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import gradio as gr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
from rapidfuzz import fuzz
#######################################
# Debug Logging Function
#######################################
def debug_print(message):
print(message)
#######################################
# Data Generation Functions
#######################################
def generate_case_data(num_records=5000):
debug_print("Generating case data...")
lob_list = np.random.choice(["Modern Life", "Xbox", "CAPS", "Devices", "Modern Work"], num_records)
issue_types = np.random.choice(["Billing", "Technical", "Hacking", "Service", "Access"], num_records)
advocates = np.random.choice(["Alice", "Bob", "Charlie", "Diana", "Eve"], num_records)
start_date = pd.Timestamp("2021-01-01")
end_date = pd.Timestamp("2023-12-31")
days_range = (end_date - start_date).days
# Generate random case dates over 3 years
case_dates = start_date + pd.to_timedelta(np.random.randint(0, days_range, num_records), unit='D')
# Simulated release dates per LOB (set in early 2022)
lob_release_dates = {
"Modern Life": pd.Timestamp("2022-01-01"),
"Xbox": pd.Timestamp("2022-02-01"),
"CAPS": pd.Timestamp("2022-03-01"),
"Devices": pd.Timestamp("2022-04-01"),
"Modern Work": pd.Timestamp("2022-05-01")
}
release_dates = np.array([lob_release_dates[lob] for lob in lob_list])
pre_release = case_dates < release_dates
CSAT = np.where(pre_release, np.random.normal(80, 5, num_records), np.random.normal(85, 5, num_records))
days_to_close = np.where(pre_release, np.random.normal(5, 1, num_records), np.random.normal(4, 1, num_records))
first_contact_resolution = np.where(pre_release, np.random.normal(70, 8, num_records), np.random.normal(75, 8, num_records))
CPI = np.where(pre_release, np.random.normal(50, 5, num_records), np.random.normal(45, 5, num_records))
# For the main case dataset, we do NOT include initiative utilization columns.
debug_print("Case data generated.")
return pd.DataFrame({
"serial_number": np.arange(1, num_records + 1),
"advocate": advocates,
"LOB": lob_list,
"issue_type": issue_types,
"case_date": case_dates,
"CSAT": CSAT,
"days_to_close": days_to_close,
"first_contact_resolution": first_contact_resolution,
"CPI": CPI
})
def generate_advocate_adoption_data():
debug_print("Generating advocate adoption data...")
advocates = ["Alice", "Bob", "Charlie", "Diana", "Eve"]
# Ensure adoption dates fall roughly in mid-2022
adoption_dates = {
"Symbiosis_adoption_date": ["2022-06-05", "2022-06-10", "2022-06-08", "2022-06-12", "2022-06-07"],
"Voice Translation_adoption_date": ["2022-06-03", "2022-06-07", "2022-06-05", "2022-06-09", "2022-06-04"],
"NoteHero_adoption_date": ["2022-06-02", "2022-06-06", "2022-06-04", "2022-06-08", "2022-06-03"]
}
df = pd.DataFrame({
"advocate": advocates,
"Symbiosis_adoption_date": pd.to_datetime(adoption_dates["Symbiosis_adoption_date"]),
"Voice Translation_adoption_date": pd.to_datetime(adoption_dates["Voice Translation_adoption_date"]),
"NoteHero_adoption_date": pd.to_datetime(adoption_dates["NoteHero_adoption_date"])
})
# Convert to date objects
for col in ["Symbiosis_adoption_date", "Voice Translation_adoption_date", "NoteHero_adoption_date"]:
df[col] = df[col].dt.date
debug_print("Advocate adoption data generated.")
return df
def generate_utilization_data():
debug_print("Generating initiative utilization data...")
# Create a standalone DataFrame with serial numbers and binary flags for each initiative.
df = global_case_data.copy()[["serial_number"]].copy()
# For demonstration, we simulate 50% usage for each initiative.
for initiative in ["Voice Translation_utilized", "Symbiosis_utilized", "NoteHero_utilized"]:
df[initiative] = np.random.choice([0, 1], size=len(df), p=[0.5, 0.5])
debug_print("Initiative utilization data generated.")
return df
#######################################
# Global Data Setup
#######################################
global_case_data = generate_case_data(num_records=5000)
global_advocate_adoption = generate_advocate_adoption_data()
global_initiative_utilization = generate_utilization_data()
# Ensure that the utilization dataset only contains serial numbers that are in the main dataset.
valid_serials = set(global_case_data["serial_number"])
global_initiative_utilization = global_initiative_utilization[global_initiative_utilization["serial_number"].isin(valid_serials)]
debug_print("Global datasets generated.")
