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import os
import pandas as pd
import requests
from plotnine import aes, geom_point, ggplot, labs, theme_light
from shiny import module, reactive, render, ui
@module.ui
def query_output_ui():
out = ui.row(
ui.column(
4,
ui.input_text("prompt", "Prompt", placeholder="Enter query"),
),
ui.column(4, ui.output_table("score_table")),
ui.column(4, ui.output_plot("score_plot")),
)
return out
@module.server
def query_output_server(input, output, session):
@reactive.Calc
def response_table():
# This is included to both show the expected API response, and populate
# the downstream item with zeros before a prompt is entered.
if input.prompt() == "":
resp = [
[
{"label": "neutral", "score": 0},
{"label": "surprise", "score": 0},
{"label": "fear", "score": 0},
{"label": "anger", "score": 0},
{"label": "disgust", "score": 0},
{"label": "sadness", "score": 0},
{"label": "joy", "score": 0},
]
]
else:
resp = query(input.prompt())
df = pd.DataFrame(
{
"sentiment": [x["label"] for x in resp[0]],
"score": [x["score"] for x in resp[0]],
}
)
return df
@output
@render.plot
def score_plot():
return plot_response(response_table(), input.prompt())
@output
@render.table()
def score_table():
return response_table()
def plot_response(df, plot_title):
out = (
ggplot(df, aes(y="reorder(sentiment, score)", x="score"))
+ geom_point()
+ theme_light()
+ labs(title=f'Prompt: "{plot_title}"', y="Sentiment", x="Score")
)
return out
def query(text):
API_URL = "https://api-inference.huggingface.co/models/j-hartmann/emotion-english-distilroberta-base"
headers = {"Authorization": "Bearer " + os.environ["HF_API_KEY"]}
payload = {"inputs": text}
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
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