UI fix
Browse files- app.py +6 -0
- appStore/target.py +5 -3
app.py
CHANGED
@@ -41,6 +41,12 @@ with st.expander("ℹ️ - About this app", expanded=False):
|
|
41 |
- Step 3: The paragraphs which are detected containing some target \
|
42 |
related information are then fed to multiple classifier to enrich the
|
43 |
Information Extraction.
|
|
|
|
|
|
|
|
|
|
|
|
|
44 |
|
45 |
""")
|
46 |
st.write("")
|
|
|
41 |
- Step 3: The paragraphs which are detected containing some target \
|
42 |
related information are then fed to multiple classifier to enrich the
|
43 |
Information Extraction.
|
44 |
+
Classifers:
|
45 |
+
- **Netzero**: Detects if any Netzero commitment is prresent in paragraph or not.
|
46 |
+
- **GHG**: Detects if any GHG related information present in paragraph or not.
|
47 |
+
- **Sector**: Detects which sectors are spoken/dsicussed about in paragraph.
|
48 |
+
- **Adaptation-Mitigation**: Detects if the pragraph is related to Adaptation and/or Mitigation.
|
49 |
+
|
50 |
|
51 |
""")
|
52 |
st.write("")
|
appStore/target.py
CHANGED
@@ -119,19 +119,21 @@ def target_display():
|
|
119 |
with c2:
|
120 |
st.write('**GHG Related Paragraphs**: `{}`'.format(count_ghg))
|
121 |
st.write('**Economy-wide Related Paragraphs**: `{}`'.format(count_economy))
|
122 |
-
|
123 |
hits = hits.sort_values(by=['Relevancy'], ascending=False)
|
124 |
netzerohit = hits[hits['Netzero Label'] == 'NETZERO']
|
125 |
if not netzerohit.empty:
|
126 |
netzerohit = netzerohit.sort_values(by = ['Netzero Score'], ascending = False)
|
127 |
-
st.
|
128 |
-
st.
|
|
|
129 |
netzerohit.iloc[0]['text'].replace("\n", " ")))
|
130 |
st.write("")
|
131 |
else:
|
132 |
st.info("🤔 No Netzero paragraph found")
|
133 |
|
134 |
# st.write("**Result {}** `page {}` (Relevancy Score: {:.2f})'".format(i+1,hits.iloc[i]['page'],hits.iloc[i]['Relevancy'])")
|
|
|
135 |
st.markdown("###### Top few Target Classified paragraph/text results ######")
|
136 |
range_val = min(5,len(hits))
|
137 |
for i in range(range_val):
|
|
|
119 |
with c2:
|
120 |
st.write('**GHG Related Paragraphs**: `{}`'.format(count_ghg))
|
121 |
st.write('**Economy-wide Related Paragraphs**: `{}`'.format(count_economy))
|
122 |
+
st.write('-------------------')
|
123 |
hits = hits.sort_values(by=['Relevancy'], ascending=False)
|
124 |
netzerohit = hits[hits['Netzero Label'] == 'NETZERO']
|
125 |
if not netzerohit.empty:
|
126 |
netzerohit = netzerohit.sort_values(by = ['Netzero Score'], ascending = False)
|
127 |
+
# st.write('-------------------')
|
128 |
+
# st.markdown("###### Netzero paragraph ######")
|
129 |
+
st.write('** Netzero paragraph `page {}`: {}'.format(netzerohit.iloc[0]['page'],
|
130 |
netzerohit.iloc[0]['text'].replace("\n", " ")))
|
131 |
st.write("")
|
132 |
else:
|
133 |
st.info("🤔 No Netzero paragraph found")
|
134 |
|
135 |
# st.write("**Result {}** `page {}` (Relevancy Score: {:.2f})'".format(i+1,hits.iloc[i]['page'],hits.iloc[i]['Relevancy'])")
|
136 |
+
st.write('-------------------')
|
137 |
st.markdown("###### Top few Target Classified paragraph/text results ######")
|
138 |
range_val = min(5,len(hits))
|
139 |
for i in range(range_val):
|