#######################################
# Helper Calculation Functions
#######################################
def calculate_throughput(df, start_date, end_date):
df_filtered = df.loc[(df["case_date"] >= start_date) & (df["case_date"] <= end_date)]
num_cases = len(df_filtered)
num_days = (end_date - start_date).days or 1
return num_cases / num_days if num_cases > 0 else 0
def calculate_throughput_per_advocate(df, start_date, end_date):
df_filtered = df.loc[(df["case_date"] >= start_date) & (df["case_date"] <= end_date)]
if df_filtered.empty:
return None
throughput = df_filtered.groupby(["LOB", "advocate"]).size() / (end_date - start_date).days
return throughput
#######################################
# Analysis Functions
#######################################
def analyze_overall_impact(release_date_str, lob_filter, issue_filter, kpi, one_tailed):
debug_print("Running Overall Impact Analysis...")
try:
# Parse release date as a date object (no .dt on a scalar)
release_date = pd.to_datetime(release_date_str).date()
except Exception as e:
return f"Error parsing release date: {str(e)}", None
df = global_case_data.copy()
df["case_date"] = pd.to_datetime(df["case_date"]).dt.date
if lob_filter != "All":
df = df[df["LOB"] == lob_filter]
if issue_filter != "All":
df = df[df["issue_type"] == issue_filter]
if df.empty:
return "No data available for the selected filters.", None
pre_data = df[df["case_date"] < release_date]
post_data = df[df["case_date"] >= release_date]
if pre_data.empty or post_data.empty:
return "No data available for the selected date range.", None
if kpi.lower() == "throughput":
throughput_pre = calculate_throughput(pre_data, pre_data["case_date"].min(), pre_data["case_date"].max())
throughput_post = calculate_throughput(post_data, post_data["case_date"].min(), post_data["case_date"].max())
t_stat, p_value = stats.ttest_ind(np.array([throughput_pre]), np.array([throughput_post]), equal_var=False)
else:
pre_vals, post_vals = pre_data[kpi].values, post_data[kpi].values
t_stat, p_value = stats.ttest_ind(pre_vals, post_vals, equal_var=False)
if one_tailed:
p_value = p_value / 2
significance = "Significant" if p_value < 0.05 and t_stat > 0 else "Not Significant"
else:
significance = "Significant" if p_value < 0.05 else "Not Significant"
analysis_text = f"""Overall Impact Analysis for KPI: {kpi}
Filters - LOB: {lob_filter}, Issue Type: {issue_filter}
Global Release Date: {release_date}
T-Test: T-Statistic = {t_stat:.3f}, P-Value = {p_value:.3f} ({significance})
"""
# Here you could also add additional aggregated results if needed.
fig, ax = plt.subplots(figsize=(6, 4))
if kpi.lower() == "throughput":
# For throughput, show a simple bar graph with aggregated throughput (for demonstration)
ax.bar(["Pre", "Post"], [throughput_pre, throughput_post], color=["blue", "green"])
ax.set_ylabel("Throughput (cases/day)")
else:
ax.boxplot([pre_data[kpi].values, post_data[kpi].values], labels=["Pre", "Post"])
ax.set_ylabel(kpi)
ax.set_title("Overall Impact Analysis")
plt.tight_layout()
plt.close(fig)
return analysis_text, fig
def analyze_all_advocates_impact(method, initiative, lob_filter, issue_filter, kpi, one_tailed,
adoption_file, adoption_name_col, adoption_date_col, utilization_file):
try:
debug_print("π Running Advocate Impact Analysis...")
df = global_case_data.copy()
if lob_filter != "All":
df = df[df["LOB"] == lob_filter]
if issue_filter != "All":
df = df[df["issue_type"] == issue_filter]
if df.empty:
debug_print("β No cases available for the selected filters.")
