From cec8f555d7dcc18f3a629c8fee65dd5521be9eef Mon Sep 17 00:00:00 2001
From: Russell Jarvis <russelljarvis@protonmail.com>
Date: Fri, 10 Dec 2021 14:49:54 +1100
Subject: [PATCH] update for web app
---
app.py | 42 +++++++++++++++++++++---------------------
1 file changed, 21 insertions(+), 21 deletions(-)
diff --git a/app.py b/app.py
index 9b35d06..6183fd2 100644
--- a/app.py
+++ b/app.py
@@ -613,31 +613,31 @@ def main():
np.mean(average_reading_time), author_name, len(ar)
)
)
- if "sentiment" in genre:
- sentiment = []
- uniqueness = []
- for block in trainingDats:
- uniqueness.append(block["uniqueness"])
- sentiment.append(block["sp"])
- temp = np.mean(sentiment) < np.mean([r["sp"] for r in ar])
-
- st.markdown("""### Sentiment""")
- st.markdown(
- """It is {} that the mean sentiment of {}'s writing is more postive relative to that of Readability of the ART Corpus.
- """.format(
- temp, author_name
- )
- )
+ #if "sentiment" in genre:
+ # sentiment = []
+ # uniqueness = []
+ # for block in trainingDats:
+ # uniqueness.append(block["uniqueness"])
+ # sentiment.append(block["sp"])
+ # temp = np.mean(sentiment) < np.mean([r["sp"] for r in ar])
+
+ # st.markdown("""### Sentiment""")
+ # st.markdown(
+ # """It is {} that the mean sentiment of {}'s writing is more postive relative to that of Readability of the ART Corpus.
+ # """.format(
+ # temp, author_name
+ # )
+ # )
- temp = "{0} positive sentiment".format(author_name)
- labels = [temp, "ART Corpus positive sentiment"]
- values = [np.mean([r["sp"] for r in ar]), np.mean(sentiment)]
+ # temp = "{0} positive sentiment".format(author_name)
+ # labels = [temp, "ART Corpus positive sentiment"]
+ # values = [np.mean([r["sp"] for r in ar]), np.mean(sentiment)]
# urlDat["reading_time"]
- fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=0.3)])
- st.write(fig)
+ # fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=0.3)])
+ # st.write(fig)
- st.markdown("\n")
+ # st.markdown("\n")
if __name__ == "__main__":
--
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