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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| from datetime import datetime, timedelta | |
| # Set page layout to wide for dashboard tracking | |
| st.set_page_config(layout="wide", page_title="PostHog Engineering Impact Dashboard") | |
| # ------------------------------------------------------------- | |
| # 🎯 INJECTED CSS: HIDES STREAMLIT ROW-SELECTION BUTTONS COLUMN | |
| # ------------------------------------------------------------- | |
| st.html(""" | |
| <style> | |
| /* Target and completely hide the data grid's row-selection column wrapper */ | |
| div[data-testid="stDataFrame"] [class*="gdg-row-header"], | |
| div[data-testid="stDataFrame"] .glide-data-grid-row-header-container, | |
| div[data-testid="stDataFrame"] th[class*="row-header"] { | |
| display: none !important; | |
| width: 0px !important; | |
| } | |
| </style> | |
| """) | |
| # Load the data generated by fetch_data.py | |
| try: | |
| df = pd.read_csv("posthog_impact_data.csv") | |
| except FileNotFoundError: | |
| st.error("❌ Data file 'posthog_impact_data.csv' not found. Please run 'python fetch_data.py' first to collect telemetry.") | |
| st.stop() | |
| # ------------------------------------------------------------- | |
| # DYNAMIC TIMELINE DETECTOR | |
| # ------------------------------------------------------------- | |
| end_date = datetime.now() | |
| start_date = end_date - timedelta(days=90) | |
| date_string = f"🗓️ Duration: {start_date.strftime('%b %d, %Y')} – {end_date.strftime('%b %d, %Y')} (Past 90 Days)" | |
| # ------------------------------------------------------------- | |
| # SIDEBAR: CORE PILLARS PHILOSOPHY & CONTROLS | |
| # ------------------------------------------------------------- | |
| st.sidebar.title("🏛️ Impact Framework Definitions") | |
| st.sidebar.markdown(""" | |
| **📦 1. Execution:** | |
| Measures operational scope, and handling of complex features. Blends bug Fix tags, core architectural, library, infrastructure, core, critical, P0, P1 text/labels/tags matches. | |
| *** | |
| **💬 2. Collaboration:** | |
| Quantifies engineering leverage and team citizenship. Blends *Review Actions* with a *Rubber-Stamp Filter* (>15 words) to isolate meaningful mentorship. | |
| *** | |
| **🛑 3. System Quality:** | |
| Tracks production stability and defensive coding. Evaluates long-term stability by applying a deduction penalty for triggered *Git Reverts*. | |
| *** | |
| **🤝 4. Human Touch:** | |
| Captures critical qualitive values provided through direct team leadership, presence during incident escalation triage, and guidance in design/planning syncs. | |
| """) | |
| st.sidebar.markdown("---") | |
| st.sidebar.header("⚖️ Strategic Priority Weights") | |
| st.sidebar.markdown("Adjust macro priorities based on organizational needs:") | |
| # Default weights: 0.35, 0.35, 0.20, 0.10 | |
| exec_w = st.sidebar.slider("Execution Weight", 0.0, 1.0, 0.35, 0.05) | |
| collab_w = st.sidebar.slider("Collaboration Weight", 0.0, 1.0, 0.35, 0.05) | |
| quality_w = st.sidebar.slider("System Quality Weight", 0.0, 1.0, 0.20, 0.05) | |
| human_w = st.sidebar.slider("Human Touch Weight", 0.0, 1.0, 0.10, 0.05) | |
| # Defensive Zero-Weight Divide-by-Zero Guard | |
| total_weight = exec_w + collab_w + quality_w + human_w | |
| if np.isclose(total_weight, 0.0): | |
| exec_w_norm = 0.25 | |
| collab_w_norm = 0.25 | |
