+
+
+
+
""",
+ unsafe_allow_html=True,
+ )
+
+
+# def set_header():
+# logo_path = "./path/to/your/local/LIME_logo.png" # Replace with the actual file path
+# text = "LiME"
+# return st.markdown(f"""
+#
+#
{text}
+#
""", unsafe_allow_html=True)
+
+
+def s_curve(x, K, b, a, x0):
+ return K / (1 + b * np.exp(-a * (x - x0)))
+
+
+def panel_level(input_df, date_column="Date"):
+ # Ensure 'Date' is set as the index
+ if date_column not in input_df.index.names:
+ input_df = input_df.set_index(date_column)
+
+ # Select numeric columns only (excluding 'Date' since it's now the index)
+ numeric_columns_df = input_df.select_dtypes(include="number")
+
+ # Group by 'Date' (which is the index) and sum the numeric columns
+ aggregated_df = numeric_columns_df.groupby(input_df.index).sum()
+
+ # Reset index if you want 'Date' back as a column
+ aggregated_df = aggregated_df.reset_index()
+
+ return aggregated_df
+
+
+def initialize_data(
+ panel=None, target_file=r"metrics_level_data\Overview_data_test_panel@#revenue.xlsx", updated_rcs=None, metrics=None
+):
+ # uopx_conv_rates = {'streaming_impressions' : 0.007,'digital_impressions' : 0.007,'search_clicks' : 0.00719,'tv_impressions' : 0.000173,
+ # "digital_clicks":0.005,"streaming_clicks":0.004,'streaming_spends':1,"tv_spends":1,"search_spends":1,
+ # "digital_spends":1}
+ # print('State initialized')
+
+ excel = pd.read_excel(target_file, sheet_name=None)
+
+ # Extract dataframes for raw data, spend input, and contribution MMM
+ raw_df = excel["RAW DATA MMM"]
+ spend_df = excel["SPEND INPUT"]
+ contri_df = excel["CONTRIBUTION MMM"]
+
+ # Check if the panel is not None
+ if panel is not None and panel != "Total Market":
+ raw_df = raw_df[raw_df["Panel"] == panel].drop(columns=["Panel"])
+ spend_df = spend_df[spend_df["Panel"] == panel].drop(columns=["Panel"])
+ contri_df = contri_df[contri_df["Panel"] == panel].drop(columns=["Panel"])
+ elif panel == "Total Market":
+ raw_df = panel_level(raw_df, date_column="Date")
+ spend_df = panel_level(spend_df, date_column="Week")
+ contri_df = panel_level(contri_df, date_column="Date")
+
+ # Revenue_df = excel['Revenue']
+
+ ## remove sesonalities, indices etc ...
+ exclude_columns = [
+ "Date",
+ "Region",
+ "Controls_Grammarly_Index_SeasonalAVG",
+ "Controls_Quillbot_Index",
+ "Daily_Positive_Outliers",
+ "External_RemoteClass_Index",
+ "Intervals ON 20190520-20190805 | 20200518-20200803 | 20210517-20210802",
+ "Intervals ON 20190826-20191209 | 20200824-20201207 | 20210823-20211206",
+ "Intervals ON 20201005-20201019",
+ "Promotion_PercentOff",
+ "Promotion_TimeBased",
+ "Seasonality_Indicator_Chirstmas",
+ "Seasonality_Indicator_NewYears_Days",
+ "Seasonality_Indicator_Thanksgiving",
+ "Trend 20200302 / 20200803",
+ ]
+ raw_df["Date"] = pd.to_datetime(raw_df["Date"])
+ contri_df["Date"] = pd.to_datetime(contri_df["Date"])
+ input_df = raw_df.sort_values(by="Date")
+ output_df = contri_df.sort_values(by="Date")
+ spend_df["Week"] = pd.to_datetime(
+ spend_df["Week"], format="%Y-%m-%d", errors="coerce"
+ )
+ spend_df.sort_values(by="Week", inplace=True)
+
+ # spend_df['Week'] = pd.to_datetime(spend_df['Week'], errors='coerce')
+ # spend_df = spend_df.sort_values(by='Week')
+
+ channel_list = [col for col in input_df.columns if col not in exclude_columns]
+ channel_list = list(set(channel_list) - set(["fb_level_achieved_tier_1", "ga_app"]))
+
+ response_curves = {}
+ mapes = {}
+ rmses = {}
+ upper_limits = {}
+ powers = {}
+ r2 = {}
+ conv_rates = {}
+ output_cols = []
+ channels = {}
+ sales = None
+ dates = input_df.Date.values
+ actual_output_dic = {}
+ actual_input_dic = {}
+
+ for inp_col in channel_list:
+ # st.write(inp_col)
+ spends = input_df[inp_col].values
+ x = spends.copy()
+ # upper limit for penalty
+ upper_limits[inp_col] = 2 * x.max()
+
+ # contribution
+ out_col = [_col for _col in output_df.columns if _col.startswith(inp_col)][0]
+ y = output_df[out_col].values.copy()
+ actual_output_dic[inp_col] = y.copy()
+ actual_input_dic[inp_col] = x.copy()
+ ##output cols aggregation
+ output_cols.append(out_col)
+
+ ## scale the input
+ power = np.ceil(np.log(x.max()) / np.log(10)) - 3
+ if power >= 0:
+ x = x / 10**power
+
+ x = x.astype("float64")
+ y = y.astype("float64")
+ # print('#printing yyyyyyyyy')
+ # print(inp_col)
+ # print(x.max())
+ # print(y.max())
+ bounds = ((0, 0, 0, 0), (3 * y.max(), 1000, 1, x.max()))
+
+ # bounds = ((y.max(), 3*y.max()),(0,1000),(0,1),(0,x.max()))
+ params, _ = curve_fit(
+ s_curve,
+ x,
+ y,
+ p0=(2 * y.max(), 0.01, 1e-5, x.max()),
+ bounds=bounds,
+ maxfev=int(1e5),
+ )
+ mape = (100 * abs(1 - s_curve(x, *params) / y.clip(min=1))).mean()
+ rmse = np.sqrt(((y - s_curve(x, *params)) ** 2).mean())
+ r2_ = r2_score(y, s_curve(x, *params))
+
+ response_curves[inp_col] = {
+ "K": params[0],
+ "b": params[1],
+ "a": params[2],
+ "x0": params[3],
+ }
+
+ updated_rcs_key = f"{metrics}#@{panel}#@{inp_col}"
+ if updated_rcs is not None and updated_rcs_key in list(updated_rcs.keys()):
+ response_curves[inp_col] = updated_rcs[updated_rcs_key]
+
+ mapes[inp_col] = mape
+ rmses[inp_col] = rmse
+ r2[inp_col] = r2_
+ powers[inp_col] = power
+
+ ## conversion rates
+ spend_col = [
+ _col
+ for _col in spend_df.columns
+ if _col.startswith(inp_col.rsplit("_", 1)[0])
+ ][0]
+
+ # print('#printing spendssss')
+ # print(spend_col)
+ conv = (
+ spend_df.set_index("Week")[spend_col]
+ / input_df.set_index("Date")[inp_col].clip(lower=1)
+ ).reset_index()
+ conv.rename(columns={"index": "Week"}, inplace=True)
+ conv["year"] = conv.Week.dt.year
+ conv_rates[inp_col] = list(conv.drop("Week", axis=1).mean().to_dict().values())[
+ 0
+ ]
+ ##print('Before',conv_rates[inp_col])
+ # conv_rates[inp_col] = uopx_conv_rates[inp_col]
+ ##print('After',(conv_rates[inp_col]))
+
+ channel = Channel(
+ name=inp_col,
+ dates=dates,
+ spends=spends,
+ # conversion_rate = np.mean(list(conv_rates[inp_col].values())),
+ conversion_rate=conv_rates[inp_col],
+ response_curve_type="s-curve",
+ response_curve_params={
+ "K": params[0],
+ "b": params[1],
+ "a": params[2],
+ "x0": params[3],
+ },
+ bounds=np.array([-10, 10]),
+ )
+ channels[inp_col] = channel
+ if sales is None:
+ sales = channel.actual_sales
+ else:
+ sales += channel.actual_sales
+ other_contributions = (
+ output_df.drop([*output_cols], axis=1).sum(axis=1, numeric_only=True).values
+ )
+ correction = output_df.drop("Date", axis=1).sum(axis=1).values - (
+ sales + other_contributions
+ )
+ scenario = Scenario(
+ name="default",
+ channels=channels,
+ constant=other_contributions,
+ correction=correction,
+ )
+ ## setting session variables
+ st.session_state["initialized"] = True
+ st.session_state["actual_df"] = input_df
+ st.session_state["raw_df"] = raw_df
+ st.session_state["contri_df"] = output_df
+ default_scenario_dict = class_to_dict(scenario)
+ st.session_state["default_scenario_dict"] = default_scenario_dict
+ st.session_state["scenario"] = scenario
+ st.session_state["channels_list"] = channel_list
+ st.session_state["optimization_channels"] = {
+ channel_name: False for channel_name in channel_list
+ }
+ st.session_state["rcs"] = response_curves
+
+ st.session_state["powers"] = powers
+ st.session_state["actual_contribution_df"] = pd.DataFrame(actual_output_dic)
+ st.session_state["actual_input_df"] = pd.DataFrame(actual_input_dic)
+
+ for channel in channels.values():
+ st.session_state[channel.name] = numerize(
+ channel.actual_total_spends * channel.conversion_rate, 1
+ )
+
+ st.session_state["xlsx_buffer"] = io.BytesIO()
+
+ if Path("../saved_scenarios.pkl").exists():
+ with open("../saved_scenarios.pkl", "rb") as f:
+ st.session_state["saved_scenarios"] = pickle.load(f)
+ else:
+ st.session_state["saved_scenarios"] = OrderedDict()
+
+ # st.session_state["total_spends_change"] = 0
+ st.session_state["optimization_channels"] = {
+ channel_name: False for channel_name in channel_list
+ }
+ st.session_state["disable_download_button"] = True
+
+
+# def initialize_data():
+# # fetch data from excel
+# output = pd.read_excel('data.xlsx',sheet_name=None)
+# raw_df = output['RAW DATA MMM']
+# contribution_df = output['CONTRIBUTION MMM']
+# Revenue_df = output['Revenue']
+
+# ## channels to be shows
+# channel_list = []
+# for col in raw_df.columns:
+# if 'click' in col.lower() or 'spend' in col.lower() or 'imp' in col.lower():
+# ##print(col)
+# channel_list.append(col)
+# else:
+# pass
+
+# ## NOTE : Considered only Desktop spends for all calculations
+# acutal_df = raw_df[raw_df.Region == 'Desktop'].copy()
+# ## NOTE : Considered one year of data
+# acutal_df = acutal_df[acutal_df.Date>'2020-12-31']
+# actual_df = acutal_df.drop('Region',axis=1).sort_values(by='Date')[[*channel_list,'Date']]
+
+# ##load response curves
+# with open('./grammarly_response_curves.json','r') as f:
+# response_curves = json.load(f)
+
+# ## create channel dict for scenario creation
+# dates = actual_df.Date.values
+# channels = {}
+# rcs = {}
+# constant = 0.
