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Runtime error
Runtime error
added links to each paper results (replacing textbox with markdown)
Browse files
app.py
CHANGED
@@ -73,7 +73,7 @@ def get_similar_paper(
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input_sentences = sent_tokenize(abstract_text_input)
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num_sents = len(input_sentences)
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-
for aa, (tt, ab, ds) in enumerate(zip(titles, abstracts, doc_scores)):
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# Compute sent-level and phrase-level affinity scores for each papers
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sent_ids, sent_scores, info, top_pairs_info = get_highlight_info(
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sent_model,
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@@ -96,7 +96,8 @@ def get_similar_paper(
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'doc_score': '%0.3f'%ds,
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'source_sentences': input_sentences,
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'highlight': word_scores,
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'top_pairs': top_pairs_info
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}
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end = time.time()
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@@ -116,8 +117,9 @@ def get_similar_paper(
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summary_out = []
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for i in range(top_papers_show):
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out_tmp = [
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gr.update(value=titles[i], visible=True),
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gr.update(value=
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]
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tp = results[display_title[i]]['top_pairs']
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for j in range(top_num_info_show):
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@@ -173,7 +175,9 @@ def change_paper(selected_papers_radio, info={}):
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abstract = info[selected_papers_radio]['abstract']
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aff_score = info[selected_papers_radio]['doc_score']
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highlights = info[selected_papers_radio]['highlight']
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-
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else:
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return
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@@ -206,7 +210,7 @@ Below we describe how to use the tool. Also feel free to check out the [video]()
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- Below the list of papers, we highlight relevant parts from the selected paper to different sentences of the submission abstract.
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- On the left, you will see individual sentences from the submission abstract to select from.
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- On the right, you will see the abstract of the selected paper, with **highlights** incidating relevant parts to the selected sentence.
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- **<span style="color:black;background-color:#DB7262;">Red highlights</span>**: sentences with high semantic similarity to the selected sentence.
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- **<span style="color:black;background-color:#65B5E3;">Blue highlights</span>**: phrases included in the selected sentence.
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- To see relevant parts in a different paper from the reviewer, select the new paper.
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-------
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@@ -234,12 +238,11 @@ Below we describe how to use the tool. Also feel free to check out the [video]()
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# Paper title, score, and top-ranking sentence pairs -- two sentence pairs per paper, three papers
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## ONE BLOCK OF INFO FOR A SINGLE PAPER
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## PAPER1
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# TODO add link to each paper
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with gr.Row():
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with gr.Column(scale=3):
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paper_title1 = gr.
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with gr.Column(scale=1):
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affinity1 = gr.
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with gr.Row() as rel1_1:
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with gr.Column(scale=1):
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sent_pair_score1_1 = gr.Textbox(label='Sentence Relevance', interactive=False, value='', visible=False)
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@@ -267,9 +270,10 @@ Below we describe how to use the tool. Also feel free to check out the [video]()
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## PAPER 2
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with gr.Row():
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with gr.Column(scale=3):
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paper_title2 = gr.
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with gr.Column(scale=1):
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affinity2 = gr.Textbox(label='Affinity', interactive=False, value='', visible=False)
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with gr.Row() as rel2_1:
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with gr.Column(scale=1):
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sent_pair_score2_1 = gr.Textbox(label='Sentence Relevance', interactive=False, value='', visible=False)
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@@ -297,9 +301,10 @@ Below we describe how to use the tool. Also feel free to check out the [video]()
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## PAPER 3
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with gr.Row():
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with gr.Column(scale=3):
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paper_title3 = gr.
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with gr.Column(scale=1):
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affinity3 = gr.Textbox(label='Affinity', interactive=False, value='', visible=False)
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with gr.Row() as rel3_1:
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with gr.Column(scale=1):
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sent_pair_score3_1 = gr.Textbox(label='Sentence Relevance', interactive=False, value='', visible=False)
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@@ -336,7 +341,7 @@ Below we describe how to use the tool. Also feel free to check out the [video]()
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# selected paper information
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with gr.Row(visible=False) as title_row:
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with gr.Column(scale=3):
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paper_title = gr.