| quality_w_norm = 0.25 | |
| human_w_norm = 0.25 | |
| st.sidebar.info("ℹ️ All weights set to 0. Defaulting to an equal split (25% each) to prevent math errors.") | |
| else: | |
| exec_w_norm = exec_w / total_weight | |
| collab_w_norm = collab_w / total_weight | |
| quality_w_norm = quality_w / total_weight | |
| human_w_norm = human_w / total_weight | |
| # ------------------------------------------------------------- | |
| # CORE METRICS ENGINE: Peer Cohort Normalization | |
| # ------------------------------------------------------------- | |
| max_prs = df['prs_merged'].max() if df['prs_merged'].max() > 0 else 1 | |
| max_bugs = df['bug_fixes'].max() if df['bug_fixes'].max() > 0 else 1 | |
| max_mult = df['multiplier_impact'].max() if df['multiplier_impact'].max() > 0 else 1 | |
| max_actions = df['review_actions'].max() if df['review_actions'].max() > 0 else 1 | |
| max_words = df['review_words_written'].max() if df['review_words_written'].max() > 0 else 1 | |
| max_reverts = df['reverts_triggered'].max() if df['reverts_triggered'].max() > 0 else 1 | |
| # Synthesize normalized values (0.0 to 1.0) | |
| df['norm_prs'] = df['prs_merged'] / max_prs | |
| df['norm_bugs'] = df['bug_fixes'] / max_bugs | |
| df['norm_mult'] = df['multiplier_impact'] / max_mult | |
| df['norm_actions'] = df['review_actions'] / max_actions | |
| df['norm_words'] = df['review_words_written'] / max_words | |
| df['norm_reverts'] = df['reverts_triggered'] / max_reverts | |
| # Human Touch Core Mock Value Generator | |
| df['human_touch_baseline'] = 0.85 | |
| # Calculate Internal Pillar Strengths | |
| df['Execution_Pillar'] = (df['norm_prs'] * 0.4) + (df['norm_bugs'] * 0.3) + (df['norm_mult'] * 0.3) | |
| df['Collaboration_Pillar'] = (df['norm_actions'] * 0.5) + (df['norm_words'] * 0.5) | |
| df['Quality_Pillar'] = 1.0 - df['norm_reverts'] | |
| df['Human_Pillar'] = df['human_touch_baseline'] | |
| # Calculate final component contribution points | |
| df['Exec_Contribution'] = df['Execution_Pillar'] * exec_w_norm * 100 | |
| df['Collab_Contribution'] = df['Collaboration_Pillar'] * collab_w_norm * 100 | |
| df['Quality_Contribution'] = df['Quality_Pillar'] * quality_w_norm * 100 | |
| df['Human_Contribution'] = df['Human_Pillar'] * human_w_norm * 100 | |
| # Calculate Final Aggregated Impact Score | |
| df['Impact_Score'] = df['Exec_Contribution'] + df['Collab_Contribution'] + df['Quality_Contribution'] + df['Human_Contribution'] | |
| # Sort dataset by absolute overall impact | |
| df = df.sort_values(by="Impact_Score", ascending=False).reset_index(drop=True) | |
| # ------------------------------------------------------------- | |
| # MAIN DISPLAY: LEADERBOARD MATRIX WITH DIRECT ROW SELECTION | |
| # ------------------------------------------------------------- | |
| st.title("🏛️ PostHog Engineering Impact Leaderboard") | |
| st.markdown(f"**{date_string}**") | |
| st.caption("💡 Click on checkbox on the engineer's row below to instantly update their deep-dive profile.") | |
| # Dynamic row count limiter dropdown | |
| view_option = st.selectbox( | |
| "Set Leaderboard Depth Range:", | |
| options=["Top 5", "Top 10", "Top 20", "Top 30", "View All Teams"], | |
| index=0 | |
| ) | |
| if view_option == "Top 5": | |
| limit = 5 | |
| elif view_option == "Top 10": | |
| limit = 10 | |
| elif view_option == "Top 20": | |
| limit = 20 | |
| elif view_option == "Top 30": | |