+# for i,info_dict in enumerate(response_curves):
+# name = info_dict.get('name')
+# response_curve_type = info_dict.get('response_curve')
+# response_curve_params = info_dict.get('params')
+# rcs[name] = response_curve_params
+# if name != 'constant':
+# spends = actual_df[name].values
+# channel = Channel(name=name,dates=dates,
+# spends=spends,
+# response_curve_type=response_curve_type,
+# response_curve_params=response_curve_params,
+# bounds=np.array([-30,30]))
+
+# channels[name] = channel
+# else:
+# constant = info_dict.get('value',0.) * len(dates)
+
+# ## create scenario
+# scenario = Scenario(name='default', channels=channels, constant=constant)
+# default_scenario_dict = class_to_dict(scenario)
+
+
+# ## setting session variables
+# st.session_state['initialized'] = True
+# st.session_state['actual_df'] = actual_df
+# st.session_state['raw_df'] = raw_df
+# st.session_state['default_scenario_dict'] = default_scenario_dict
+# st.session_state['scenario'] = scenario
+# st.session_state['channels_list'] = channel_list
+# st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
+# st.session_state['rcs'] = rcs
+# for channel in channels.values():
+# if channel.name not in st.session_state:
+# st.session_state[channel.name] = float(channel.actual_total_spends)
+
+# if 'xlsx_buffer' not in st.session_state:
+# st.session_state['xlsx_buffer'] = io.BytesIO()
+
+# ## for saving scenarios
+# if 'saved_scenarios' not in st.session_state:
+# if Path('../saved_scenarios.pkl').exists():
+# with open('../saved_scenarios.pkl','rb') as f:
+# st.session_state['saved_scenarios'] = pickle.load(f)
+
+# else:
+# st.session_state['saved_scenarios'] = OrderedDict()
+
+# if 'total_spends_change' not in st.session_state:
+# st.session_state['total_spends_change'] = 0
+
+# if 'optimization_channels' not in st.session_state:
+# st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
+
+# if 'disable_download_button' not in st.session_state:
+# st.session_state['disable_download_button'] = True
+
+
+def create_channel_summary(scenario):
+
+ # Provided data
+ data = {
+ "Channel": [
+ "Paid Search",
+ "Ga will cid baixo risco",
+ "Digital tactic others",
+ "Fb la tier 1",
+ "Fb la tier 2",
+ "Paid social others",
+ "Programmatic",
+ "Kwai",
+ "Indicacao",
+ "Infleux",
+ "Influencer",
+ ],
+ "Spends": [
+ "$ 11.3K",
+ "$ 155.2K",
+ "$ 50.7K",
+ "$ 125.4K",
+ "$ 125.2K",
+ "$ 105K",
+ "$ 3.3M",
+ "$ 47.5K",
+ "$ 55.9K",
+ "$ 632.3K",
+ "$ 48.3K",
+ ],
+ "Revenue": [
+ "558.0K",
+ "3.5M",
+ "5.2M",
+ "3.1M",
+ "3.1M",
+ "2.1M",
+ "20.8M",
+ "1.6M",
+ "728.4K",
+ "22.9M",
+ "4.8M",
+ ],
+ }
+
+ # Create DataFrame
+ df = pd.DataFrame(data)
+
+ # Convert currency strings to numeric values
+ df["Spends"] = (
+ df["Spends"]
+ .replace({"\$": "", "K": "*1e3", "M": "*1e6"}, regex=True)
+ .map(pd.eval)
+ .astype(int)
+ )
+ df["Revenue"] = (
+ df["Revenue"]
+ .replace({"\$": "", "K": "*1e3", "M": "*1e6"}, regex=True)
+ .map(pd.eval)
+ .astype(int)
+ )
+
+ # Calculate ROI
+ df["ROI"] = (df["Revenue"] - df["Spends"]) / df["Spends"]
+
+ # Format columns
+ format_currency = lambda x: f"${x:,.1f}"
+ format_roi = lambda x: f"{x:.1f}"
+
+ df["Spends"] = [
+ "$ 11.3K",
+ "$ 155.2K",
+ "$ 50.7K",
+ "$ 125.4K",
+ "$ 125.2K",
+ "$ 105K",
+ "$ 3.3M",
+ "$ 47.5K",
+ "$ 55.9K",
+ "$ 632.3K",
+ "$ 48.3K",
+ ]
+ df["Revenue"] = [
+ "$ 536.3K",
+ "$ 3.4M",
+ "$ 5M",
+ "$ 3M",
+ "$ 3M",
+ "$ 2M",
+ "$ 20M",
+ "$ 1.5M",
+ "$ 7.1M",
+ "$ 22M",
+ "$ 4.6M",
+ ]
+ df["ROI"] = df["ROI"].apply(format_roi)
+
+ return df
+
+
+# @st.cache(allow_output_mutation=True)
+# def create_contribution_pie(scenario):
+# #c1f7dc
+# colors_map = {col:color for col,color in zip(st.session_state['channels_list'],plotly.colors.n_colors(plotly.colors.hex_to_rgb('#BE6468'), plotly.colors.hex_to_rgb('#E7B8B7'),23))}
+# total_contribution_fig = make_subplots(rows=1, cols=2,subplot_titles=['Spends','Revenue'],specs=[[{"type": "pie"}, {"type": "pie"}]])
+# total_contribution_fig.add_trace(
+# go.Pie(labels=[channel_name_formating(channel_name) for channel_name in st.session_state['channels_list']] + ['Non Media'],
+# values= [round(scenario.channels[channel_name].actual_total_spends * scenario.channels[channel_name].conversion_rate,1) for channel_name in st.session_state['channels_list']] + [0],
+# marker=dict(colors = [plotly.colors.label_rgb(colors_map[channel_name]) for channel_name in st.session_state['channels_list']] + ['#F0F0F0']),
+# hole=0.3),
+# row=1, col=1)
+
+# total_contribution_fig.add_trace(
+# go.Pie(labels=[channel_name_formating(channel_name) for channel_name in st.session_state['channels_list']] + ['Non Media'],
+# values= [scenario.channels[channel_name].actual_total_sales for channel_name in st.session_state['channels_list']] + [scenario.correction.sum() + scenario.constant.sum()],
+# hole=0.3),
+# row=1, col=2)
+
+# total_contribution_fig.update_traces(textposition='inside',texttemplate='%{percent:.1%}')
+# total_contribution_fig.update_layout(uniformtext_minsize=12,title='Channel contribution', uniformtext_mode='hide')
+# return total_contribution_fig
+
+# @st.cache(allow_output_mutation=True)
+
+# def create_contribuion_stacked_plot(scenario):
+# weekly_contribution_fig = make_subplots(rows=1, cols=2,subplot_titles=['Spends','Revenue'],specs=[[{"type": "bar"}, {"type": "bar"}]])
+# raw_df = st.session_state['raw_df']
+# df = raw_df.sort_values(by='Date')
+# x = df.Date
+# weekly_spends_data = []
+# weekly_sales_data = []
+# for channel_name in st.session_state['channels_list']:
+# weekly_spends_data.append((go.Bar(x=x,
+# y=scenario.channels[channel_name].actual_spends * scenario.channels[channel_name].conversion_rate,
+# name=channel_name_formating(channel_name),
+# hovertemplate="Date:%{x}
Spend:%{y:$.2s}",
+# legendgroup=channel_name)))
+# weekly_sales_data.append((go.Bar(x=x,
+# y=scenario.channels[channel_name].actual_sales,
+# name=channel_name_formating(channel_name),
+# hovertemplate="Date:%{x}
Revenue:%{y:$.2s}",
+# legendgroup=channel_name, showlegend=False)))
+# for _d in weekly_spends_data:
+# weekly_contribution_fig.add_trace(_d, row=1, col=1)
+# for _d in weekly_sales_data:
+# weekly_contribution_fig.add_trace(_d, row=1, col=2)
+# weekly_contribution_fig.add_trace(go.Bar(x=x,
+# y=scenario.constant + scenario.correction,
+# name='Non Media',
+# hovertemplate="Date:%{x}
Revenue:%{y:$.2s}"), row=1, col=2)
+# weekly_contribution_fig.update_layout(barmode='stack', title='Channel contribuion by week', xaxis_title='Date')
+# weekly_contribution_fig.update_xaxes(showgrid=False)
+# weekly_contribution_fig.update_yaxes(showgrid=False)
+# return weekly_contribution_fig
+
+# @st.cache(allow_output_mutation=True)
+# def create_channel_spends_sales_plot(channel):
+# if channel is not None:
+# x = channel.dates
+# _spends = channel.actual_spends * channel.conversion_rate
+# _sales = channel.actual_sales
+# channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
+# channel_sales_spends_fig.add_trace(go.Bar(x=x, y=_sales,marker_color='#c1f7dc',name='Revenue', hovertemplate="Date:%{x}
Revenue:%{y:$.2s}"), secondary_y = False)
