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Update app.py
Browse files
app.py
CHANGED
@@ -280,8 +280,13 @@ with gr.Blocks(fill_width=True) as demo:
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QA_run_button = gr.Button("Start QA", interactive=False)
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hsummary = gr.Textbox(label="Summary")
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# with gr.Tab("Event Type Classification Eval"):
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@@ -365,6 +370,16 @@ with gr.Blocks(fill_width=True) as demo:
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}
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"""
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# Test event listeners
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T_predict_button.click(
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@@ -407,7 +422,7 @@ with gr.Blocks(fill_width=True) as demo:
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outputs=[hsummary, qa_df])
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qa_df.select(T_on_select, None, T_tweetID)
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T_tweetID.change(fn=None, inputs=T_tweetID, outputs=None, js=
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# Event listener for single text classification
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QA_run_button = gr.Button("Start QA", interactive=False)
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hsummary = gr.Textbox(label="Summary")
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with gr.Group():
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with gr.Row():
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with gr.Column(scale=7):
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qa_df = gr.DataFrame()
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with gr.Column(scale=3):
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qa_tweet_embed = gr.HTML("""<div id="tweet-container2"></div>""")
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# with gr.Tab("Event Type Classification Eval"):
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}
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"""
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m2_js = """ (x) =>
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{
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reloadTwitterWidgets();
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const tweetContainer = document.getElementById("tweet-container2");
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tweetContainer.innerHTML = "";
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twttr.widgets.createTweet(x,tweetContainer,{theme: 'dark', dnt: true, align: 'center'});
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}
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"""
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# Test event listeners
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T_predict_button.click(
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outputs=[hsummary, qa_df])
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qa_df.select(T_on_select, None, T_tweetID)
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T_tweetID.change(fn=None, inputs=T_tweetID, outputs=None, js=m2_js)
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# Event listener for single text classification
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