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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 load_agent(model_id):
"""
This function load the agent's results
"""
# Load the metrics
metadata = get_metadata(model_id)
# get predictions
predictions = predict(model_id)
return model_id, predictions
def get_metadata(model_id):
"""
Get the metadata of the model repo
:param model_id:
:return: metadata
"""
try:
readme_path = hf_hub_download(model_id, filename="README.md")
metadata = metadata_load(readme_path)
print(metadata)
return metadata
except requests.exceptions.HTTPError:
return None
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 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**
""")
gr.Markdown(
"""
Model 1 = nlptown/bert-base-multilingual-uncased-sentiment
""")
with gr.Row():
btn1 = gr.Button("Predict - Model 1")
with gr.Row():
out_1 = gr.Textbox(label="Predictions for Model 1")
btn1.click(fn=get_prediction(model_id_1), inputs=inp_1, outputs=out_1)
gr.Markdown(
"""
Model 2 = microsoft/deberta-base
""")
with gr.Row():
btn2 = gr.Button("Predict - Model 2")
with gr.Row():
out_2 = gr.Textbox(label="Predictions for Model 2")
btn2.click(fn=get_prediction(model_id_2), inputs=inp_1, outputs=out_2)
gr.Markdown(
"""
Model 3 = distilbert-base-uncased-finetuned-sst-2-english"
""")
with gr.Row():
btn3 = gr.Button("Predict - Model 3")
with gr.Row():
out_3 = gr.Textbox(label="Predictions for Model 3")
btn3.click(fn=get_prediction(model_id_3), inputs=inp_1, outputs=out_3)
gr.Markdown(
"""
Model 4 = lordtt13/emo-mobilebert
""")
with gr.Row():
btn4 = gr.Button("Predict - Model 4")
with gr.Row():
out_4 = gr.Textbox(label="Predictions for Model 4")
btn3.click(fn=get_prediction(model_id_4), inputs=inp_1, outputs=out_4)
gr.Markdown(
"""
Model 5 = juliensimon/reviews-sentiment-analysis
""")
with gr.Row():
btn5 = gr.Button("Predict - Model 5")
with gr.Row():
out_5 = gr.Textbox(label="Predictions for Model 5")
btn5.click(fn=get_prediction(model_id_5), inputs=inp_1, outputs=out_5)
app.launch() |