+# channel_sales_spends_fig.add_trace(go.Scatter(x=x, y=_spends,line=dict(color='#005b96'),name='Spends',hovertemplate="Date:%{x}
Spend:%{y:$.2s}"), secondary_y = True)
+# channel_sales_spends_fig.update_layout(xaxis_title='Date',yaxis_title='Revenue',yaxis2_title='Spends ($)',title='Channel spends and Revenue week wise')
+# channel_sales_spends_fig.update_xaxes(showgrid=False)
+# channel_sales_spends_fig.update_yaxes(showgrid=False)
+# else:
+# raw_df = st.session_state['raw_df']
+# df = raw_df.sort_values(by='Date')
+# x = df.Date
+# scenario = class_from_dict(st.session_state['default_scenario_dict'])
+# _sales = scenario.constant + scenario.correction
+# channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
+# channel_sales_spends_fig.add_trace(go.Bar(x=x, y=_sales,marker_color='#c1f7dc',name='Revenue', hovertemplate="Date:%{x}
Revenue:%{y:$.2s}"), secondary_y = False)
+# # channel_sales_spends_fig.add_trace(go.Scatter(x=x, y=_spends,line=dict(color='#15C39A'),name='Spends',hovertemplate="Date:%{x}
Spend:%{y:$.2s}"), secondary_y = True)
+# channel_sales_spends_fig.update_layout(xaxis_title='Date',yaxis_title='Revenue',yaxis2_title='Spends ($)',title='Channel spends and Revenue week wise')
+# channel_sales_spends_fig.update_xaxes(showgrid=False)
+# channel_sales_spends_fig.update_yaxes(showgrid=False)
+# return channel_sales_spends_fig
+
+
+# Define a shared color palette
+
+
+def create_contribution_pie():
+ color_palette = [
+ "#F3F3F0",
+ "#5E7D7E",
+ "#2FA1FF",
+ "#00EDED",
+ "#00EAE4",
+ "#304550",
+ "#EDEBEB",
+ "#7FBEFD",
+ "#003059",
+ "#A2F3F3",
+ "#E1D6E2",
+ "#B6B6B6",
+ ]
+ total_contribution_fig = make_subplots(
+ rows=1,
+ cols=2,
+ subplot_titles=["Spends", "Revenue"],
+ specs=[[{"type": "pie"}, {"type": "pie"}]],
+ )
+
+ channels_list = [
+ "Paid Search",
+ "Ga will cid baixo risco",
+ "Digital tactic others",
+ "Fb la tier 1",
+ "Fb la tier 2",
+ "Paid social others",
+ "Programmatic",
+ "Kwai",
+ "Indicacao",
+ "Infleux",
+ "Influencer",
+ "Non Media",
+ ]
+
+ # Assign colors from the limited palette to channels
+ colors_map = {
+ col: color_palette[i % len(color_palette)]
+ for i, col in enumerate(channels_list)
+ }
+ colors_map["Non Media"] = color_palette[
+ 5
+ ] # Assign fixed green color for 'Non Media'
+
+ # Hardcoded values for Spends and Revenue
+ spends_values = [0.5, 3.36, 1.1, 2.7, 2.7, 2.27, 70.6, 1, 1, 13.7, 1, 0]
+ revenue_values = [1, 4, 5, 3, 3, 2, 50.8, 1.5, 0.7, 13, 0, 16]
+
+ # Add trace for Spends pie chart
+ total_contribution_fig.add_trace(
+ go.Pie(
+ labels=[channel_name for channel_name in channels_list],
+ values=spends_values,
+ marker=dict(
+ colors=[colors_map[channel_name] for channel_name in channels_list]
+ ),
+ hole=0.3,
+ ),
+ row=1,
+ col=1,
+ )
+
+ # Add trace for Revenue pie chart
+ total_contribution_fig.add_trace(
+ go.Pie(
+ labels=[channel_name for channel_name in channels_list],
+ values=revenue_values,
+ marker=dict(
+ colors=[colors_map[channel_name] for channel_name in channels_list]
+ ),
+ hole=0.3,
+ ),
+ row=1,
+ col=2,
+ )
+
+ total_contribution_fig.update_traces(
+ textposition="inside", texttemplate="%{percent:.1%}"
+ )
+ total_contribution_fig.update_layout(
+ uniformtext_minsize=12, title="Channel contribution", uniformtext_mode="hide"
+ )
+ return total_contribution_fig
+
+
+def create_contribuion_stacked_plot(scenario):
+ weekly_contribution_fig = make_subplots(
+ rows=1,
+ cols=2,
+ subplot_titles=["Spends", "Revenue"],
+ specs=[[{"type": "bar"}, {"type": "bar"}]],
+ )
+ raw_df = st.session_state["raw_df"]
+ df = raw_df.sort_values(by="Date")
+ x = df.Date
+ weekly_spends_data = []
+ weekly_sales_data = []
+
+ for i, channel_name in enumerate(st.session_state["channels_list"]):
+ color = color_palette[i % len(color_palette)]
+
+ weekly_spends_data.append(
+ go.Bar(
+ x=x,
+ y=scenario.channels[channel_name].actual_spends
+ * scenario.channels[channel_name].conversion_rate,
+ name=channel_name_formating(channel_name),
+ hovertemplate="Date:%{x}
Spend:%{y:$.2s}",
+ legendgroup=channel_name,
+ marker_color=color,
+ )
+ )
+
+ weekly_sales_data.append(
+ go.Bar(
+ x=x,
+ y=scenario.channels[channel_name].actual_sales,
+ name=channel_name_formating(channel_name),
+ hovertemplate="Date:%{x}
Revenue:%{y:$.2s}",
+ legendgroup=channel_name,
+ showlegend=False,
+ marker_color=color,
+ )
+ )
+
+ for _d in weekly_spends_data:
+ weekly_contribution_fig.add_trace(_d, row=1, col=1)
+ for _d in weekly_sales_data:
+ weekly_contribution_fig.add_trace(_d, row=1, col=2)
+
+ weekly_contribution_fig.add_trace(
+ go.Bar(
+ x=x,
+ y=scenario.constant + scenario.correction,
+ name="Non Media",
+ hovertemplate="Date:%{x}
Revenue:%{y:$.2s}",
+ marker_color=color_palette[-1],
+ ),
+ row=1,
+ col=2,
+ )
+
+ weekly_contribution_fig.update_layout(
+ barmode="stack", title="Channel contribution by week", xaxis_title="Date"
+ )
+ weekly_contribution_fig.update_xaxes(showgrid=False)
+ weekly_contribution_fig.update_yaxes(showgrid=False)
+ return weekly_contribution_fig
+
+
+def create_channel_spends_sales_plot(channel):
+ if channel is not None:
+ x = channel.dates
+ _spends = channel.actual_spends * channel.conversion_rate
+ _sales = channel.actual_sales
+ channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
+ channel_sales_spends_fig.add_trace(
+ go.Bar(
+ x=x,
+ y=_sales,
+ marker_color=color_palette[
+ 3
+ ], # You can choose a color from the palette
+ name="Revenue",
+ hovertemplate="Date:%{x}
Revenue:%{y:$.2s}",
+ ),
+ secondary_y=False,
+ )
+
+ channel_sales_spends_fig.add_trace(
+ go.Scatter(
+ x=x,
+ y=_spends,
+ line=dict(
+ color=color_palette[2]
+ ), # You can choose another color from the palette
+ name="Spends",
+ hovertemplate="Date:%{x}
Spend:%{y:$.2s}",
+ ),
+ secondary_y=True,
+ )
+
+ channel_sales_spends_fig.update_layout(
+ xaxis_title="Date",
+ yaxis_title="Revenue",
+ yaxis2_title="Spends ($)",
+ title="Channel spends and Revenue week-wise",
+ )
+ channel_sales_spends_fig.update_xaxes(showgrid=False)
+ channel_sales_spends_fig.update_yaxes(showgrid=False)
+ else:
+ raw_df = st.session_state["raw_df"]
+ df = raw_df.sort_values(by="Date")
+ x = df.Date
+ scenario = class_from_dict(st.session_state["default_scenario_dict"])
+ _sales = scenario.constant + scenario.correction
+ channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
+ channel_sales_spends_fig.add_trace(
+ go.Bar(
+ x=x,
+ y=_sales,
+ marker_color=color_palette[
+ 0
+ ], # You can choose a color from the palette
+ name="Revenue",
+ hovertemplate="Date:%{x}
Revenue:%{y:$.2s}",
+ ),
+ secondary_y=False,
+ )
+
+ channel_sales_spends_fig.update_layout(
+ xaxis_title="Date",
+ yaxis_title="Revenue",
+ yaxis2_title="Spends ($)",
+ title="Channel spends and Revenue week-wise",
+ )
+ channel_sales_spends_fig.update_xaxes(showgrid=False)
+ channel_sales_spends_fig.update_yaxes(showgrid=False)
+
+ return channel_sales_spends_fig
+
+
+def format_numbers(value, n_decimals=1, include_indicator=True):
+ if include_indicator:
+ return f"{CURRENCY_INDICATOR} {numerize(value,n_decimals)}"
+ else:
+ return f"{numerize(value,n_decimals)}"
+
+
+def decimal_formater(num_string, n_decimals=1):
+ parts = num_string.split(".")
