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import os
import time
import gradio as gr
import pandas as pd
from classifier import classify
HFTOKEN = os.environ["HF_TOKEN"]
def load_and_analyze_csv(file, text_field, event_model):
df = pd.read_table(file.name)
if text_field not in df.columns:
raise gr.Error(f"Error: Enter text column'{text_field}' not in CSV file.")
floods, fires, nones = [], [], []
for post in df[text_field].to_list():
res = classify(post, event_model, HFTOKEN)
if res["event"] == 'flood':
floods.append(post)
elif res["event"] == 'fire':
fires.append(post)
else:
nones.append(post)
fire_related = gr.CheckboxGroup(choices=fires)
flood_related = gr.CheckboxGroup(choices=floods)
not_related = gr.CheckboxGroup(choices=nones)
# time.sleep(5)
return flood_related, fire_related, not_related
def analyze_selected_texts(selections):
selected_texts = selections
analysis_results = [f"Word Count: {len(text.split())}" for text in selected_texts]
result_df = pd.DataFrame({"Selected Text": selected_texts, "Analysis": analysis_results})
return result_df
with gr.Blocks() as demo:
event_models = ["jayebaku/distilbert-base-multilingual-cased-crexdata-relevance-classifier"]
with gr.Tab("Event Type Classification"):
with gr.Row(equal_height=True):
with gr.Column(scale=4):
file_input = gr.File(label="Upload CSV File")
with gr.Column(scale=6):
text_field = gr.Textbox(label="Text field name", value="tweet_text")
event_model = gr.Dropdown(event_models, label="Select classification model")
predict_button = gr.Button("Start Prediction")
with gr.Row(): # XXX confirm this is not a problem later --equal_height=True
with gr.Column():
gr.Markdown("""### Flood-related""")
flood_checkbox_output = gr.CheckboxGroup(label="Select ONLY incorrect classifications", interactive=True)
with gr.Column():
gr.Markdown("""### Fire-related""")
fire_checkbox_output = gr.CheckboxGroup(label="Select ONLY incorrect classifications", interactive=True)
with gr.Column():
gr.Markdown("""### None""")
none_checkbox_output = gr.CheckboxGroup(label="Select ONLY incorrect classifications", interactive=True)
predict_button.click(load_and_analyze_csv, inputs=[file_input, text_field, event_model],
outputs=[flood_checkbox_output, fire_checkbox_output, none_checkbox_output])
with gr.Tab("Question Answering"):
# XXX Add some button disabling here, if the classification process is not completed first XXX
analysis_button = gr.Button("Analyze Selected Texts")
analysis_output = gr.DataFrame(headers=["Selected Text", "Analysis"])
analysis_button.click(analyze_selected_texts, inputs=flood_checkbox_output, outputs=analysis_output)
demo.launch() |