Update app.py

app.py

@@ -43,7 +43,7 @@ def refinar_resultados(df, include_word=[]):
 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] > 80]
+    return [match[0] for match in matches if match[1] > 75]
     
 def match_query_words_in_titles(query, title):
     """
@@ -51,7 +51,7 @@ def match_query_words_in_titles(query, title):
     Returns True if all words match to a certain degree; False otherwise.
     """
     query_words = query.lower().split()
-    match_threshold = 80  # Adjust this threshold as needed
+    match_threshold = 75  # Adjust this threshold as needed
 
     for word in query_words:
         # Find the best match for each word in the query within the title
@@ -88,7 +88,7 @@ def calcular_fator_avaliacao(titulo, EC, PU):
 def select_nearest_items(df, query):
     # Lower the title similarity threshold if necessary
     df['Title_Similarity'] = df['Title'].apply(lambda x: fuzz.WRatio(query, x))
-    df_filtered = df[df['Title_Similarity'] > 80]  # Adjusted threshold
+    df_filtered = df[df['Title_Similarity'] > 75]  # Adjusted threshold
 
     # Calculate mode price in a more inclusive manner
     mode_price = df_filtered['Price'].mode()
@@ -156,6 +156,9 @@ def integrated_app(query, titulo, EC, PU, selected_rows):
         selected_indices = [int(idx) for idx in selected_rows.split(',') if idx.isdigit()]
         df_nearest = df_nearest.iloc[selected_indices]
 
+    df_nearest.reset_index(drop=True, inplace=True)
+    df_nearest['ID'] = df_nearest.index
+
     fator_avaliacao = calcular_fator_avaliacao(titulo, EC, PU)
     valor_avaliacao = df_nearest['Price'].mean() * fator_avaliacao
     return f"Valor Médio do Bem: R$ {df_nearest['Price'].mean():.2f}, Fator de Avaliação: {fator_avaliacao*100:.2f}%, Valor de Avaliação: R$ {valor_avaliacao:.2f}", df_nearest