+ if len(parts) == 1:
+ return num_string + "." + "0" * n_decimals
+ else:
+ to_be_padded = n_decimals - len(parts[-1])
+ if to_be_padded > 0:
+ return num_string + "0" * to_be_padded
+ else:
+ return num_string
+
+
+def channel_name_formating(channel_name):
+ name_mod = channel_name.replace("_", " ")
+ if name_mod.lower().endswith(" imp"):
+ name_mod = name_mod.replace("Imp", "Spend")
+ elif name_mod.lower().endswith(" clicks"):
+ name_mod = name_mod.replace("Clicks", "Spend")
+ return name_mod
+
+
+def send_email(email, message):
+ s = smtplib.SMTP("smtp.gmail.com", 587)
+ s.starttls()
+ s.login("geethu4444@gmail.com", "jgydhpfusuremcol")
+ s.sendmail("geethu4444@gmail.com", email, message)
+ s.quit()
+
+
+if __name__ == "__main__":
+ initialize_data()
diff --git a/utilities_with_panel.py b/utilities_with_panel.py
new file mode 100644
index 0000000000000000000000000000000000000000..473cc7e58ffbd6dd5bfa73cc80c9e3612ce07f88
--- /dev/null
+++ b/utilities_with_panel.py
@@ -0,0 +1,1025 @@
+from numerize.numerize import numerize
+import streamlit as st
+import pandas as pd
+import json
+from classes import Channel, Scenario
+import numpy as np
+from plotly.subplots import make_subplots
+import plotly.graph_objects as go
+from classes import class_to_dict
+from collections import OrderedDict
+import io
+import plotly
+from pathlib import Path
+import pickle
+import streamlit_authenticator as stauth
+import yaml
+from yaml import SafeLoader
+from streamlit.components.v1 import html
+import smtplib
+from scipy.optimize import curve_fit
+from sklearn.metrics import r2_score
+from classes import class_from_dict
+import os
+import base64
+
+
+
+
+color_palette = ['#001f78', '#00b5db', '#f03d14', '#fa6e0a', '#ffbf45']
+
+
+CURRENCY_INDICATOR = '€'
+
+def load_authenticator():
+ with open('config.yaml') as file:
+ config = yaml.load(file, Loader=SafeLoader)
+ st.session_state['config'] = config
+ authenticator = stauth.Authenticate(
+ config['credentials'],
+ config['cookie']['name'],
+ config['cookie']['key'],
+ config['cookie']['expiry_days'],
+ config['preauthorized']
+ )
+ st.session_state['authenticator'] = authenticator
+ return authenticator
+
+def nav_page(page_name, timeout_secs=3):
+ nav_script = """
+
+ """ % (page_name, timeout_secs)
+ html(nav_script)
+
+
+# def load_local_css(file_name):
+# with open(file_name) as f:
+# st.markdown(f'', unsafe_allow_html=True)
+
+
+# def set_header():
+# return st.markdown(f"""
+#
MMM LiME
+#
.svg )
+#
""", unsafe_allow_html=True)
+
+path = os.path.dirname(__file__)
+
+file_ = open(f"{path}/mastercard_logo.png", "rb")
+
+contents = file_.read()
+
+data_url = base64.b64encode(contents).decode("utf-8")
+
+file_.close()
+
+
+
+DATA_PATH = './data'
+
+IMAGES_PATH = './data/images_224_224'
+
+# New - Sprint 2
+if 'bin_dict' not in st.session_state:
+
+ with open("data_import.pkl", "rb") as f:
+ data = pickle.load(f)
+
+ st.session_state['bin_dict'] = data["bin_dict"]
+
+# panel_col = [col.lower().replace('.','_').replace('@','_').replace(" ", "_").replace('-', '').replace(':', '').replace("__", "_") for col in st.session_state['bin_dict']['Panel Level 1'] ] [0]# set the panel column
+
+panel_col="Panel"
+
+is_panel = True if len(panel_col)>0 else False
+
+date_col='Date'
+#is_panel = False # flag if set to true - do panel level response curves
+
+def load_local_css(file_name):
+
+ with open(file_name) as f:
+
+ st.markdown(f'', unsafe_allow_html=True)
+
+
+
+
+
+# def set_header():
+
+# return st.markdown(f"""
+
+#
H & M Recommendations
+
+#
+
+#
""", unsafe_allow_html=True)
+path1 = os.path.dirname(__file__)
+
+file_1 = open(f"{path}/ALDI_2017.png", "rb")
+
+contents1 = file_1.read()
+
+data_url1 = base64.b64encode(contents1).decode("utf-8")
+
+file_1.close()
+
+
+
+DATA_PATH1 = './data'
+
+IMAGES_PATH1 = './data/images_224_224'
+
+
+
+
+
+def set_header():
+ return st.markdown(f"""
+
+
+
+
""", unsafe_allow_html=True)
+
+# def set_header():
+# logo_path = "./path/to/your/local/LIME_logo.png" # Replace with the actual file path
+# text = "LiME"
+# return st.markdown(f"""
+#
+#
{text}
+#
""", unsafe_allow_html=True)
+
+
+def s_curve(x,K,b,a,x0):
+ return K / (1 + b * np.exp(-a*(x-x0)))
+
+
+def overview_test_data_prep_panel(X, df, spends_X, date_col, panel_col, target_col):
+ '''
+ function to create the data which is used in initialize data fn
+ X : X test with contributions
+ df : originally uploaded data (media data) which has raw vars
+ spends_X : spends of dates in X test
+ '''
+
+ # define channels
+ channels = {'paid_search': ['paid_search_impressions', 'paid_search_clicks'],
+
+ 'fb_level_achieved_tier_1': ['fb_level_achieved_tier_1_impressions'], #, 'fb:_level_achieved_-_tier_1_clicks'],
+
+ 'fb_level_achieved_tier_2': ['fb:_level_achieved_tier_2_impressions',
+ 'fb_level_achieved_tier_2_clicks'],
+
+ 'paid_social_others' : ['paid_social_others_impressions', 'paid_social_others_clicks'],
+
+ 'ga_app': ['ga_app_impressions', 'ga_app_clicks'],
+
+ 'digital_tactic_others': ['digital_tactic_others_impressions', 'digital_tactic_others_clicks'],
+
+ 'kwai': ['kwai_impressions', 'kwai_clicks'],
+
+ 'programmatic': ['programmatic_impressions', 'programmatic_clicks'],
+
+ # 'affiliates':['affiliates_clicks'],
+ #
+ # "indicacao":['indicacao_clicks'],
+ #
+ # "infleux":['infleux_clicks'],
+ #
+ # "influencer":['influencer_clicks']
+ }
+
+ channel_list = list(channels.keys())
+
+ # map transformed variable to raw variable name & channel name
+ # mapping eg : paid_search_clicks_lag_2 (transformed var) --> paid_search_clicks (raw var) --> paid_search (channel)
+ variables = {}
+ channel_and_variables = {}
+ new_variables = {}
+ new_channels_and_variables = {}
+
+ for transformed_var in [col for col in
+ X.drop(columns=[date_col, panel_col, target_col, 'pred', 'panel_effect']).columns if
+ "_contr" not in col]:
+ if len([col for col in df.columns if col in transformed_var]) == 1:
+ raw_var = [col for col in df.columns if col in transformed_var][0]
+ variables[transformed_var] = raw_var
+ channel_and_variables[raw_var] = [channel for channel, raw_vars in channels.items() if raw_var in raw_vars][
+ 0]
+ else:
+ new_variables[transformed_var] = transformed_var
+ new_channels_and_variables[transformed_var] = 'base'
+
+ # Raw DF
+ raw_X = pd.merge(X[[date_col, panel_col]], df[[date_col, panel_col] + list(variables.values())], how='left',
+ on=[date_col, panel_col])
+ assert len(raw_X) == len(X)
+
+ raw_X_cols = []
+ for i in raw_X.columns:
+ if i in channel_and_variables.keys():
+ raw_X_cols.append(channel_and_variables[i])
+ else:
+ raw_X_cols.append(i)
+ raw_X.columns = raw_X_cols
+
+ # Contribution DF
+ contr_X = X[[date_col, panel_col, 'panel_effect'] + [col for col in X.columns if
+ "_contr" in col and "sum_" not in col]].copy()
+ new_variables = [col for col in contr_X.columns if
