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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() |