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import sklearn
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
#pipe = joblib.load("https://huggingface.co/spaces/scikit-learn/sentiment-analysis/tree/main/pipeline.pkl")
#inputs = [gr.Textbox(value = "The customer service was satisfactory.")]
#outputs = [gr.Label(label = "Sentiment")]
#title = "Sentiment Analysis"
app = gr.Blocks()
def load_agent(model_id_1, model_id_2):
"""
This function load the agent's results
"""
# Load the metrics
metadata_1 = get_metadata(model_id_1)
# Get the accuracy
results_1 = parse_metrics_accuracy(metadata_1)
# Load the metrics
metadata_2 = get_metadata(model_id_2)
# Get the accuracy
results_2 = parse_metrics_accuracy(metadata_2)
return model_id_1, results_1, model_id_2, results_2
def parse_metrics_accuracy(meta):
if "model-index" not in meta:
return None
result = meta["model-index"][0]["results"]
metrics = result[0]["metrics"]
accuracy = metrics[0]["value"]
return accuracy
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():
model1_input = gr.Textbox(label="Model 1")
model2_input = gr.Textbox(label="Model 2")
with gr.Row():
inp = gr.Textbox(label="Type text here.",placeholder="The customer service was satisfactory.")
out = gr.Textbox(label="Prediction")
btn = gr.Button("Run")
btn.click(fn=predict, inputs=inp, outputs=out)
gr.Markdown(
"""
Type two models id you want to compare or check examples below.
""")
with gr.Row():
model1_input = gr.Textbox(label="Model 1")
model2_input = gr.Textbox(label="Model 2")
with gr.Row():
app_button = gr.Button("Compare models")
with gr.Row():
with gr.Column():
model1_name = gr.Markdown()
model1_score_output = gr.Textbox(label="Sentiment")
with gr.Column():
model2_name = gr.Markdown()
model2_score_output = gr.Textbox(label="Sentiment")
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=[["distilbert-base-uncased-finetuned-sst-2-english","distilbert-base-uncased-finetuned-sst-2-english"],
["distilbert-base-uncased-finetuned-sst-2-english", "distilbert-base-uncased-finetuned-sst-2-english"]],
inputs=[model1_input, model2_input])
app.launch()