+ "_flag" in col.lower() or "trend" in col.lower() or "sine" in col.lower()]
+ if len(new_variables) > 0:
+ contr_X['const'] = contr_X[['panel_effect'] + new_variables].sum(axis=1)
+ contr_X.drop(columns=['panel_effect'], inplace=True)
+ contr_X.drop(columns=new_variables, inplace=True)
+ else:
+ contr_X.rename(columns={'panel_effect': 'const'}, inplace=True)
+
+ new_contr_X_cols = []
+ for col in contr_X.columns:
+ col_clean = col.replace("_contr", "")
+ new_contr_X_cols.append(col_clean)
+ contr_X.columns = new_contr_X_cols
+
+ contr_X_cols = []
+ for i in contr_X.columns:
+ if i in variables.keys():
+ contr_X_cols.append(channel_and_variables[variables[i]])
+ else:
+ contr_X_cols.append(i)
+ contr_X.columns = contr_X_cols
+
+ # Spends DF
+ spends_X.columns = [col.replace("_cost", "") for col in spends_X.columns]
+
+ raw_X.rename(columns={"date": "Date"}, inplace=True)
+ contr_X.rename(columns={"date": "Date"}, inplace=True)
+ spends_X.rename(columns={'date': 'Week'}, inplace=True)
+
+ # Create excel
+ file_name = "data_test_overview_panel_#" + target_col + ".xlsx"
+ with pd.ExcelWriter(file_name) as writer:
+ raw_X.to_excel(writer, sheet_name="RAW DATA MMM", index=False)
+ contr_X.to_excel(writer, sheet_name="CONTRIBUTION MMM", index=False)
+ spends_X.to_excel(writer, sheet_name="SPEND INPUT", index=False)
+
+
+def overview_test_data_prep_nonpanel(X, df, spends_X, date_col, target_col):
+ '''
+ function to create the data which is used in initialize data fn
+ X : X test with contributions
+ df : originally uploaded data (media data) which has raw vars
+ spends_X : spends of dates in X test
+ '''
+ # define channels
+ channels = {'paid_search': ['paid_search_impressions', 'paid_search_clicks'],
+
+ 'fb_level_achieved_tier_1': ['fb_level_achieved_tier_1_impressions', 'fb_level_achieved_tier_1_clicks'],
+
+ 'fb_level_achieved_tier_2': ['fb_level_achieved_tier_2_impressions',
+ 'fb_level_achieved_tier_2_clicks'],
+
+ 'paid_social_others' : ['paid_social_others_impressions', 'paid_social_others_clicks'],
+
+ 'ga_app_will_and_cid_pequena_baixo_risco': ['ga_app_will_and_cid_pequena_baixo_risco_impressions', 'ga_app_will_and_cid_pequena_baixo_risco_clicks'],
+
+ 'digital_tactic_others': ['digital_tactic_others_impressions', 'digital_tactic_others_clicks'],
+
+ 'kwai': ['kwai_impressions', 'kwai_clicks'],
+
+ 'programmatic': ['programmatic_impressions', 'programmatic_clicks'],
+
+ 'affiliates':['affiliates_clicks', 'affiliates_impressions'],
+
+ "indicacao":['indicacao_clicks', 'indicacao_impressions'],
+
+ "infleux":['infleux_clicks', 'infleux_impressions'],
+
+ "influencer":['influencer_clicks', 'influencer_impressions']
+ }
+
+ channel_list = list(channels.keys())
+
+ # map transformed variable to raw variable name & channel name
+ # mapping eg : paid_search_clicks_lag_2 (transformed var) --> paid_search_clicks (raw var) --> paid_search (channel)
+ variables = {}
+ channel_and_variables = {}
+ new_variables = {}
+ new_channels_and_variables = {}
+
+ cols_to_del = list(set([date_col, target_col, 'pred']).intersection((set(X.columns))))
+ for transformed_var in [col for col in
+ X.drop(columns=cols_to_del).columns if
+ "_contr" not in col]: # also has 'const'
+ if len([col for col in df.columns if col in transformed_var]) == 1: # col is raw var
+ raw_var = [col for col in df.columns if col in transformed_var][0]
+ variables[transformed_var] = raw_var
+ channel_and_variables[raw_var] = [channel for channel, raw_vars in channels.items() if raw_var in raw_vars][0]
+ else: # when no corresponding raw var then base
+ new_variables[transformed_var] = transformed_var
+ new_channels_and_variables[transformed_var] = 'base'
+
+ # Raw DF
+ raw_X = pd.merge(X[[date_col]], df[[date_col] + list(variables.values())], how='left',
+ on=[date_col])
+ assert len(raw_X) == len(X)
+
+ raw_X_cols = []
+ for i in raw_X.columns:
+ if i in channel_and_variables.keys():
+ raw_X_cols.append(channel_and_variables[i])
+ else:
+ raw_X_cols.append(i)
+ raw_X.columns = raw_X_cols
+
+ # Contribution DF
+ contr_X = X[[date_col] + [col for col in X.columns if "_contr" in col and "sum_" not in col]].copy()
+ # st.write(contr_X.columns)
+ new_variables = [col for col in contr_X.columns if
+ "_flag" in col.lower() or "trend" in col.lower() or "sine" in col.lower()]
+ if len(new_variables) > 0: # if new vars are available, their contributions should be added to base (called const)
+ contr_X['const_contr'] = contr_X[['const_contr'] + new_variables].sum(axis=1)
+ contr_X.drop(columns=new_variables, inplace=True)
+
+
+ new_contr_X_cols = []
+ for col in contr_X.columns:
+ col_clean = col.replace("_contr", "")
+ new_contr_X_cols.append(col_clean)
+ contr_X.columns = new_contr_X_cols
+
+ contr_X_cols = []
+ for i in contr_X.columns:
+ if i in variables.keys():
+ contr_X_cols.append(channel_and_variables[variables[i]])
+ else:
+ contr_X_cols.append(i)
+ contr_X.columns = contr_X_cols
+
+ # Spends DF
+ spends_X.columns = [col.replace("_cost", "").replace("_spends", '').replace("_spend", "") for col in spends_X.columns]
+
+ raw_X.rename(columns={"date": "Date"}, inplace=True)
+ contr_X.rename(columns={"date": "Date"}, inplace=True)
+ spends_X.rename(columns={'date': 'Week'}, inplace=True)
+
+ # Create excel
+ file_name = "data_test_overview_panel_#" + target_col + ".xlsx"
+ with pd.ExcelWriter(file_name) as writer:
+ raw_X.to_excel(writer, sheet_name="RAW DATA MMM", index=False)
+ contr_X.to_excel(writer, sheet_name="CONTRIBUTION MMM", index=False)
+ spends_X.to_excel(writer, sheet_name="SPEND INPUT", index=False)
+
+
+def initialize_data(target_col,selected_markets):
+ # uopx_conv_rates = {'streaming_impressions' : 0.007,'digital_impressions' : 0.007,'search_clicks' : 0.00719,'tv_impressions' : 0.000173,
+ # "digital_clicks":0.005,"streaming_clicks":0.004,'streaming_spends':1,"tv_spends":1,"search_spends":1,
+ # "digital_spends":1}
+ #print('State initialized')
+ # excel = pd.read_excel("data_test_overview_panel.xlsx",sheet_name=None)
+ #excel = pd.read_excel(r"metrics_level_data\Overview_data_test_panel@#revenue.xlsx" + target_col + ".xlsx",sheet_name=None)
+
+ excel = pd.read_excel(r"metrics_level_data\Overview_data_test_panel@#revenue.xlsx",sheet_name=None)
+
+ raw_df = excel['RAW DATA MMM']
+
+ spend_df = excel['SPEND INPUT']
+ contri_df = excel['CONTRIBUTION MMM']
+
+ #st.write(raw_df)
+ if selected_markets!= "Total Market":
+
+ raw_df=raw_df[raw_df['Panel']==selected_markets]
+ spend_df=spend_df[spend_df['Panel']==selected_markets]
+ contri_df=contri_df[contri_df['Panel']==selected_markets]
+
+ else:
+ raw_df=raw_df.groupby('Date').sum().reset_index()
+ spend_df=spend_df.groupby('Week').sum().reset_index()
+ contri_df=contri_df.groupby('Date').sum().reset_index()
+ #Revenue_df = excel['Revenue']
+
+ ## remove sesonalities, indices etc ...
