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| # import sklearn | |
| from os import O_ACCMODE | |
| import gradio as gr | |
| import joblib | |
| from transformers import pipeline | |
| import requests.exceptions | |
| from huggingface_hub import HfApi, hf_hub_download | |
| from huggingface_hub.repocard import metadata_load | |
| app = gr.Blocks() | |
| model_id_1 = "nlptown/bert-base-multilingual-uncased-sentiment" | |
| model_id_2 = "microsoft/deberta-base" | |
| model_id_3 = "distilbert-base-uncased-finetuned-sst-2-english" | |
| model_id_4 = "lordtt13/emo-mobilebert" | |
| model_id_5 = "juliensimon/reviews-sentiment-analysis" | |
| def parse_output(output_json): # list of list of dicts | |
| 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-base | |
| """) | |
| 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") | |
| 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") | |
| 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) | |
| app.launch() |