Update app.py
Browse files
app.py
CHANGED
@@ -1,5 +1,6 @@
|
|
1 |
import json
|
2 |
import requests
|
|
|
3 |
from relbert import RelBERT
|
4 |
import gradio as gr
|
5 |
|
@@ -21,6 +22,12 @@ def cosine_similarity(a, b, zero_vector_mask: float = -100):
|
|
21 |
return sum(map(lambda x: x[0] * x[1], zip(a, b)))/(norm_a * norm_b)
|
22 |
|
23 |
|
|
|
|
|
|
|
|
|
|
|
|
|
24 |
def greet(
|
25 |
query,
|
26 |
candidate_1,
|
@@ -29,7 +36,7 @@ def greet(
|
|
29 |
candidate_4,
|
30 |
candidate_5,
|
31 |
candidate_6):
|
32 |
-
query = query.split(',')
|
33 |
# validate query
|
34 |
if len(query) == 0:
|
35 |
raise ValueError(f'ERROR: query is empty {query}')
|
@@ -50,7 +57,7 @@ def greet(
|
|
50 |
]):
|
51 |
if i == '':
|
52 |
continue
|
53 |
-
candidate = i.split(',')
|
54 |
if len(candidate) == 1:
|
55 |
raise ValueError(f'ERROR: candidate {n + 1} contains single word {candidate}')
|
56 |
if len(candidate) > 2:
|
@@ -64,9 +71,6 @@ def greet(
|
|
64 |
sims = []
|
65 |
for v in vectors:
|
66 |
sims.append(cosine_similarity(v, vector_q))
|
67 |
-
# output = list(zip(pairs_id, sims, pairs))
|
68 |
-
# output = sorted(list(zip(pairs_id, sims, pairs)), key=lambda _x: _x[1], reverse=True)
|
69 |
-
# output = {f'candidate {i}: [{p[0]}, {p[1]}]': s for n, (i, s, p) in enumerate(output)}
|
70 |
output = {f'candidate {i}: [{p[0]}, {p[1]}]': s for i, s, p in zip(pairs_id, sims, pairs)}
|
71 |
return output
|
72 |
|
|
|
1 |
import json
|
2 |
import requests
|
3 |
+
import re
|
4 |
from relbert import RelBERT
|
5 |
import gradio as gr
|
6 |
|
|
|
22 |
return sum(map(lambda x: x[0] * x[1], zip(a, b)))/(norm_a * norm_b)
|
23 |
|
24 |
|
25 |
+
def clean(text):
|
26 |
+
text = re.sub(r"\A\s+", "", text)
|
27 |
+
text = re.sub(r"\s+\Z", "", text)
|
28 |
+
return text
|
29 |
+
|
30 |
+
|
31 |
def greet(
|
32 |
query,
|
33 |
candidate_1,
|
|
|
36 |
candidate_4,
|
37 |
candidate_5,
|
38 |
candidate_6):
|
39 |
+
query = [clean(i) for i in query.split(',')]
|
40 |
# validate query
|
41 |
if len(query) == 0:
|
42 |
raise ValueError(f'ERROR: query is empty {query}')
|
|
|
57 |
]):
|
58 |
if i == '':
|
59 |
continue
|
60 |
+
candidate = [clean(x) for x in i.split(',')]
|
61 |
if len(candidate) == 1:
|
62 |
raise ValueError(f'ERROR: candidate {n + 1} contains single word {candidate}')
|
63 |
if len(candidate) > 2:
|
|
|
71 |
sims = []
|
72 |
for v in vectors:
|
73 |
sims.append(cosine_similarity(v, vector_q))
|
|
|
|
|
|
|
74 |
output = {f'candidate {i}: [{p[0]}, {p[1]}]': s for i, s, p in zip(pairs_id, sims, pairs)}
|
75 |
return output
|
76 |
|