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import gradio as gr
import typing
from headline_detector import FasttextDetector, CNNDetector, IndoBERTweetDetector
model_dict = {
"IndoBERTweet": IndoBERTweetDetector,
"CNN": CNNDetector,
"fastText": FasttextDetector,
}
num_text_process_limit = 10
default_model = "fastText"
scenario_number = ["1", "2", "3", "4", "5", "6"]
string_seperator = "|||"
markdown_top_info = """
# [headline_detector](https://huggingface.co/kaenova/headline_detector) spaces
Easily use the model with huggingface 🤗 and gradio interface!
Call by API is also available. Check the bottom of the page on 'Use via API 🚀'
"""
def process_single_text(
text_input: "str", model_name: "str", scenario_number_choose: "str"
):
# Split input
prediction_input = text_input.split(string_seperator)
if len(prediction_input) > num_text_process_limit:
prediction_input = prediction_input[:num_text_process_limit]
# Checking and finalizing scneario number
if not isinstance(scenario_number_choose, int):
try:
scenario_number_choose = int(scenario_number_choose)
except:
raise gr.Error("Not a valid scenario number")
if f"{scenario_number_choose}" not in scenario_number:
raise gr.Error("Scenario number must between 1-6")
# Checking model name
if model_name not in list(model_dict.keys()):
raise gr.Error("Model name must either 'IndoBERTweet' or 'CNN' or 'fastText'")
# Inferencing to library
model: typing.Union[
FasttextDetector, IndoBERTweetDetector, CNNDetector
] = model_dict[model_name]
model = model.load_from_scenario(scenario_number_choose)
results = model.predict_text(prediction_input)
# Building returned value
final_string_results = ""
for i, data in enumerate(results):
if i != 0:
final_string_results += "\n"
if data == 0:
final_string_results += "0"
elif data == 1:
final_string_results += "1"
return final_string_results
with gr.Blocks() as demo:
gr.Markdown(markdown_top_info)
# Single Input
with gr.Column():
input_text = gr.Textbox(
label="Input Text",
info=f"Text to determine wether headline or not. For multiple input you can divide each string by '{string_seperator}'. Maximum processed string is {num_text_process_limit}.",
lines=5,
value="""Kami adalah|||\nseorang kapiten|||daitas kamu ada jurang|||\nDITEMUKAN PRIA DIATAS JURANG"""
)
model_used = gr.Dropdown(
list(model_dict.keys()),
label="Model",
info="Model used to determine the text",
value=default_model,
interactive=True,
allow_custom_value=False,
)
scenario_used = gr.Radio(
scenario_number,
label="Scenario Number",
type="value",
info="Preprocessing type (for more information, refer to the paper)",
value="1",
)
single_text_button = gr.Button("Detect!")
results_text = gr.Textbox(
label="Result",
interactive=False,
info="Each line representing every string. Headline (1) or non-Headline (0)",
)
single_text_button.click(
process_single_text,
inputs=[input_text, model_used, scenario_used],
outputs=[results_text],
api_name="predict_single_text",
)
gr.Markdown("_Notes:_ for first time running, all models may take 20-60 seconds to load")
if __name__ == "__main__":
demo.launch()
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