Update app.py

app.py

@@ -12,5 +12,45 @@ from bertopic import BERTopic
 from sklearn.cluster import KMeans
 import numpy as np
 
+venue = 'ICLR.cc/2023/Conference'
+venue_short = 'iclr2023'
+
+def get_conference_notes(venue, blind_submission=False):
+    """
+    Get all notes of a conference (data) from OpenReview API.
+    If results are not final, you should set blind_submission=True.
+    """
+
+    blind_param = '-/Blind_Submission' if blind_submission else ''
+    offset = 0
+    notes = []
+    while True:
+        print('Offset:', offset, 'Data:', len(notes))
+        url = f'https://api.openreview.net/notes?invitation={venue}/{blind_param}&offset={offset}'
+        response = requests.get(url)
+        data = response.json()
+        if len(data['notes']) == 0:
+            break
+        offset += 1000
+        notes.extend(data['notes'])
+    return notes
+
+raw_notes = get_conference_notes(venue, blind_submission=True)
+st.write("Number of submissions at ICLR 2023:", len(raw_notes))
+
+df_raw = pd.json_normalize(raw_notes)
+# set index as first column
+# df_raw.set_index(df_raw.columns[0], inplace=True)
+accepted_venues = ['ICLR 2023 poster', 'ICLR 2023 notable top 5%', 'ICLR 2023 notable top 25%']
+df = df_raw[df_raw["content.venue"].isin(accepted_venues)]
+st.write("Number of submissions accepted at ICLR 2023:", len(df))
+
+df_filtered = df[['id', 'content.title', 'content.keywords', 'content.abstract']]
+df = df_filtered
+
+list_of_abstracts = list(df["content.title"].values)
+
+
+
 x = st.slider('Select a value')
 st.write(x, 'squared is', x * x)