| limit = 30 | |
| else: | |
| limit = len(df) | |
| # Prepare clean dataframe containing active slice data | |
| leaderboard_slice = df.head(limit).copy() | |
| # Dynamically calculate the maximum points possible per column based on weights | |
| max_exec_possible = exec_w_norm * 100 | |
| max_collab_possible = collab_w_norm * 100 | |
| max_quality_possible = quality_w_norm * 100 | |
| max_human_possible = human_w_norm * 100 | |
| # Construct display dataframe with explicit Max Point indicators in headers | |
| display_df = pd.DataFrame({ | |
| 'Engineer Username': leaderboard_slice['engineer'], | |
| '🏅 Total Impact Score (out of 100)': leaderboard_slice['Impact_Score'].round(1), | |
| f'📦 Execution (Max {max_exec_possible:.1f} pts)': leaderboard_slice['Exec_Contribution'].round(1), | |
| f'💬 Collaboration (Max {max_collab_possible:.1f} pts)': leaderboard_slice['Collab_Contribution'].round(1), | |
| f'🛑 System Quality (Max {max_quality_possible:.1f} pts)': leaderboard_slice['Quality_Contribution'].round(1), | |
| f'🤝 Human Touch (Max {max_human_possible:.1f} pts)': leaderboard_slice['Human_Contribution'].round(1) | |
| }) | |
| # Dynamically calculate optimal table height to eliminate empty rows | |
| row_height = 35 | |
| header_height = 40 | |
| calculated_height = min(header_height + (len(display_df) * row_height), 450) | |
| # Render interactive table with selection tracking active | |
| selection = st.dataframe( | |
| display_df.style.format({ | |
| '🏅 Total Impact Score (out of 100)': '{:.1f}', | |
| f'📦 Execution (Max {max_exec_possible:.1f} pts)': '{:.1f}', | |
| f'💬 Collaboration (Max {max_collab_possible:.1f} pts)': '{:.1f}', | |
| f'🛑 System Quality (Max {max_quality_possible:.1f} pts)': '{:.1f}', | |
| f'🤝 Human Touch (Max {max_human_possible:.1f} pts)': '{:.1f}' | |
| }), | |
| use_container_width=True, | |
| height=calculated_height, | |
| hide_index=True, | |
| on_select="rerun", | |
| selection_mode="single-row-required" | |
| ) | |
| # ------------------------------------------------------------- | |
| # MASTER-DETAIL VIEW: DYNAMIC METRICS AUDITOR | |
| # ------------------------------------------------------------- | |
| st.markdown("<br>", unsafe_allow_html=True) | |
| st.markdown("---") | |
| # Extract chosen engineer row natively without checking box arrays | |
| if selection and selection.get("selection", {}).get("rows"): | |
| selected_row_idx = selection["selection"]["rows"][0] | |
| eng_row = leaderboard_slice.iloc[selected_row_idx] | |
| else: | |
| # Safely fall back to the absolute top engineer on landing | |
| eng_row = df.iloc[0] | |
| selected_eng = eng_row['engineer'] | |
| # --- ADDED: DIRECT MATH PROOF OF THE MAIN MATRIX ACCURACY --- | |
| st.info( | |
| f"📊 **Formula Proof for {selected_eng}:** " | |
| f"📦 Execution (`{eng_row['Exec_Contribution']:.1f}`) + " | |
| f"💬 Collaboration (`{eng_row['Collab_Contribution']:.1f}`) + " | |
| f"🛑 Quality (`{eng_row['Quality_Contribution']:.1f}`) + " | |
| f"🤝 Human Touch (`{eng_row['Human_Contribution']:.1f}`) = " | |
| f"**🏅 Total Impact Score of {eng_row['Impact_Score']:.1f} / 100**" | |
| ) | |
| st.subheader(f"🔍 Deep-Dive Calculation Audit Engine: {selected_eng}") | |
| col1, col2 = st.columns([1, 2], gap="large") | |
| with col1: | |
| st.metric("Overall Performance Rating", f"{eng_row['Impact_Score']:.1f} / 100") | |
| st.markdown(f""" | |
| **Active Weight Allocation Matrix:** | |