return "No data available for the selected filters.", None, None
df["case_date"] = pd.to_datetime(df["case_date"], utc=True, errors="coerce").dt.normalize().dt.date
debug_print(f"β
Data filtered. {len(df)} cases remain.")
debug_print(f"π Min case date: {df['case_date'].min()}, Max case date: {df['case_date'].max()}")
# For Initiative Utilization, use standalone DF
utilization_df = global_initiative_utilization.copy()
if method == "Initiative Utilization" and utilization_file is not None:
try:
util_df = pd.read_csv(utilization_file.name)
except Exception:
try:
util_df = pd.read_excel(utilization_file.name)
except Exception as e:
debug_print(f"β Error reading utilization file: {str(e)}")
return f"Error reading utilization file: {str(e)}", None, None
if "serial_number" not in util_df.columns:
debug_print("β The uploaded utilization file must have a 'serial_number' column.")
return "The uploaded utilization file must have a 'serial_number' column.", None, None
utilization_df = util_df.copy()
debug_print(f"β
Uploaded initiative utilization file processed: {utilization_df.shape[0]} rows.")
else:
debug_print("π No initiative utilization file uploaded; using default global initiative utilization data.")
# Build adoption mapping for Adoption Date method
adoption_mapping = {}
if method == "Adoption Date" and adoption_file is not None:
try:
uploaded_df = pd.read_csv(adoption_file.name)
except Exception:
try:
uploaded_df = pd.read_excel(adoption_file.name)
except Exception as e:
debug_print(f"β Error reading adoption file: {str(e)}")
return f"Error reading adoption file: {str(e)}", None, None
if adoption_name_col not in uploaded_df.columns or adoption_date_col not in uploaded_df.columns:
debug_print("β Specified columns not found in the uploaded adoption file.")
return "Specified columns not found in the uploaded adoption file.", None, None
debug_print("π Processing uploaded adoption file...")
for idx, row in uploaded_df.iterrows():
name_uploaded = str(row[adoption_name_col])
adoption_date = pd.to_datetime(row[adoption_date_col], utc=True, errors="coerce")
if pd.isnull(adoption_date):
debug_print(f"β Skipping invalid adoption date for {name_uploaded}")
continue
adoption_date = adoption_date.date()
# Map using fuzzy matching on the default global adoption names
for adv in df["advocate"].unique():
score = fuzz.ratio(name_uploaded.lower(), adv.lower())
if score >= 95:
adoption_mapping[adv] = min(adoption_mapping.get(adv, adoption_date), adoption_date)
debug_print(f"β
Uploaded adoption file processed. Mapped {len(adoption_mapping)} advocates.")
else:
debug_print("π No adoption file uploaded; using default global adoption data.")
# Normalize global adoption dates
for col in ["Symbiosis_adoption_date", "Voice Translation_adoption_date", "NoteHero_adoption_date"]:
global_advocate_adoption[col] = pd.to_datetime(global_advocate_adoption[col], utc=True, errors="coerce")
global_advocate_adoption[col] = global_advocate_adoption[col].apply(lambda x: x.date() if pd.notnull(x) else None)
all_pre_vals, all_post_vals = [], []
results = []
debug_print("π Processing advocates...")
for adv in df["advocate"].unique():
try:
df_adv = df[df["advocate"] == adv]
if method == "Adoption Date":
if adv in adoption_mapping:
adoption_date = adoption_mapping[adv]
else:
col_name = initiative + "_adoption_date"
adoption_series = global_advocate_adoption.loc[global_advocate_adoption["advocate"] == adv, col_name]
if adoption_series.empty or pd.isnull(adoption_series.values[0]):
debug_print(f"β Skipping {adv}: No valid adoption date found.")
continue
adoption_date = adoption_series.values[0]
if pd.isnull(adoption_date):
debug_print(f"β Skipping {adv}: Adoption date is NULL after conversion.")
continue
debug_print(f"π Processing {adv}: Adoption Date = {adoption_date}")
pre_data = df_adv[df_adv["case_date"] < adoption_date]
post_data = df_adv[df_adv["case_date"] >= adoption_date]
debug_print(f" {adv}: Pre-data count = {len(pre_data)}, Post-data count = {len(post_data)}")
if pre_data.empty:
debug_print(f"β Skipping {adv}: No pre-adoption cases.")
