import locale import gradio as gr import matplotlib.pyplot as plt import numpy as np import numpy_financial as npf import pandas as pd import plotly.graph_objects as go import seaborn as sns from pandas.tseries.offsets import DateOffset, MonthEnd from scipy import optimize from portfolio import calculate_portfolio_returns from utils import get_all_mf_schemes_df,get_mf_scheme_data js_func = """ function refresh() { const url = new URL(window.location); if (url.searchParams.get('__theme') !== 'dark') { url.searchParams.set('__theme', 'dark'); window.location.href = url.href; } } """ locale.setlocale(locale.LC_MONETARY, 'en_IN') def get_portfolio_report(*args): period = args[0] custom_start_date = args[1] custom_end_date = args[2] SIP_Date = args[3] sip_amount = args[4] lumpsum_amount = args[5] stepup = args[6] schemes_df = args[7] # Extract scheme names and weights, into a dictionary from the args scheme_name_and_weight = {} for i in range(8, len(args) - 1, 2): if args[i] and args[i+1]: scheme_name_and_weight[args[i]] = float(args[i+1]) use_inception_date = args[-1] if not scheme_name_and_weight: return "Please add at least one scheme.", None, None, None end_date = pd.Timestamp.now().floor('D') if use_inception_date: start_date = pd.Timestamp(custom_start_date) elif period == "Custom": if not custom_start_date or not custom_end_date: return "Please provide both start and end dates for custom period.", None, None, None start_date = pd.Timestamp(custom_start_date) end_date = pd.Timestamp(custom_end_date) elif period == "YTD": start_date = pd.Timestamp(f"{end_date.year}-01-01") elif not period: return "Please select a period, provide custom dates, or use the inception date.", None, None, None else: period_parts = period.split() if len(period_parts) < 2: return "Invalid period selected.", None, None, None if 'year' in period_parts[1]: years = int(period_parts[0]) start_date = end_date - DateOffset(years=years) else: months = int(period_parts[0]) start_date = end_date - DateOffset(months=months) portfolio_report_string = calculate_portfolio_returns(scheme_name_and_weight, sip_amount, lumpsum_amount, stepup, start_date, end_date, SIP_Date, schemes_df) return portfolio_report_string def quick_search_schemes(query, schemes_df): if not query: return [] matching_schemes = schemes_df[schemes_df['schemeName'].str.contains(query, case=False, na=False)] return matching_schemes['schemeName'].tolist()[:40] def update_scheme_dropdown(query, schemes_df, key_up_data: gr.KeyUpData): schemes = quick_search_schemes(key_up_data.input_value, schemes_df) return gr.update(choices=schemes, visible=True) def update_schemes_list(schemes_list, updated_data): new_schemes_list = [] for _, row in updated_data.iterrows(): scheme_name = row.get('Scheme Name') weight = row.get('Weight (%)') action = row.get('Actions') if scheme_name and weight is not None and action != '🗑️': # Only keep rows that aren't marked for deletion try: weight_float = float(weight) new_schemes_list.append((scheme_name, weight_float)) except ValueError: # If weight is not a valid float, skip this row continue return new_schemes_list def update_schemes_table(schemes_list): df = pd.DataFrame(schemes_list, columns=["Scheme Name", "Weight (%)"]) df["Actions"] = "❌" # Calculate the sum of weights total_weight = df["Weight (%)"].sum() # Add a row for the total total_row = pd.DataFrame({ "Scheme Name": ["Total"], "Weight (%)": [total_weight], "Actions": [""] }) # Concatenate the original dataframe with the total row df = pd.concat([df, total_row], ignore_index=True) # Add a warning if total weight exceeds 100% if total_weight > 100: df.loc[df.index[-1], "Actions"] = "⚠️ Exceeds 100%" return df def add_scheme_to_list(schemes_list, scheme_name, weight): if scheme_name and weight: new_list = schemes_list + [(scheme_name, float(weight))] return new_list, update_schemes_table(new_list), None, 0 return schemes_list, update_schemes_table(schemes_list), scheme_name, weight def update_schemes(schemes_list, updated_data): try: new_schemes_list = [] for _, row in updated_data.iterrows(): scheme_name = row.get('Scheme Name') weight = row.get('Weight (%)') if scheme_name != 'Total' and weight is not None: try: weight_float = float(weight) new_schemes_list.append((scheme_name, weight_float)) except ValueError: continue if not new_schemes_list: return schemes_list, update_schemes_table(schemes_list), "No valid schemes found in the table." return new_schemes_list, update_schemes_table(new_schemes_list), None except Exception as e: error_msg = f"Error updating schemes: {str(e)}" return schemes_list, update_schemes_table(schemes_list), error_msg def prepare_inputs(period, custom_start, custom_end, SIP_Date, sip_amount, schemes_list, schemes_df): inputs = [period, custom_start, custom_end, SIP_Date, sip_amount, schemes_df] for name, weight in schemes_list: inputs.extend([name, weight]) return inputs def handle_row_selection(schemes_list, evt: gr.SelectData, table_data): if evt.index is not None and len(evt.index) > 1: column_index = evt.index[1] if column_index == 2: # "Actions" column row_index = evt.index[0] if row_index < len(table_data) - 1: # Ensure we're not trying