Update app.py

app.py

@@ -1,56 +1,23 @@
 import streamlit as st
 import pandas as pd
-import re
-import matplotlib.pyplot as plt
 
-# Load your dataset
-data = pd.read_csv('results_v2.csv')
+df = pd.read_csv('last_results.csv')
 
-st.title('Previsões de Receitas')
+with st.sidebar:
+    st.markdown('Previsões de Receitas')
+    selected_instituicao = st.selectbox('Select Instituição', df['Instituição'].unique())
+    selected_conta = st.selectbox('Select Conta', df['Conta'].unique())
 
-# Add filters for 'Instituição' and 'Conta'
-selected_instituicao = st.selectbox('Selecionar Instituição', data['Instituição'].unique())
-selected_conta = st.selectbox('Selecionar Conta', data['Conta'].unique())
+# Filter the DataFrame based on selected values
+filtered_df = df[(df['Instituição'] == selected_instituicao) & (df['Conta'] == selected_conta)]
 
-def extract_and_format_numbers(input_data):
-    if isinstance(input_data, pd.Series):
-        # Convert Series object to a list of strings
-        input_data = input_data.tolist()
+# Display the filtered DataFrame
+st.write('Filtered DataFrame:')
+st.write(filtered_df)
 
-    # Join the list of strings into a single string
-    input_string = "".join(input_data)
-
-    # Extract and format numbers as monetary values
-    numbers = re.findall(r'\d+\.\d+e\+\d+|\d+\.\d+', input_string)
-    formatted_numbers = []
-
-    for num in numbers:
-        # Convert the string to a float
-        num_float = float(num)
-
-        # Format as a monetary value (assuming Brazilian Real currency)
-        monetary_value = f'R$ {num_float:.2f}'
-        formatted_numbers.append(monetary_value)
-
-    return formatted_numbers
-
-filtered_data = data[(data['Instituição'] == selected_instituicao)]
-
-# Count of each unique 'Conta' value
-conta_counts = filtered_data['Conta'].value_counts()
-
-# Set up the layout with columns
-col1, col2 = st.columns([2, 1])
-
-with col1:
-    st.write("Previsões:")
-    table_data = data[(data['Instituição'] == selected_instituicao) & (data['Conta'] == selected_conta)]
-    formatted_forecasts = extract_and_format_numbers(table_data['Forecasts'])
-    st.write(formatted_forecasts)
-
-with col2:
-    # Plotting a pie chart
-    fig, ax = plt.subplots()
-    ax.pie(conta_counts, labels=conta_counts.index, autopct='%1.1f%%')
-    ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
-    st.pyplot(fig)  # Display the pie chart in Streamlit
+# Display the Forecasts values
+if not filtered_df.empty:
+    forecasts_values = filtered_df['Forecasts'].values
+    st.write('Forecasts Values:', forecasts_values)
+else:
+    st.warning('No data available for the selected filters.')