Update app.py

app.py

@@ -1,10 +1,13 @@
+
 import gradio as gr
 import requests
 import pandas as pd
-from difflib import get_close_matches
-import re  # Importa o módulo de expressões regulares
+import re
 import csv
 import datetime
+from rapidfuzz import process, fuzzimport
+import requests
+from difflib import get_close_matches
 from fuzzywuzzy import process
 
 bens_df = pd.read_excel('bens_tab.xlsx')
@@ -36,8 +39,8 @@ def refinar_resultados(df):
     return df_refinado
 
 def get_best_match(query, choices, limit=5):
-    matches = process.extractBests(query, choices, limit=limit)
-    return [match[0] for match in matches if match[1] > 60]  # Adjust score threshold as needed
+    matches = process.extract(query, choices, scorer=fuzz.WRatio, limit=limit)
+    return [match[0] for match in matches if match[1] > 60]
     
 def filtrar_itens_similares(df, termo_pesquisa, limit=5):
     titulos = df['Title'].tolist()
@@ -69,22 +72,25 @@ def calcular_fator_avaliacao(titulo, EC, PU):
     return fator_avaliacao
 
 def select_nearest_items(df):
-    median_price = df['Price'].median()
-    df['Distance'] = (df['Price'] - median_price).abs()
-    df_sorted = df.sort_values('Distance')
+    mode_prices = df['Price'].mode()
+    if len(mode_prices) > 0:
+        target_price = mode_prices.min()  # Choose the smallest mode if multiple
+    else:
+        target_price = df['Price'].median()  # Fallback to median if no mode
     
-    # Ensuring unique marketplaces, maintaining a list of included marketplaces
+    df['Distance'] = (df['Price'] - target_price).abs()
+    df_sorted = df.sort_values('Distance')
+
     included_marketplaces = set()
-    nearest_items = pd.DataFrame()
+    nearest_items = []
     for _, row in df_sorted.iterrows():
         if row['Marketplace'] not in included_marketplaces:
-            # Convert row to DataFrame and append correctly
-            row_df = pd.DataFrame([row])
-            nearest_items = pd.concat([nearest_items, row_df], ignore_index=True)
+            nearest_items.append(row)
             included_marketplaces.add(row['Marketplace'])
-        if len(included_marketplaces) >= 3:  # Ensure at least 3 unique marketplaces are included
+        if len(included_marketplaces) >= 3:
             break
-    return nearest_items
+
+    return pd.DataFrame(nearest_items)
 
 def integrated_app(query, titulo, EC, PU):
     df_mercadolibre = fetch_data_to_dataframe(query, 50, "mercadolibre")