Update app.py

app.py

@@ -27,15 +27,15 @@ html_content = f"""
 st.markdown(html_content, unsafe_allow_html=True)
 
 # Cache models to prevent re-loading on every run
-@st.cache_resource
+#@st.cache_resource
 def load_translation_model(model_name):
     return T5ForConditionalGeneration.from_pretrained(model_name)
 
-@st.cache_resource
+#@st.cache_resource
 def load_tapex_model():
     return BartForConditionalGeneration.from_pretrained("microsoft/tapex-large-finetuned-wtq")
 
-@st.cache_resource
+#@st.cache_resource
 def load_tapex_tokenizer():
     return TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wtq")
 
@@ -111,8 +111,8 @@ def apply_prophet(df_clean):
         return pd.DataFrame()
 
     # Debugging: Check structure of df_clean
-    st.write("Estrutura do DataFrame df_clean:")
-    st.write(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()
@@ -127,8 +127,8 @@ def apply_prophet(df_clean):
         })
 
         # Debugging: Check the data passed into Prophet
-        st.write(f"Dados para Prophet - Grupo {row['Rotulo']}:")
-        st.write(data)
+        #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)
@@ -156,14 +156,16 @@ def apply_prophet(df_clean):
         anomalies = forecast[(forecast['real'] < forecast['yhat_lower']) | (forecast['real'] > forecast['yhat_upper'])]
 
         # Debugging: Check the anomalies detected
-        st.write(f"Anomalias detectadas para o grupo {row['Rotulo']}:")
-        st.write(anomalies)
+        #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']
         all_anomalies = pd.concat([all_anomalies, anomalies[['ds', 'real', 'group']]], ignore_index=True)
 
     # Return the dataframe of all anomalies
+    st.write(f"Concluída a aplicação do modelo de série tempotal")
+    st.write(all_anomalies.head())
     return all_anomalies
 
 # Initialize session states