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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 = "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):
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.")
gr.Markdown(
"""
Model Predictions
""")
# with gr.Row():
# model1_input = gr.Textbox(label="Model 1")
with gr.Row():
btn1 = gr.Button("Prediction for Model 1")
with gr.Row():
out_1 = gr.Textbox(label="Prediction")
classifier = pipeline("text-classification", model=model_id_1)
btn1.click(fn=predict, inputs=inp_1, outputs=out_1)
# with gr.Row():
# model2_input = gr.Textbox(label="Model 2")
with gr.Row():
btn2 = gr.Button("Prediction for Model 2")
with gr.Row():
out_2 = gr.Textbox(label="Prediction")
classifier = pipeline("text-classification", model=model_id_2)
btn2.click(fn=predict, inputs=inp_1, outputs=out_2)
app.launch() |