Update app.py

app.py

@@ -100,47 +100,60 @@ def apply_prophet(df_clean):
         st.error("DataFrame está vazio após o pré-processamento.")
         return pd.DataFrame()
 
+    # Debugging: Check structure of df_clean
+    st.write("Estrutura do DataFrame df_clean:")
+    st.write(df_clean)
+
     # Criar um DataFrame vazio para armazenar todas as anomalias
     all_anomalies = pd.DataFrame()
+    
     # Processar cada linha no DataFrame
     for index, row in df_clean.iterrows():
+        # Extract timestamp and value columns
+        date_columns = [col for col in df_clean.columns if isinstance(col, pd.Timestamp)]
         data = pd.DataFrame({
-            'ds': [col for col in df_clean.columns if isinstance(col, pd.Timestamp)],
-            'y': row[[isinstance(col, pd.Timestamp) for col in df_clean.columns]].values
+            'ds': date_columns,
+            'y': row[date_columns].values
         })
-    
-        # Remove lines where 'y' is zero until the first non-zero value
-        data = data[data['y'] > 0].reset_index(drop=True)
+
+        # Debugging: Check the data passed into Prophet
+        st.write(f"Dados para Prophet - Grupo {row['Rotulo']}:")
+        st.write(data)
+
+        # Remove rows where 'y' is zero or missing
+        data = data[data['y'] > 0].dropna().reset_index(drop=True)
+
+        # Ensure there's enough data for Prophet to run
         if data.empty or len(data) < 2:
-            print(f"Skipping group {row['Rotulo']} because there are less than 2 non-zero observations.")
+            st.write(f"Pular grupo {row['Rotulo']} por não ter observações suficientes.")
             continue
-    
-        # Try to create and train the model
+
         try:
-            model = Prophet(interval_width=0.95)  # Confidence interval
+            # Create and fit the Prophet model
+            model = Prophet(interval_width=0.95)
             model.fit(data)
         except ValueError as e:
-            print(f"Skipping group {row['Rotulo']} due to error: {e}")
+            st.write(f"Pular grupo {row['Rotulo']} devido ao erro: {e}")
             continue
-    
+
         # Make future predictions
         future = model.make_future_dataframe(periods=12, freq='M')
         forecast = model.predict(future)
-    
-        # Identify points outside the confidence interval
-        num_real = len(data)
-        num_forecast = len(forecast)
-        real_values = list(data['y']) + [None] * (num_forecast - num_real)
+
+        # Add real values and calculate anomalies
+        real_values = list(data['y']) + [None] * (len(forecast) - len(data))
         forecast['real'] = real_values
         anomalies = forecast[(forecast['real'] < forecast['yhat_lower']) | (forecast['real'] > forecast['yhat_upper'])]
-    
-        # Add group label to the anomalies
+
+        # Debugging: Check the anomalies detected
+        st.write(f"Anomalias detectadas para o grupo {row['Rotulo']}:")
+        st.write(anomalies)
+
+        # Add group label and append anomalies to all_anomalies DataFrame
         anomalies['group'] = row['Rotulo']
-    
-        # Append anomalies to the all_anomalies DataFrame
         all_anomalies = pd.concat([all_anomalies, anomalies[['ds', 'real', 'group']]], ignore_index=True)
 
-    # Renomear colunas e aplicar filtros
+    # Return the dataframe of all anomalies
     return all_anomalies
 
 tab1, tab2 = st.tabs(["Meta Prophet", "Microsoft TAPEX"])