+ exclude_columns = ['Date', 'Week','Panel',date_col, panel_col,'Others'
+ ]
+
+ # Aggregate all 3 dfs to date level (from date-panel level)
+ raw_df[date_col]=pd.to_datetime(raw_df[date_col])
+ raw_df_aggregations = {c:'sum' for c in raw_df.columns if c not in exclude_columns}
+ raw_df = raw_df.groupby(date_col).agg(raw_df_aggregations).reset_index()
+
+ contri_df[date_col]=pd.to_datetime(contri_df[date_col])
+ contri_df_aggregations = {c:'sum' for c in contri_df.columns if c not in exclude_columns}
+ contri_df = contri_df.groupby(date_col).agg(contri_df_aggregations).reset_index()
+
+ input_df = raw_df.sort_values(by=[date_col])
+
+ output_df = contri_df.sort_values(by=[date_col])
+
+ spend_df['Week'] = pd.to_datetime(spend_df['Week'], format='%Y-%m-%d', errors='coerce')
+ spend_df_aggregations = {c: 'sum' for c in spend_df.columns if c not in exclude_columns}
+ spend_df = spend_df.groupby('Week').agg(spend_df_aggregations).reset_index()
+ # spend_df['Week'] = pd.to_datetime(spend_df['Week'], errors='coerce')
+ # spend_df = spend_df.sort_values(by='Week')
+
+
+ channel_list = [col for col in input_df.columns if col not in exclude_columns]
+
+ response_curves = {}
+ mapes = {}
+ rmses = {}
+ upper_limits = {}
+ powers = {}
+ r2 = {}
+ conv_rates = {}
+ output_cols = []
+ channels = {}
+ sales = None
+ dates = input_df.Date.values
+ actual_output_dic = {}
+ actual_input_dic = {}
+
+ # ONLY FOR TESTING
+ # channel_list=['programmatic']
+ infeasible_channels = [c for c in contri_df.select_dtypes(include=['float', 'int']).columns if contri_df[c].sum()<=0]
+ # st.write(infeasible_channels)
+ channel_list=list(set(channel_list)-set(infeasible_channels))
+
+
+ for inp_col in channel_list:
+ #st.write(inp_col)
+
+ # # New - Sprint 2
+ # if is_panel:
+ # input_df1 = input_df.groupby([date_col]).agg({inp_col:'sum'}).reset_index() # aggregate spends on date
+ # spends = input_df1[inp_col].values
+ # else :
+ # spends = input_df[inp_col].values
+ spends = spend_df[inp_col].values
+
+ x = spends.copy()
+ # upper limit for penalty
+ upper_limits[inp_col] = 2*x.max()
+
+
+
+ # contribution
+ # New - Sprint 2
+ out_col = [_col for _col in output_df.columns if _col.startswith(inp_col)][0]
+ if is_panel :
+ output_df1 = output_df.groupby([date_col]).agg({out_col:'sum'}).reset_index()
+ y = output_df1[out_col].values.copy()
+ else :
+ y = output_df[out_col].values.copy()
+
+ actual_output_dic[inp_col] = y.copy()
+ actual_input_dic[inp_col] = x.copy()
+ ##output cols aggregation
+ output_cols.append(out_col)
+
+ ## scale the input
+ power = (np.ceil(np.log(x.max()) / np.log(10) )- 3)
+ if power >= 0 :
+ x = x / 10**power
+
+
+ x = x.astype('float64')
+ y = y.astype('float64')
+ #print('#printing yyyyyyyyy')
+ #print(inp_col)
+ #print(x.max())
+ #print(y.max())
+ # st.write(y.max(),x.max())
+ print(y.max(),x.max())
+ if y.max()<=0.01:
+ if x.max()<=0.01 :
+ st.write("here-here")
+ bounds = ((0, 0, 0, 0), (3 * 0.01, 1000, 1, 0.01))
+
+ else :
+ st.write("here")
+ bounds = ((0, 0, 0, 0), (3 * 0.01, 1000, 1, 0.01))
+ else :
+ bounds = ((0, 0, 0, 0), (3 * y.max(), 1000, 1, x.max()))
+ #bounds = ((y.max(), 3*y.max()),(0,1000),(0,1),(0,x.max()))
+ params,_ = curve_fit(s_curve,x,y,p0=(2*y.max(),0.01,1e-5,x.max()),
+ bounds=bounds,
+ maxfev=int(1e5))
+ mape = (100 * abs(1 - s_curve(x, *params) / y.clip(min=1))).mean()
+ rmse = np.sqrt(((y - s_curve(x,*params))**2).mean())
+ r2_ = r2_score(y, s_curve(x,*params))
+
+ response_curves[inp_col] = {'K' : params[0], 'b' : params[1], 'a' : params[2], 'x0' : params[3]}
+ mapes[inp_col] = mape
+ rmses[inp_col] = rmse
+ r2[inp_col] = r2_
+ powers[inp_col] = power
+
+
+ ## conversion rates
+ spend_col = [_col for _col in spend_df.columns if _col.startswith(inp_col.rsplit('_',1)[0])][0]
+
+ #print('#printing spendssss')
+ #print(spend_col)
+ conv = (spend_df.set_index('Week')[spend_col] / input_df.set_index('Date')[inp_col].clip(lower=1)).reset_index()
+ conv.rename(columns={'index':'Week'},inplace=True)
+ conv['year'] = conv.Week.dt.year
+ conv_rates[inp_col] = list(conv.drop('Week',axis=1).mean().to_dict().values())[0]
+ ##print('Before',conv_rates[inp_col])
+ # conv_rates[inp_col] = uopx_conv_rates[inp_col]
+ ##print('After',(conv_rates[inp_col]))
+
+
+ channel = Channel(name=inp_col,dates=dates,
+ spends=spends,
+ # conversion_rate = np.mean(list(conv_rates[inp_col].values())),
+ conversion_rate = conv_rates[inp_col],
+ response_curve_type='s-curve',
+ response_curve_params={'K' : params[0], 'b' : params[1], 'a' : params[2], 'x0' : params[3]},
+ bounds=np.array([-10,10]))
+ channels[inp_col] = channel
+ if sales is None:
+ sales = channel.actual_sales
+ else:
+ sales += channel.actual_sales
+ # st.write(inp_col, channel.actual_sales)
+ # st.write(output_cols)
+ other_contributions = output_df.drop([*output_cols], axis=1).sum(axis=1, numeric_only = True).values
+ correction = output_df.drop(['Date'],axis=1).sum(axis=1).values - (sales + other_contributions)
+
+ scenario_test_df=pd.DataFrame(columns=['other_contributions','correction', 'sales'])
+ scenario_test_df['other_contributions']=other_contributions
+ scenario_test_df['correction']=correction
+ scenario_test_df['sales']=sales
+ scenario_test_df.to_csv("test/scenario_test_df.csv",index=False)
+ output_df.to_csv("test/output_df.csv",index=False)
+
+ scenario = Scenario(name='default', channels=channels, constant=other_contributions, correction = correction)
+ ## setting session variables
+ st.session_state['initialized'] = True
+ st.session_state['actual_df'] = input_df
+ st.session_state['raw_df'] = raw_df
+ st.session_state['contri_df'] = output_df
+ default_scenario_dict = class_to_dict(scenario)
+ st.session_state['default_scenario_dict'] = default_scenario_dict
+ st.session_state['scenario'] = scenario
+ st.session_state['channels_list'] = channel_list
+ st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
+ st.session_state['rcs'] = response_curves
+ st.session_state['powers'] = powers
+ st.session_state['actual_contribution_df'] = pd.DataFrame(actual_output_dic)
+ st.session_state['actual_input_df'] = pd.DataFrame(actual_input_dic)
+
+ for channel in channels.values():
+ st.session_state[channel.name] = numerize(channel.actual_total_spends * channel.conversion_rate,1)
+
+ st.session_state['xlsx_buffer'] = io.BytesIO()
+
+
+ if Path('../saved_scenarios.pkl').exists():
+ with open('../saved_scenarios.pkl','rb') as f:
+ st.session_state['saved_scenarios'] = pickle.load(f)
+ else:
+ st.session_state['saved_scenarios'] = OrderedDict()
+
+ st.session_state['total_spends_change'] = 0
+ st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
+ st.session_state['disable_download_button'] = True
+
+# def initialize_data():
+# # fetch data from excel
+# output = pd.read_excel('data.xlsx',sheet_name=None)
+# raw_df = output['RAW DATA MMM']
+# contribution_df = output['CONTRIBUTION MMM']
+# Revenue_df = output['Revenue']
+
+# ## channels to be shows
+# channel_list = []
+# for col in raw_df.columns:
+# if 'click' in col.lower() or 'spend' in col.lower() or 'imp' in col.lower():
+# ##print(col)
+# channel_list.append(col)
+# else:
+# pass
+
+# ## NOTE : Considered only Desktop spends for all calculations
+# acutal_df = raw_df[raw_df.Region == 'Desktop'].copy()
+# ## NOTE : Considered one year of data
+# acutal_df = acutal_df[acutal_df.Date>'2020-12-31']
+# actual_df = acutal_df.drop('Region',axis=1).sort_values(by='Date')[[*channel_list,'Date']]
+
+# ##load response curves
+# with open('./grammarly_response_curves.json','r') as f:
+# response_curves = json.load(f)
+
+# ## create channel dict for scenario creation
+# dates = actual_df.Date.values
+# channels = {}
+# rcs = {}
+# constant = 0.
