| lines=3, | |
| placeholder="Type a phrase that has some emotion", | |
| label="Input Text", | |
| ), | |
| outputs="label", | |
| title="Emotion Classification", | |
| 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...."] | |
| ], | |
| ) | |
| iface.launch() | |
| if __name__ == "__main__": | |
| main() | |