update

app.py

@@ -24,6 +24,15 @@ def greet(
         candidate_8,
         candidate_9,
         candidate_10):
+    query = query.split(',')
+    # validate query
+    if len(query) == 0:
+        raise ValueError(f'ERROR: query is empty {query}')
+    if len(query) == 1:
+        raise ValueError(f'ERROR: query contains single word {query}')
+    if len(query) > 2:
+        raise ValueError(f'ERROR: query contains more than two word {query}')
+
     pairs = []
     pairs_id = []
     for n, i in enumerate([
@@ -38,32 +47,25 @@ def greet(
         candidate_9,
         candidate_10
     ]):
-        query = query.split(',')
-        # validate query
-        if len(query) == 0:
-            raise ValueError(f'ERROR: query is empty {query}')
-        if len(query) == 1:
-            raise ValueError(f'ERROR: query contains single word {query}')
-        if len(query) > 2:
-            raise ValueError(f'ERROR: query contains more than two word {query}')
-
-        if i != '':
-            if len(i.split(',')) == 1:
-                raise ValueError(f'ERROR: candidate {n + 1} contains single word {i.split(",")}')
-            if len(i.split(',')) > 2:
-                raise ValueError(f'ERROR: candidate {n + 1} contains more than two word {i.split(",")}')
-            pairs.append(i.split(','))
-            pairs_id.append(n+1)
-        if len(pairs_id) < 2:
-            raise ValueError(f'ERROR: please specify at least two candidates: {pairs}')
-        vectors = model.get_embedding(pairs+[query])
-        vector_q = vectors.pop(-1)
-        sims = []
-        for v in vectors:
-            sims.append(cosine_similarity(v, vector_q))
-        output = sorted(list(zip(pairs_id, sims, pairs)), key=lambda _x: _x[1], reverse=True)
-        output = {f'candidate {n}: [{p[0]}, {p[1]}]': s for n, (i, s, p) in enumerate(output)}
-        return output
+        if i == '':
+            continue
+        candidate = i.split(',')
+        if len(candidate) == 1:
+            raise ValueError(f'ERROR: candidate {n + 1} contains single word {candidate}')
+        if len(candidate) > 2:
+            raise ValueError(f'ERROR: candidate {n + 1} contains more than two word {candidate}')
+        pairs.append(candidate)
+        pairs_id.append(n+1)
+    if len(pairs_id) < 2:
+        raise ValueError(f'ERROR: please specify at least two candidates: {pairs}')
+    vectors = model.get_embedding(pairs+[query])
+    vector_q = vectors.pop(-1)
+    sims = []
+    for v in vectors:
+        sims.append(cosine_similarity(v, vector_q))
+    output = sorted(list(zip(pairs_id, sims, pairs)), key=lambda _x: _x[1], reverse=True)
+    output = {f'candidate {n}: [{p[0]}, {p[1]}]': s for n, (i, s, p) in enumerate(output)}
+    return output
 
 
 demo = gr.Interface(