Spaces:
Running
Running
File size: 2,778 Bytes
db66b47 75f1b92 7af13f4 da4611f 48193db 7af13f4 f04ccc3 9f35afb d68a14d db66b47 b2f58d6 db66b47 534efde 258c1c8 db66b47 7af13f4 d3d7595 7af13f4 06b445f a14bbf4 48193db 75f1b92 81d1362 03b5ddd 81d1362 d68a14d 29d2c40 406cf9a 29d2c40 406cf9a 29d2c40 406cf9a 29d2c40 406cf9a 29d2c40 406cf9a 2a80f3b 326066b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
# 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 get_prediction(model_id):
classifier = pipeline("text-classification", model=model_id, return_all_scores=True)
def predict(review):
prediction = classifier(review)
print(prediction)
return list(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() |