import gradio as gr from transformers import pipeline app = gr.Blocks() model_id_1 = "nlptown/bert-base-multilingual-uncased-sentiment" model_id_2 = "microsoft/deberta-xlarge-mnli" model_id_3 = "distilbert-base-uncased-finetuned-sst-2-english" model_id_4 = "lordtt13/emo-mobilebert" model_id_5 = "juliensimon/reviews-sentiment-analysis" model_id_6 = "sbcBI/sentiment_analysis_model" def parse_output(output_json): list_pred=[] for i in range(len(output_json[0])): label = output_json[0][i]['label'] score = output_json[0][i]['score'] list_pred.append((label, score)) return list_pred def get_prediction(model_id): classifier = pipeline("text-classification", model=model_id, return_all_scores=True) def predict(review): prediction = classifier(review) print(prediction) return parse_output(prediction) return predict with app: gr.Markdown( """ # Compare Sentiment Analysis Models Type text to predict sentiment. """) with gr.Row(): inp_1= gr.Textbox(label="Type text here.",placeholder="The customer service was satisfactory.") gr.Markdown( """ **Model Predictions** """) with gr.Row(): with gr.Column(): gr.Markdown( """ Model 1 = nlptown/bert-base-multilingual-uncased-sentiment """) btn1 = gr.Button("Predict - Model 1") gr.Markdown( """ Model 2 = microsoft/deberta-xlarge-mnli """) btn2 = gr.Button("Predict - Model 2") gr.Markdown( """ Model 3 = distilbert-base-uncased-finetuned-sst-2-english" """) btn3 = gr.Button("Predict - Model 3") gr.Markdown( """ Model 4 = lordtt13/emo-mobilebert """) btn4 = gr.Button("Predict - Model 4") gr.Markdown( """ Model 5 = juliensimon/reviews-sentiment-analysis """) btn5 = gr.Button("Predict - Model 5") gr.Markdown( """ Model 6 = sbcBI/sentiment_analysis_model """) btn6 = gr.Button("Predict - Model 6") with gr.Column(): out_1 = gr.Textbox(label="Predictions for Model 1") out_2 = gr.Textbox(label="Predictions for Model 2") out_3 = gr.Textbox(label="Predictions for Model 3") out_4 = gr.Textbox(label="Predictions for Model 4") out_5 = gr.Textbox(label="Predictions for Model 5") out_6 = gr.Textbox(label="Predictions for Model 6") btn1.click(fn=get_prediction(model_id_1), inputs=inp_1, outputs=out_1) btn2.click(fn=get_prediction(model_id_2), inputs=inp_1, outputs=out_2) btn3.click(fn=get_prediction(model_id_3), inputs=inp_1, outputs=out_3) btn4.click(fn=get_prediction(model_id_4), inputs=inp_1, outputs=out_4) btn5.click(fn=get_prediction(model_id_5), inputs=inp_1, outputs=out_5) btn6.click(fn=get_prediction(model_id_6), inputs=inp_1, outputs=out_6) app.launch()