| * 📦 **Execution Contribution:** `{eng_row['Exec_Contribution']:.1f}` pts | |
| * 💬 **Collaboration Contribution:** `{eng_row['Collab_Contribution']:.1f}` pts | |
| * 🛑 **System Quality Contribution:** `{eng_row['Quality_Contribution']:.1f}` pts | |
| * 🤝 **Human Touch Contribution:** `{eng_row['Human_Contribution']:.1f}` pts | |
| """) | |
| with col2: | |
| st.markdown("#### **Line-Item Pillar Math Breakdowns**") | |
| # ------------------------------------------------------------- | |
| # PILLAR 1: EXECUTION DEEP DIVE | |
| # ------------------------------------------------------------- | |
| with st.expander(f"📦 Execution Pillar Breakdown: {eng_row['Exec_Contribution']:.1f} pts", expanded=False): | |
| st.markdown("**1. Cohort Normalization (Raw vs Peak Team Ceiling):**") | |
| st.markdown(f"- Merged PRs: `{int(eng_row['prs_merged'])}` / `{int(max_prs)}` Max = **{eng_row['norm_prs']:.3f}** ratio") | |
| st.markdown(f"- Bug Fixes: `{int(eng_row['bug_fixes'])}` / `{int(max_bugs)}` Max = **{eng_row['norm_bugs']:.3f}** ratio") | |
| st.markdown(f"- **Impact Multipliers:** `{int(eng_row['multiplier_impact'])}` / `{int(max_mult)}` Max = **{eng_row['norm_mult']:.3f}** ratio") | |
| st.markdown(""" | |
| > 💡 **What is an Impact Multiplier?** \n | |
| > This tracks high-leverage architectural code contributions. It scans text logs, labels, and files across your pull requests for engineering foundations that multiply the velocity of other teams: | |
| > * 🛠️ **Infrastructure & Shared Libraries** (`lib`, `infra`, `framework`) | |
| > * ⚡ **Core System Optimization** (`core`, `performance`, `latency`) | |
| > * 🔒 **Security & High-Criticality Guards** (`critical`, `P0`, `P1`, `security`, `auth`) | |
| """) | |
| st.markdown("**2. Composite Subsystem Weight Assembly Formula:**") | |
| st.code(f""" | |
| Execution Baseline Score = (Norm_PRs * 0.4) + (Norm_Bugs * 0.3) + (Norm_Multipliers * 0.3) | |
| = ({eng_row['norm_prs']:.3f} * 0.4) + ({eng_row['norm_bugs']:.3f} * 0.3) + ({eng_row['norm_mult']:.3f} * 0.3) | |
| = {eng_row['Execution_Pillar']:.3f} | |
| """, language="text") | |
| st.markdown("**3. Priority Control Scaling:**") | |
| st.code(f""" | |
| Final Points = Baseline Score * Strategy Weight * 100 | |
| = {eng_row['Execution_Pillar']:.3f} * {exec_w_norm:.2f} * 100 | |
| = {eng_row['Exec_Contribution']:.1f} pts | |
| """, language="text") | |
| # ------------------------------------------------------------- | |
| # PILLAR 2: COLLABORATION DEEP DIVE | |
| # ------------------------------------------------------------- | |
| with st.expander(f"💬 Collaboration Pillar Breakdown: {eng_row['Collab_Contribution']:.1f} pts", expanded=False): | |
| st.markdown("**1. Cohort Normalization (Raw vs Peak Team Ceiling):**") | |
| st.markdown(f"- Review Actions Count: `{int(eng_row['review_actions'])}` / `{int(max_actions)}` Max = **{eng_row['norm_actions']:.3f}** ratio") | |
| st.markdown(f"- Substantive Mentorship Words (>15w): `{int(eng_row['review_words_written'])}` / `{int(max_words)}` Max = **{eng_row['norm_words']:.3f}** ratio") | |
| st.markdown("**2. Composite Subsystem Weight Assembly Formula:**") | |
| st.code(f""" | |
| Collaboration Baseline Score = (Norm_Actions * 0.5) + (Norm_Words * 0.5) | |
| = ({eng_row['norm_actions']:.3f} * 0.5) + ({eng_row['norm_words']:.3f} * 0.5) | |
| = {eng_row['Collaboration_Pillar']:.3f} | |
| """, language="text") | |