+# for i,info_dict in enumerate(response_curves):
+# name = info_dict.get('name')
+# response_curve_type = info_dict.get('response_curve')
+# response_curve_params = info_dict.get('params')
+# rcs[name] = response_curve_params
+# if name != 'constant':
+# spends = actual_df[name].values
+# channel = Channel(name=name,dates=dates,
+# spends=spends,
+# response_curve_type=response_curve_type,
+# response_curve_params=response_curve_params,
+# bounds=np.array([-30,30]))
+
+# channels[name] = channel
+# else:
+# constant = info_dict.get('value',0.) * len(dates)
+
+# ## create scenario
+# scenario = Scenario(name='default', channels=channels, constant=constant)
+# default_scenario_dict = class_to_dict(scenario)
+
+
+# ## setting session variables
+# st.session_state['initialized'] = True
+# st.session_state['actual_df'] = actual_df
+# st.session_state['raw_df'] = raw_df
+# st.session_state['default_scenario_dict'] = default_scenario_dict
+# st.session_state['scenario'] = scenario
+# st.session_state['channels_list'] = channel_list
+# st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
+# st.session_state['rcs'] = rcs
+# for channel in channels.values():
+# if channel.name not in st.session_state:
+# st.session_state[channel.name] = float(channel.actual_total_spends)
+
+# if 'xlsx_buffer' not in st.session_state:
+# st.session_state['xlsx_buffer'] = io.BytesIO()
+
+# ## for saving scenarios
+# if 'saved_scenarios' not in st.session_state:
+# if Path('../saved_scenarios.pkl').exists():
+# with open('../saved_scenarios.pkl','rb') as f:
+# st.session_state['saved_scenarios'] = pickle.load(f)
+
+# else:
+# st.session_state['saved_scenarios'] = OrderedDict()
+
+# if 'total_spends_change' not in st.session_state:
+# st.session_state['total_spends_change'] = 0
+
+# if 'optimization_channels' not in st.session_state:
+# st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
+
+# if 'disable_download_button' not in st.session_state:
+# st.session_state['disable_download_button'] = True
+def create_channel_summary(scenario):
+ summary_columns = []
+
+ actual_spends_rows = []
+
+ actual_sales_rows = []
+
+ actual_roi_rows = []
+
+ for channel in scenario.channels.values():
+
+ name_mod = channel.name.replace('_', ' ')
+
+ if name_mod.lower().endswith(' imp'):
+ name_mod = name_mod.replace('Imp', ' Impressions')
+
+ print(name_mod, channel.actual_total_spends, channel.conversion_rate,
+ channel.actual_total_spends * channel.conversion_rate)
+
+ summary_columns.append(name_mod)
+
+ actual_spends_rows.append(format_numbers(float(channel.actual_total_spends * channel.conversion_rate)))
+
+ actual_sales_rows.append(format_numbers((float(channel.actual_total_sales))))
+
+ actual_roi_rows.append(decimal_formater(
+ format_numbers((channel.actual_total_sales) / (channel.actual_total_spends * channel.conversion_rate),
+ include_indicator=False, n_decimals=4), n_decimals=4))
+
+ actual_summary_df = pd.DataFrame([summary_columns, actual_spends_rows, actual_sales_rows, actual_roi_rows]).T
+
+ actual_summary_df.columns = ['Channel', 'Spends', 'Revenue', 'ROI']
+
+ actual_summary_df['Revenue'] = actual_summary_df['Revenue'].map(lambda x: str(x)[1:])
+
+ return actual_summary_df
+
+
+# def create_channel_summary(scenario):
+#
+# # Provided data
+# data = {
+# 'Channel': ['Paid Search', 'Ga will cid baixo risco', 'Digital tactic others', 'Fb la tier 1', 'Fb la tier 2', 'Paid social others', 'Programmatic', 'Kwai', 'Indicacao', 'Infleux', 'Influencer'],
+# 'Spends': ['$ 11.3K', '$ 155.2K', '$ 50.7K', '$ 125.4K', '$ 125.2K', '$ 105K', '$ 3.3M', '$ 47.5K', '$ 55.9K', '$ 632.3K', '$ 48.3K'],
+# 'Revenue': ['558.0K', '3.5M', '5.2M', '3.1M', '3.1M', '2.1M', '20.8M', '1.6M', '728.4K', '22.9M', '4.8M']
+# }
+#
+# # Create DataFrame
+# df = pd.DataFrame(data)
+#
+# # Convert currency strings to numeric values
+# df['Spends'] = df['Spends'].replace({'\$': '', 'K': '*1e3', 'M': '*1e6'}, regex=True).map(pd.eval).astype(int)
+# df['Revenue'] = df['Revenue'].replace({'\$': '', 'K': '*1e3', 'M': '*1e6'}, regex=True).map(pd.eval).astype(int)
+#
+# # Calculate ROI
+# df['ROI'] = ((df['Revenue'] - df['Spends']) / df['Spends'])
+#
+# # Format columns
+# format_currency = lambda x: f"${x:,.1f}"
+# format_roi = lambda x: f"{x:.1f}"
+#
+# df['Spends'] = ['$ 11.3K', '$ 155.2K', '$ 50.7K', '$ 125.4K', '$ 125.2K', '$ 105K', '$ 3.3M', '$ 47.5K', '$ 55.9K', '$ 632.3K', '$ 48.3K']
+# df['Revenue'] = ['$ 536.3K', '$ 3.4M', '$ 5M', '$ 3M', '$ 3M', '$ 2M', '$ 20M', '$ 1.5M', '$ 7.1M', '$ 22M', '$ 4.6M']
+# df['ROI'] = df['ROI'].apply(format_roi)
+#
+# return df
+
+
+#@st.cache_data()
+def create_contribution_pie(scenario):
+ #c1f7dc
+
+ light_blue = 'rgba(0, 31, 120, 0.7)'
+ light_orange = 'rgba(0, 181, 219, 0.7)'
+ light_green = 'rgba(240, 61, 20, 0.7)'
+ light_red = 'rgba(250, 110, 10, 0.7)'
+ light_purple = 'rgba(255, 191, 69, 0.7)'
+
+ colors_map = {col:color for col,color in zip(st.session_state['channels_list'],plotly.colors.n_colors(plotly.colors.hex_to_rgb('#BE6468'), plotly.colors.hex_to_rgb('#E7B8B7'),23))}
+ total_contribution_fig = make_subplots(rows=1, cols=2,subplot_titles=['Media Spends','Revenue Contribution'],specs=[[{"type": "pie"}, {"type": "pie"}]])
+ total_contribution_fig.add_trace(
+ go.Pie(labels=[channel_name_formating(channel_name) for channel_name in st.session_state['channels_list']] + ['Non Media'],
+ values= [round(scenario.channels[channel_name].actual_total_spends * scenario.channels[channel_name].conversion_rate,1) for channel_name in st.session_state['channels_list']] + [0],
+ marker_colors=[light_blue, light_orange, light_green, light_red, light_purple],
+ hole=0.3),
+ row=1, col=1)
+
+ total_contribution_fig.add_trace(
+ go.Pie(labels=[channel_name_formating(channel_name) for channel_name in st.session_state['channels_list']] + ['Non Media'],
+ values= [scenario.channels[channel_name].actual_total_sales for channel_name in st.session_state['channels_list']] + [scenario.correction.sum() + scenario.constant.sum()],
+ hole=0.3),
+ row=1, col=2)
+
+ total_contribution_fig.update_traces(textposition='inside',texttemplate='%{percent:.1%}')
+ total_contribution_fig.update_layout(uniformtext_minsize=12,title='', uniformtext_mode='hide')
+ return total_contribution_fig
+
+#@st.cache_data()
+
+# def create_contribuion_stacked_plot(scenario):
+# weekly_contribution_fig = make_subplots(rows=1, cols=2,subplot_titles=['Spends','Revenue'],specs=[[{"type": "bar"}, {"type": "bar"}]])
+# raw_df = st.session_state['raw_df']
+# df = raw_df.sort_values(by='Date')
+# x = df.Date
+# weekly_spends_data = []
+# weekly_sales_data = []
+# for channel_name in st.session_state['channels_list']:
+# weekly_spends_data.append((go.Bar(x=x,
+# y=scenario.channels[channel_name].actual_spends * scenario.channels[channel_name].conversion_rate,
+# name=channel_name_formating(channel_name),
+# hovertemplate="Date:%{x}
Spend:%{y:$.2s}",
+# legendgroup=channel_name)))
+# weekly_sales_data.append((go.Bar(x=x,
+# y=scenario.channels[channel_name].actual_sales,
+# name=channel_name_formating(channel_name),
+# hovertemplate="Date:%{x}
Revenue:%{y:$.2s}",
+# legendgroup=channel_name, showlegend=False)))
+# for _d in weekly_spends_data:
+# weekly_contribution_fig.add_trace(_d, row=1, col=1)
+# for _d in weekly_sales_data:
+# weekly_contribution_fig.add_trace(_d, row=1, col=2)
+# weekly_contribution_fig.add_trace(go.Bar(x=x,
+# y=scenario.constant + scenario.correction,
+# name='Non Media',
+# hovertemplate="Date:%{x}
Revenue:%{y:$.2s}"), row=1, col=2)
+# weekly_contribution_fig.update_layout(barmode='stack', title='Channel contribuion by week', xaxis_title='Date')
+# weekly_contribution_fig.update_xaxes(showgrid=False)
+# weekly_contribution_fig.update_yaxes(showgrid=False)
+# return weekly_contribution_fig
+
+# @st.cache_data(allow_output_mutation=True)
+# def create_channel_spends_sales_plot(channel):
+# if channel is not None:
+# x = channel.dates
+# _spends = channel.actual_spends * channel.conversion_rate
+# _sales = channel.actual_sales
+# channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
