Spaces:
Running
Running
File size: 2,551 Bytes
667fe9d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
from __future__ import annotations
from pathlib import Path
import gradio as gr
from constants import MODELS_DIR
from model import predict, tokenize
CSS_PATH = Path("style.css")
TOKENIZER_EXT = ".tokenizer.pkl"
MODEL_EXT = ".model.pkl"
POSITIVE_LABEL = "Positive 😊"
NEGATIVE_LABEL = "Negative 😤"
REFRESH_SYMBOL = "🔄"
def load_style() -> str:
if not CSS_PATH.is_file():
return ""
with Path.open(CSS_PATH) as f:
return f.read()
def predict_wrapper(text: str, tokenizer: str, model: str) -> str:
toks = tokenize(text, MODELS_DIR / f"{tokenizer}{TOKENIZER_EXT}")
pred = predict(toks, MODELS_DIR / f"{model}{MODEL_EXT}")
return POSITIVE_LABEL if pred else NEGATIVE_LABEL
def train_wrapper() -> None:
msg = "Training is not supported in the GUI."
raise NotImplementedError(msg)
def evaluate_wrapper() -> None:
msg = "Evaluation is not supported in the GUI."
raise NotImplementedError(msg)
with gr.Blocks(css=load_style()) as demo:
gr.Markdown("## Sentiment Analysis")
with gr.Row(equal_height=True):
textbox = gr.Textbox(
lines=10,
label="Enter text to analyze",
placeholder="Enter text here",
key="input-textbox",
)
with gr.Column():
output = gr.Label()
with gr.Row(elem_classes="justify-between"):
clear_btn = gr.ClearButton([textbox, output], value="Clear 🧹")
analyze_btn = gr.Button(
"Analyze 🔍",
variant="primary",
interactive=False,
)
with gr.Row():
tokenizer_selector = gr.Dropdown(
choices=[tkn.stem[: -len(".tokenizer")] for tkn in MODELS_DIR.glob(f"*{TOKENIZER_EXT}")],
label="Tokenizer",
key="tokenizer-selector",
)
model_selector = gr.Dropdown(
choices=[mdl.stem[: -len(".model")] for mdl in MODELS_DIR.glob(f"*{MODEL_EXT}")],
label="Model",
key="model-selector",
)
# TODO: Refresh button
# Event handlers
textbox.input(
fn=lambda text: gr.update(interactive=bool(text.strip())),
inputs=[textbox],
outputs=[analyze_btn],
)
analyze_btn.click(
fn=predict_wrapper,
inputs=[textbox, tokenizer_selector, model_selector],
outputs=[output],
)
demo.queue()
demo.launch()
|