to delete the total row # Remove the row table_data = table_data.drop(row_index).reset_index(drop=True) # Update the schemes_list updated_schemes_list = [(row['Scheme Name'], row['Weight (%)']) for _, row in table_data.iterrows() if row['Scheme Name'] != 'Total'] # Recalculate the total return update_schemes_table(updated_schemes_list), updated_schemes_list return table_data, schemes_list def create_ui(): schemes_df = get_all_mf_schemes_df() with gr.Blocks(js=js_func) as demo: gr.Markdown("# Mutual Fund SIP Returns Calculator") with gr.Row(): period = gr.Dropdown(choices=["YTD", "1 month","3 months","6 months","1 year", "3 years", "5 years", "7 years", "10 years","15 years","20 years", "Custom"], label="Select Period",value="YTD") custom_start_date = gr.Textbox(label="Custom Start Date (YYYY-MM-DD)", visible=False) custom_end_date = gr.Textbox(label="Custom End Date (YYYY-MM-DD)", visible=False) SIP_Date = gr.Dropdown(label="Monthly SIP Date", choices=["start","middle","end"],value="start") with gr.Column(): use_inception_date = gr.Checkbox(label="Use Earliest Inception Date", value=False) inception_date_display = gr.Textbox(label="Earliest Inception Date", interactive=False) with gr.Row(): sip_amount = gr.Number(label="SIP Amount (₹)") upfront_amount = gr.Number(label="Upfront Investment (₹)",value=0) stepup = gr.Number(label="Stepup %",value=0) schemes_list = gr.State([]) with gr.Row(): scheme_dropdown = gr.Dropdown(label="Select Scheme", choices=[], allow_custom_value=True, interactive=True) scheme_weight = gr.Slider(minimum=0, maximum=100, step=1, label="Scheme Weight (%)") add_button = gr.Button("Add Scheme") schemes_table = gr.Dataframe( headers=["Scheme Name", "Weight (%)", "Actions"], datatype=["str", "number", "str"], col_count=(3, "fixed"), label="Added Schemes", type="pandas", interactive=True ) update_button = gr.Button("Update Schemes") error_message = gr.Textbox(label="Error", visible=False) calculate_button = gr.Button("Calculate Returns") result = gr.Textbox(label="Results",) # pie_chart = gr.Plot(label="Scheme Weightages") # final_value = gr.Number(label="Final Value (₹)", interactive=False) # total_investment = gr.Number(label="Total Investment (₹)", interactive=False) def update_custom_date_visibility(period): return {custom_start_date: gr.update(visible=period=="Custom"), custom_end_date: gr.update(visible=period=="Custom")} period.change(update_custom_date_visibility, inputs=[period], outputs=[custom_start_date, custom_end_date]) scheme_dropdown.key_up( fn=update_scheme_dropdown, inputs=[scheme_dropdown, gr.State(schemes_df)], outputs=scheme_dropdown, queue=False, show_progress="hidden" ) add_button.click(add_scheme_to_list, inputs=[schemes_list, scheme_dropdown, scheme_weight], outputs=[schemes_list, schemes_table, scheme_dropdown, scheme_weight]) def update_schemes_and_show_error(schemes_list, updated_data): new_schemes_list, updated_table, error = update_schemes(schemes_list, updated_data) return ( new_schemes_list, updated_table, gr.update(value=error, visible=bool(error)) ) update_button.click( update_schemes_and_show_error, inputs=[schemes_list, schemes_table], outputs=[schemes_list, schemes_table, error_message] ) schemes_table.select( handle_row_selection, inputs=[schemes_list, schemes_table], outputs=[schemes_table, schemes_list] ) def get_earliest_inception_date(schemes_list, schemes_df): inception_dates = [] for scheme_name, _ in schemes_list: scheme_code = schemes_df[schemes_df['schemeName'] == scheme_name]['schemeCode'].values[0] _, inception_date = get_mf_scheme_data(scheme_code) inception_dates.append(inception_date) return max(inception_dates).strftime("%Y-%m-%d") if inception_dates else "" def update_inception_date(use_inception_date, schemes_list, schemes_df): if use_inception_date and schemes_list: earliest_inception_date = get_earliest_inception_date(schemes_list, schemes_df) return gr.update(value=earliest_inception_date, visible=True) else: return gr.update(value="", visible=False) use_inception_date.change( update_inception_date, inputs=[use_inception_date, schemes_list, gr.State(schemes_df)], outputs=inception_date_display ) def prepare_inputs_with_inception(period, custom_start, custom_end, SIP_Date, sip_amount, upfront_amount,stepup, schemes_list, schemes_df, use_inception_date, inception_date_display): inputs = [period, custom_start, custom_end, SIP_Date, sip_amount, upfront_amount, stepup, schemes_df] for name, weight in schemes_list: inputs.extend([name, weight]) inputs.append(use_inception_date) # Add use_inception_date to the inputs if use_inception_date and inception_date_display: inputs[1] = inception_date_display # Replace custom_start with inception_date_display return inputs calculate_button.click( lambda *args: get_portfolio_report(*prepare_inputs_with_inception(*args)), inputs=[period, custom_start_date, custom_end_date, SIP_Date, sip_amount,upfront_amount,stepup,schemes_list, gr.State(schemes_df), use_inception_date, inception_date_display], outputs=[result] # outputs=[result, final_value, total_investment] # outputs=[result, pie_chart, final_value, total_investment] ) return demo demo = create_ui() demo.launch(debug=True)