continue
if post_data.empty:
debug_print(f"β Skipping {adv}: No post-adoption cases.")
continue
slice_info = f"Adoption Date: {adoption_date}"
elif method == "Initiative Utilization":
col_name = initiative + "_utilized"
df_adv = df_adv.copy()
df_adv = df_adv.merge(utilization_df[["serial_number", col_name]], on="serial_number", how="left")
df_adv[col_name] = df_adv[col_name].fillna(0)
pre_data = df_adv[df_adv[col_name] == 0]
post_data = df_adv[df_adv[col_name] == 1]
slice_info = "Initiative Utilization"
else:
continue
if pre_data.empty or post_data.empty:
debug_print(f"β Advocate {adv}: Not enough data; skipping.")
continue
if kpi.lower() == "throughput":
pre_val = calculate_throughput(pre_data, pre_data["case_date"].min(), pre_data["case_date"].max())
post_val = calculate_throughput(post_data, post_data["case_date"].min(), post_data["case_date"].max())
else:
pre_val = np.mean(pre_data[kpi].values)
post_val = np.mean(post_data[kpi].values)
pct_change = ((post_val - pre_val) / pre_val) * 100 if pre_val else np.nan
results.append({
"advocate": adv,
"Pre_Mean": pre_val,
"Post_Mean": post_val,
"Percent_Change": pct_change,
"Slice_Info": slice_info
})
all_pre_vals.extend(pre_data[kpi].values)
all_post_vals.extend(post_data[kpi].values)
debug_print(f"β
Processed {adv}: {pct_change:.2f}% change.")
except Exception as e:
debug_print(f"β Error processing {adv}: {str(e)}")
if not results:
debug_print("β No valid advocates found for analysis.")
return "No valid advocates found for analysis. Check the case date ranges.", None, None
results_df = pd.DataFrame(results).sort_values(by="Percent_Change", ascending=False)
# Perform aggregated T-Test
try:
if len(all_pre_vals) > 1 and len(all_post_vals) > 1:
t_stat, p_value = stats.ttest_ind(all_pre_vals, all_post_vals, equal_var=False)
if one_tailed:
p_value = p_value / 2
significance = "Statistically Significant" if p_value < 0.05 and t_stat > 0 else "Not Statistically Significant"
else:
significance = "Statistically Significant" if p_value < 0.05 else "Not Statistically Significant"
else:
t_stat, p_value = np.nan, np.nan
significance = "Insufficient Data for Statistical Test"
except Exception as e:
debug_print(f"β Error performing T-Test: {str(e)}")
return f"Error performing T-Test: {str(e)}", None, None
pre_mean = np.mean(all_pre_vals) if len(all_pre_vals) > 0 else np.nan
post_mean = np.mean(all_post_vals) if len(all_post_vals) > 0 else np.nan
overall_pct_change = ((post_mean - pre_mean) / pre_mean) * 100 if pre_mean else np.nan
overall_summary = f"""π Aggregated Advocate Impact Analysis using method '{method}' for initiative '{initiative}' on KPI '{kpi}'.
Number of advocates analyzed: {len(results_df)}
Aggregated Pre vs Post Analysis:
- Pre-Adoption Mean: {pre_mean:.2f}
- Post-Adoption Mean: {post_mean:.2f}
- Percent Change: {overall_pct_change:.2f}%
T-Test Results:
- T-Statistic: {t_stat:.3f}
- P-Value: {p_value:.3f}
- Result: {significance}
"""
fig, ax = plt.subplots(figsize=(6, 4))
ax.bar(["Pre-Adoption", "Post-Adoption"], [pre_mean, post_mean], color=["blue", "green"])
ax.set_title(f"Aggregated Impact of {initiative} on {kpi}")
ax.set_ylabel(kpi)
plt.tight_layout()
plt.close(fig)
debug_print("π― Advocate Impact Analysis completed.")
