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# https://huggingface.co/spaces/CK42/sentiment-model-comparison/blob/main/app.py
# 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 = "juliensimon/distilbert-amazon-shoe-reviews"
model_id_2 = "juliensimon/distilbert-amazon-shoe-reviews"
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
# classifier = pipeline("text-classification", model="juliensimon/distilbert-amazon-shoe-reviews")
def predict(review, model_id):
classifier = pipeline("text-classification", model=model_id)
prediction = classifier(review)
print(prediction)
stars = prediction[0]['label']
stars = (int)(stars.split('_')[1])+1
score = 100*prediction[0]['score']
return "{} {:.0f}%".format("\U00002B50"*stars, score)
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.")
out_2 = gr.Textbox(label="Prediction")
# gr.Markdown(
# """
# Model Predictions
# """)
with gr.Row():
model1_input = gr.Textbox(label="Model 1")
with gr.Row():
btn = gr.Button("Prediction for Model 1")
btn.click(fn=load_agent(model_id_1), inputs=inp_1, outputs=out_2)
with gr.Row():
model2_input = gr.Textbox(label="Model 2")
with gr.Row():
btn = gr.Button("Prediction for Model 2")
btn.click(fn=predict(model_id_2), inputs=inp_1, outputs=out_2)
# app_button.click(load_agent, inputs=[model1_input, model2_input], outputs=[model1_name, model1_score_output, model2_name, model2_score_output])
# examples = gr.Examples(examples=[["juliensimon/distilbert-amazon-shoe-reviews","juliensimon/distilbert-amazon-shoe-reviews"]],
# inputs=[model1_input, model2_input])
app.launch()
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