Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 from sentence_transformers import SentenceTransformer, util
 import numpy as np
 import pandas as pd
+import math
 import spacy
 import torch
 import plotly.express as px
@@ -63,7 +64,7 @@ def compare_text(transcript, categories, threshold):
         phrases = torch.stack(phrases_list)
         cosine_scores = util.cos_sim(embeddings, phrases).numpy()
         max_scores = np.max(cosine_scores, axis=1)
-        df_cosines[df_category.iloc[0,2]] = max_scores.round(decimals = 3)
+        df_cosines[df_category.iloc[0,2]] = math.ceil(max_scores * 1000) / 1000.0
         for num_sentence, scores in enumerate(cosine_scores):
             for num_phrase, score in enumerate(scores):
                 if score >= threshold:
@@ -72,12 +73,11 @@ def compare_text(transcript, categories, threshold):
                         'sentence': sentences[num_sentence],
                         'phrase': df_category.at[num_phrase,'example'],
                         'category': df_category.at[num_phrase,'label'],
-                        'similarity': score
+                        'similarity': math.ceil(score * 1000) / 1000.0
                     }
                     df_results = df_results.append(new_row, ignore_index=True)
 
-    # df_cosines = df_cosines.round(decimals = 3) 
-    df_results = df_results.sort_values(['line','similarity'],ascending=[True,False]).round(decimals = 3)
+    df_results = df_results.sort_values(['line','similarity'],ascending=[True,False])
 
     df_summary = pd.DataFrame(df_cosines.max(numeric_only=True),columns=['similarity'])
     df_summary['ok'] = np.where(df_summary['similarity'] > threshold, True, False)
@@ -99,8 +99,7 @@ def compare_text(transcript, categories, threshold):
     fig.update_traces(textfont_size=24, textangle=0, textposition="inside", cliponaxis=False)
     fig.update_yaxes(range=[0, 1])  
 
-    df_results = df_results.round(decimals = 3)
-    df_summary = df_summary['similarity'].round(decimals = 2)
+    df_summary = df_summary['similarity']
     
     return df_summary.to_dict(), fig, df_cosines, df_results