+# channel_sales_spends_fig.add_trace(go.Bar(x=x, y=_sales,marker_color='#c1f7dc',name='Revenue', hovertemplate="Date:%{x}
Revenue:%{y:$.2s}"), secondary_y = False)
+# channel_sales_spends_fig.add_trace(go.Scatter(x=x, y=_spends,line=dict(color='#005b96'),name='Spends',hovertemplate="Date:%{x}
Spend:%{y:$.2s}"), secondary_y = True)
+# channel_sales_spends_fig.update_layout(xaxis_title='Date',yaxis_title='Revenue',yaxis2_title='Spends ($)',title='Channel spends and Revenue week wise')
+# channel_sales_spends_fig.update_xaxes(showgrid=False)
+# channel_sales_spends_fig.update_yaxes(showgrid=False)
+# else:
+# raw_df = st.session_state['raw_df']
+# df = raw_df.sort_values(by='Date')
+# x = df.Date
+# scenario = class_from_dict(st.session_state['default_scenario_dict'])
+# _sales = scenario.constant + scenario.correction
+# channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
+# channel_sales_spends_fig.add_trace(go.Bar(x=x, y=_sales,marker_color='#c1f7dc',name='Revenue', hovertemplate="Date:%{x}
Revenue:%{y:$.2s}"), secondary_y = False)
+# # channel_sales_spends_fig.add_trace(go.Scatter(x=x, y=_spends,line=dict(color='#15C39A'),name='Spends',hovertemplate="Date:%{x}
Spend:%{y:$.2s}"), secondary_y = True)
+# channel_sales_spends_fig.update_layout(xaxis_title='Date',yaxis_title='Revenue',yaxis2_title='Spends ($)',title='Channel spends and Revenue week wise')
+# channel_sales_spends_fig.update_xaxes(showgrid=False)
+# channel_sales_spends_fig.update_yaxes(showgrid=False)
+# return channel_sales_spends_fig
+
+
+# Define a shared color palette
+
+
+# def create_contribution_pie():
+# color_palette = ['#F3F3F0', '#5E7D7E', '#2FA1FF', '#00EDED', '#00EAE4', '#304550', '#EDEBEB', '#7FBEFD', '#003059', '#A2F3F3', '#E1D6E2', '#B6B6B6']
+# total_contribution_fig = make_subplots(rows=1, cols=2, subplot_titles=['Spends', 'Revenue'], specs=[[{"type": "pie"}, {"type": "pie"}]])
+#
+# channels_list = ['Paid Search', 'Ga will cid baixo risco', 'Digital tactic others', 'Fb la tier 1', 'Fb la tier 2', 'Paid social others', 'Programmatic', 'Kwai', 'Indicacao', 'Infleux', 'Influencer', 'Non Media']
+#
+# # Assign colors from the limited palette to channels
+# colors_map = {col: color_palette[i % len(color_palette)] for i, col in enumerate(channels_list)}
+# colors_map['Non Media'] = color_palette[5] # Assign fixed green color for 'Non Media'
+#
+# # Hardcoded values for Spends and Revenue
+# spends_values = [0.5, 3.36, 1.1, 2.7, 2.7, 2.27, 70.6, 1, 1, 13.7, 1, 0]
+# revenue_values = [1, 4, 5, 3, 3, 2, 50.8, 1.5, 0.7, 13, 0, 16]
+#
+# # Add trace for Spends pie chart
+# total_contribution_fig.add_trace(
+# go.Pie(
+# labels=[channel_name for channel_name in channels_list],
+# values=spends_values,
+# marker=dict(colors=[colors_map[channel_name] for channel_name in channels_list]),
+# hole=0.3
+# ),
+# row=1, col=1
+# )
+#
+# # Add trace for Revenue pie chart
+# total_contribution_fig.add_trace(
+# go.Pie(
+# labels=[channel_name for channel_name in channels_list],
+# values=revenue_values,
+# marker=dict(colors=[colors_map[channel_name] for channel_name in channels_list]),
+# hole=0.3
+# ),
+# row=1, col=2
+# )
+#
+# total_contribution_fig.update_traces(textposition='inside', texttemplate='%{percent:.1%}')
+# total_contribution_fig.update_layout(uniformtext_minsize=12, title='Channel contribution', uniformtext_mode='hide')
+# return total_contribution_fig
+
+def create_contribuion_stacked_plot(scenario):
+ weekly_contribution_fig = make_subplots(rows=1, cols=2, subplot_titles=['Spends', 'Revenue'], specs=[[{"type": "bar"}, {"type": "bar"}]])
+ raw_df = st.session_state['raw_df']
+ df = raw_df.sort_values(by='Date')
+ x = df.Date
+ weekly_spends_data = []
+ weekly_sales_data = []
+
+ for i, channel_name in enumerate(st.session_state['channels_list']):
+ color = color_palette[i % len(color_palette)]
+
+ weekly_spends_data.append(go.Bar(
+ x=x,
+ y=scenario.channels[channel_name].actual_spends * scenario.channels[channel_name].conversion_rate,
+ name=channel_name_formating(channel_name),
+ hovertemplate="Date:%{x}
Spend:%{y:$.2s}",
+ legendgroup=channel_name,
+ marker_color=color,
+ ))
+
+ weekly_sales_data.append(go.Bar(
+ x=x,
+ y=scenario.channels[channel_name].actual_sales,
+ name=channel_name_formating(channel_name),
+ hovertemplate="Date:%{x}
Revenue:%{y:$.2s}",
+ legendgroup=channel_name,
+ showlegend=False,
+ marker_color=color,
+ ))
+
+ for _d in weekly_spends_data:
+ weekly_contribution_fig.add_trace(_d, row=1, col=1)
+ for _d in weekly_sales_data:
+ weekly_contribution_fig.add_trace(_d, row=1, col=2)
+
+ weekly_contribution_fig.add_trace(go.Bar(
+ x=x,
+ y=scenario.constant + scenario.correction,
+ name='Non Media',
+ hovertemplate="Date:%{x}
Revenue:%{y:$.2s}",
+ marker_color=color_palette[-1],
+ ), row=1, col=2)
+
+ weekly_contribution_fig.update_layout(barmode='stack', title='Channel contribution by week', xaxis_title='Date')
+ weekly_contribution_fig.update_xaxes(showgrid=False)
+ weekly_contribution_fig.update_yaxes(showgrid=False)
+ return weekly_contribution_fig
+
+def create_channel_spends_sales_plot(channel):
+ if channel is not None:
+ x = channel.dates
+ _spends = channel.actual_spends * channel.conversion_rate
+ _sales = channel.actual_sales
+ channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
+ channel_sales_spends_fig.add_trace(go.Bar(
+ x=x,
+ y=_sales,
+ marker_color=color_palette[1], # You can choose a color from the palette
+ name='Revenue',
+ hovertemplate="Date:%{x}
Revenue:%{y:$.2s}",
+ ), secondary_y=False)
+
+ channel_sales_spends_fig.add_trace(go.Scatter(
+ x=x,
+ y=_spends,
+ line=dict(color=color_palette[3]), # You can choose another color from the palette
+ name='Spends',
+ hovertemplate="Date:%{x}
Spend:%{y:$.2s}",
+ ), secondary_y=True)
+
+ channel_sales_spends_fig.update_layout(xaxis_title='Date', yaxis_title='Revenue', yaxis2_title='Spends ($)', title='Channel spends and Revenue week-wise')
+ channel_sales_spends_fig.update_xaxes(showgrid=False)
+ channel_sales_spends_fig.update_yaxes(showgrid=False)
+ else:
+ raw_df = st.session_state['raw_df']
+ df = raw_df.sort_values(by='Date')
+ x = df.Date
+ scenario = class_from_dict(st.session_state['default_scenario_dict'])
+ _sales = scenario.constant + scenario.correction
+ channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
+ channel_sales_spends_fig.add_trace(go.Bar(
+ x=x,
+ y=_sales,
+ marker_color=color_palette[0], # You can choose a color from the palette
+ name='Revenue',
+ hovertemplate="Date:%{x}
Revenue:%{y:$.2s}",
+ ), secondary_y=False)
+
+ channel_sales_spends_fig.update_layout(xaxis_title='Date', yaxis_title='Revenue', yaxis2_title='Spends ($)', title='Channel spends and Revenue week-wise')
+ channel_sales_spends_fig.update_xaxes(showgrid=False)
+ channel_sales_spends_fig.update_yaxes(showgrid=False)
+
+ return channel_sales_spends_fig
+
+def format_numbers(value, n_decimals=1,include_indicator = True):
+ if include_indicator:
+ return f'{CURRENCY_INDICATOR} {numerize(value,n_decimals)}'
+ else:
+ return f'{numerize(value,n_decimals)}'
+
+
+def decimal_formater(num_string,n_decimals=1):
+ parts = num_string.split('.')
+ if len(parts) == 1:
+ return num_string+'.' + '0'*n_decimals
+ else:
+ to_be_padded = n_decimals - len(parts[-1])
+ if to_be_padded > 0 :
+ return num_string+'0'*to_be_padded
+ else:
+ return num_string
+
+
+def channel_name_formating(channel_name):
+ name_mod = channel_name.replace('_', ' ')
+ if name_mod.lower().endswith(' imp'):
+ name_mod = name_mod.replace('Imp','Spend')
+ elif name_mod.lower().endswith(' clicks'):
+ name_mod = name_mod.replace('Clicks','Spend')
+ return name_mod
+
+
+def send_email(email,message):
+ s = smtplib.SMTP('smtp.gmail.com', 587)
+ s.starttls()
+ s.login("geethu4444@gmail.com", "jgydhpfusuremcol")
+ s.sendmail("geethu4444@gmail.com", email, message)
+ s.quit()
+
+if __name__ == "__main__":
+ initialize_data()
diff --git a/willbank.png b/willbank.png
new file mode 100644
index 0000000000000000000000000000000000000000..a8d64e778aa30096a15d1300f212e1c76dfcab30
Binary files /dev/null and b/willbank.png differ