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with gr.Column(scale=1):
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affinity= gr.Textbox(label='Affinity', interactive=False, value='')
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with gr.Row():
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input_sentences = sent_tokenize(abstract_text_input)
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num_sents = len(input_sentences)
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+
for aa, (tt, ab, ds, url) in enumerate(zip(titles, abstracts, doc_scores, paper_urls)):
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# Compute sent-level and phrase-level affinity scores for each papers
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sent_ids, sent_scores, info, top_pairs_info = get_highlight_info(
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sent_model,
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'doc_score': '%0.3f'%ds,
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'source_sentences': input_sentences,
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'highlight': word_scores,
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'top_pairs': top_pairs_info,
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'url': url
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}
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end = time.time()
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summary_out = []
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for i in range(top_papers_show):
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out_tmp = [
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#gr.update(value=titles[i], visible=True),
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gr.update(value="#### [%s](%s)"%(titles[i], paper_urls[i]), visible=True),
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gr.update(value='#### Affinity: %0.3f'%doc_scores[i], visible=True) # document affinity
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]
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tp = results[display_title[i]]['top_pairs']
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for j in range(top_num_info_show):
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abstract = info[selected_papers_radio]['abstract']
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aff_score = info[selected_papers_radio]['doc_score']
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highlights = info[selected_papers_radio]['highlight']
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url = info[selected_papers_radio]['url']
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title_out = '#### [%s](%s)'%(title, url) # output in format of markdown
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return title_out, abstract, aff_score, highlights['0']
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else:
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return
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- Below the list of papers, we highlight relevant parts from the selected paper to different sentences of the submission abstract.
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- On the left, you will see individual sentences from the submission abstract to select from.
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- On the right, you will see the abstract of the selected paper, with **highlights** incidating relevant parts to the selected sentence.
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+
- **<span style="color:black;background-color:#DB7262;">Red highlights</span>**: sentences with high semantic similarity to the selected sentence. The darker the color, the higher the similarity.
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- **<span style="color:black;background-color:#65B5E3;">Blue highlights</span>**: phrases included in the selected sentence.
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- To see relevant parts in a different paper from the reviewer, select the new paper.
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-------
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# Paper title, score, and top-ranking sentence pairs -- two sentence pairs per paper, three papers
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## ONE BLOCK OF INFO FOR A SINGLE PAPER
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## PAPER1
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with gr.Row():
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with gr.Column(scale=3):
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paper_title1 = gr.Markdown(value='', visible=False)
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with gr.Column(scale=1):
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affinity1 = gr.Markdown(value='', visible=False)
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with gr.Row() as rel1_1:
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with gr.Column(scale=1):
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sent_pair_score1_1 = gr.Textbox(label='Sentence Relevance', interactive=False, value='', visible=False)
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## PAPER 2
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with gr.Row():
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with gr.Column(scale=3):
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paper_title2 = gr.Markdown(value='', visible=False)
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with gr.Column(scale=1):
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#affinity2 = gr.Textbox(label='Affinity', interactive=False, value='', visible=False)
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affinity2 = gr.Markdown(value='', visible=False)
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with gr.Row() as rel2_1:
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with gr.Column(scale=1):
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sent_pair_score2_1 = gr.Textbox(label='Sentence Relevance', interactive=False, value='', visible=False)
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## PAPER 3
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with gr.Row():
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with gr.Column(scale=3):
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paper_title3 = gr.Markdown(value='', visible=False)
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with gr.Column(scale=1):
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# affinity3 = gr.Textbox(label='Affinity', interactive=False, value='', visible=False)
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affinity3 = gr.Markdown(value='', visible=False)
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with gr.Row() as rel3_1:
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with gr.Column(scale=1):
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sent_pair_score3_1 = gr.Textbox(label='Sentence Relevance', interactive=False, value='', visible=False)
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# selected paper information
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with gr.Row(visible=False) as title_row:
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with gr.Column(scale=3):
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paper_title = gr.Markdown(value='')
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with gr.Column(scale=1):
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affinity= gr.Textbox(label='Affinity', interactive=False, value='')
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with gr.Row():
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