return overall_summary, fig, results_df
except Exception as e:
debug_print(f"β Fatal Error in Function: {str(e)}")
return f"Fatal Error: {str(e)}", None, None
with gr.Blocks() as demo:
gr.Markdown("# Impact Analysis Dashboard")
with gr.Tabs():
# Tab 1: Overall Impact Analysis
with gr.TabItem("Overall Impact Analysis"):
gr.Markdown("### Overall Impact Analysis (Global Release Date)")
overall_release_date = gr.Textbox(label="Global Release Date (YYYY-MM-DD)", placeholder="e.g., 2022-01-15")
overall_lob = gr.Dropdown(choices=["All", "Modern Life", "Xbox", "CAPS", "Devices", "Modern Work"],
label="Filter by LOB", value="All")
overall_issue = gr.Dropdown(choices=["All", "Billing", "Technical", "Hacking", "Service", "Access"],
label="Filter by Issue Type", value="All")
overall_kpi = gr.Dropdown(choices=["CSAT", "days_to_close", "first_contact_resolution", "CPI", "throughput"],
label="Select KPI", value="CSAT")
one_tailed_overall = gr.Checkbox(label="Use One-Tailed T-Test")
overall_btn = gr.Button("Analyze Overall Impact")
overall_output = gr.Textbox(label="Overall Impact Analysis Results")
overall_plot = gr.Plot(label="Overall Impact Graph")
overall_btn.click(analyze_overall_impact,
inputs=[overall_release_date, overall_lob, overall_issue, overall_kpi, one_tailed_overall],
outputs=[overall_output, overall_plot])
# Tab 2: Advocate Impact Analysis
with gr.TabItem("Advocate Impact Analysis"):
gr.Markdown("### Advocate Impact Analysis (Aggregated Pre vs Post)")
adoption_method = gr.Radio(choices=["Adoption Date", "Initiative Utilization"],
label="Method", value="Adoption Date")
initiative_select = gr.Dropdown(choices=["Symbiosis", "Voice Translation", "NoteHero"],
label="Select Initiative", value="Symbiosis")
adv_lob = gr.Dropdown(choices=["All", "Modern Life", "Xbox", "CAPS", "Devices", "Modern Work"],
label="Filter by LOB", value="All")
adv_issue = gr.Dropdown(choices=["All", "Billing", "Technical", "Hacking", "Service", "Access"],
label="Filter by Issue Type", value="All")
adv_kpi = gr.Dropdown(choices=["CSAT", "days_to_close", "first_contact_resolution", "CPI", "throughput"],
label="Select KPI", value="CSAT")
one_tailed_adv = gr.Checkbox(label="Use One-Tailed T-Test")
with gr.Accordion("Optional File Uploads (Click to expand)", open=False):
gr.Markdown("Upload is optional. For Adoption Date method, upload a CSV/Excel with two columns (Advocate Name and Adoption Date). For Initiative Utilization, upload a CSV/Excel with a 'serial_number' column.")
adoption_file = gr.File(label="Upload Adoption Date File (optional)")
adoption_name_col = gr.Textbox(label="Adoption File: Advocate Name Column", placeholder="e.g., Name")
adoption_date_col = gr.Textbox(label="Adoption File: Adoption Date Column", placeholder="e.g., AdoptionDate")
utilization_file = gr.File(label="Upload Initiative Utilization File (optional)")
adv_btn = gr.Button("Analyze Advocate Impact")
adv_overall_output = gr.Textbox(label="Aggregated Advocate Impact Summary")
adv_plot = gr.Plot(label="Aggregated Advocate Impact Graph")
adv_table = gr.Dataframe(label="Advocate Impact Details")
adv_btn.click(analyze_all_advocates_impact,
inputs=[adoption_method, initiative_select, adv_lob, adv_issue, adv_kpi, one_tailed_adv,
adoption_file, adoption_name_col, adoption_date_col, utilization_file],
outputs=[adv_overall_output, adv_plot, adv_table])
# Optional Debug Logs Tab
with gr.TabItem("Debug Logs"):
gr.Markdown("### Debug Logs")
debug_btn = gr.Button("Refresh Debug Logs")
debug_output = gr.Textbox(label="Debug Logs", lines=15)
debug_btn.click(lambda: "Check console output for debug logs.", inputs=[], outputs=[debug_output])
demo.launch()
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