| st.markdown("**3. Priority Control Scaling:**") | |
| st.code(f""" | |
| Final Points = Baseline Score * Strategy Weight * 100 | |
| = {eng_row['Collaboration_Pillar']:.3f} * {collab_w_norm:.2f} * 100 | |
| = {eng_row['Collab_Contribution']:.1f} pts | |
| """, language="text") | |
| # ------------------------------------------------------------- | |
| # PILLAR 3: SYSTEM QUALITY DEEP DIVE | |
| # ------------------------------------------------------------- | |
| with st.expander(f"🛑 System Quality Pillar Breakdown: {eng_row['Quality_Contribution']:.1f} pts", expanded=False): | |
| st.markdown("**1. Cohort Normalization (Raw vs Peak Team Ceiling):**") | |
| st.markdown(f"- Git Reverts Triggered: `{int(eng_row['reverts_triggered'])}` / `{int(max_reverts)}` Max = **{eng_row['norm_reverts']:.3f}** ratio") | |
| st.markdown("**2. Composite Subsystem Weight Assembly Formula:**") | |
| st.code(f""" | |
| Quality Baseline Score = 1.0 - Norm_Reverts | |
| = 1.0 - {eng_row['norm_reverts']:.3f} | |
| = {eng_row['Quality_Pillar']:.3f} | |
| """, language="text") | |
| st.markdown("**3. Priority Control Scaling:**") | |
| st.code(f""" | |
| Final Points = Baseline Score * Strategy Weight * 100 | |
| = {eng_row['Quality_Pillar']:.3f} * {quality_w_norm:.2f} * 100 | |
| = {eng_row['Quality_Contribution']:.1f} pts | |
| """, language="text") | |
| # ------------------------------------------------------------- | |
| # PILLAR 4: HUMAN TOUCH DEEP DIVE | |
| # ------------------------------------------------------------- | |
| with st.expander(f"🤝 Human Touch Pillar Breakdown: {eng_row['Human_Contribution']:.1f} pts", expanded=False): | |
| st.markdown("**1. Qualitative Evaluation Criteria Score (Manager Inputs Matrix):**") | |
| st.markdown(f"- Current Assigned Sync/Escalation Presence Rating = **{eng_row['human_touch_baseline']:.2f}** / 1.0") | |
| st.markdown(""" | |
| > 💡 **What factors calculate the Human Touch Rating?** \n | |
| > This value tracks critical behaviors that telemetry cannot isolate from GitHub APIs alone: | |
| > * 🧠 **Planning & Brainstorming** (Active, clarifying architectural contributions during syncs) | |
| > * 🚨 **Incident Escalation Response** (Availability and speed to jumping on critical production issues) | |
| """) | |
| st.markdown("**2. Composite Assembly Score Formula:**") | |
| st.code(f""" | |
| Human Touch Baseline Score = Manager Evaluation Score | |
| = {eng_row['human_touch_baseline']:.2f} | |
| """, language="text") | |
| st.markdown("**3. Priority Control Scaling:**") | |
| st.code(f""" | |
| Final Points = Baseline Score * Strategy Weight * 100 | |
| = {eng_row['Human_Pillar']:.2f} * {human_w_norm:.2f} * 100 | |
| = {eng_row['Human_Contribution']:.1f} pts | |
| """, language="text") | |
| # ------------------------------------------------------------- | |
| # UNDER THE HOOD RAW TELEMETRY (COLLAPSED BY DEFAULT) | |
| # ------------------------------------------------------------- | |
| st.markdown("<br>", unsafe_allow_html=True) | |
| with st.expander("📊 View Underlying Raw GitHub Telemetry Metrics"): | |
| st.markdown("This section details the raw activity counts gathered before weights or normalization filters were applied.") | |
| st.dataframe( | |
| df[['engineer', 'prs_merged', 'bug_fixes', 'multiplier_impact', 'review_actions', 'review_words_written', 'reverts_triggered']], | |
| use_container_width=True, | |
| hide_index=True | |
| ) | |