Update app.py

app.py

@@ -34,12 +34,12 @@ def refinar_resultados(df, exclude_word="conjunto", include_word=False):
     
     return df_refinado
 
-def get_best_match(query, choices, limit=15):
+def get_best_match(query, choices, limit=50):
     # Using RapidFuzz for improved performance and fuzzy matching
     matches = process.extract(query, choices, scorer=fuzz.WRatio, limit=limit)
     return [match[0] for match in matches if match[1] > 50]
     
-def filtrar_itens_similares(df, termo_pesquisa, limit=15):
+def filtrar_itens_similares(df, termo_pesquisa, limit=50):
     titulos = df['Title'].tolist()
     titulos_similares = get_best_match(termo_pesquisa, titulos, limit=limit)
     df_filtrado = df[df['Title'].isin(titulos_similares)]
@@ -78,17 +78,17 @@ def select_nearest_items(df, query):
     results = []
 
     for _, row in df_sorted.iterrows():
-        if row['Marketplace'] not in marketplaces_selected and len(marketplaces_selected) < 3:
+        if row['Marketplace'] not in marketplaces_selected and len(marketplaces_selected) < 5:
             results.append(row)
             marketplaces_selected.add(row['Marketplace'])
 
-        if len(results) >= 3:
+        if len(results) >= 5:
             break
 
     return pd.DataFrame(results)
 
 
-def search_with_fallback(query, df, limit=15):
+def search_with_fallback(query, df, limit=50):
     query_parts = query.split()
     include_conjunto = "conjunto" in query.lower()
     
@@ -104,7 +104,9 @@ def search_with_fallback(query, df, limit=15):
 
 def integrated_app(query, titulo, EC, PU):
     df_mercadolibre = fetch_data_to_dataframe(query, 50, "mercadolibre")
+    print(df_mercadolibre)
     df_combined = pd.concat([df_mercadolibre, data_crawler], ignore_index=True)
+    print(df_combined)
 
     if df_combined.empty:
         return "Nenhum dado encontrado. Tente uma consulta diferente.", pd.DataFrame()