docs: add clear end-to-end README, update app.py and requirements, remove temp lock file

README.md

@@ -1,3 +1,237 @@
-# HIFFI Top 500 Hip-Hop Artist Intelligence
+# HIFFI Hip-Hop 500 — Artist Intelligence Engine
 
-Automated data pipeline for multi-source verification and composite metric ranking.
+An end-to-end data pipeline and interactive analytics dashboard designed to collect, validate, analyze, and rank 500 hip-hop artists based on their digital performance.
+
+The system combines artist identity verification, performance metrics, weighted ranking formulas, and breakout talent analysis to support reliable, data-driven artist discovery.
+
+## 📌 Project Overview
+
+Music industry datasets can contain incorrect artist links, unofficial fan accounts, missing metrics, and temporary viral spikes that distort rankings.
+
+The HIFFI Hip-Hop 500 Intelligence Engine addresses these challenges through four key capabilities:
+
+1. **Artist Identity Verification** — Cross-references artist information using sources such as MusicBrainz and Wikidata to help identify incorrect account matches.
+2. **Channel Health Classification** — Categorizes artist channels as `ACTIVE_HEALTHY`, `CATALOG_EVERGREEN`, or `DORMANT_CATALOG` based on defined activity and performance criteria.
+3. **Multi-Factor Ranking** — Calculates a composite HIFFI score using four weighted factors: recent momentum, view velocity, audience reach, and consistency.
+4. **Breakout Artist Discovery** — Uses a Breakout Velocity Index (BVI) to highlight emerging artists with strong growth potential.
+5. **Interactive Analytics Dashboard** — Provides rankings, performance visualizations, artist-level insights, filtering, and CSV/Excel exports through Streamlit.
+
+## ✨ Key Features
+
+- **Data Cleaning and Validation:** Identifies duplicate records, missing values, inconsistent data types, and potential outliers.
+- **Artist Identity Matching:** Uses available artist metadata and external knowledge sources to support identity verification.
+- **YouTube Metrics Collection:** Integrates YouTube Data API v3 to retrieve supported channel statistics and video information.
+- **Historical Performance Tracking:** Uses SQLite to store metric snapshots and support growth and velocity calculations.
+- **Weighted Ranking Model:** Combines four performance indicators into a composite score.
+- **Channel Health Assessment:** Distinguishes active channels, evergreen catalogs, and potentially dormant channels.
+- **Breakout Velocity Analysis:** Helps identify artists gaining attention relative to their existing audience size.
+- **Interactive Dashboard:** Displays key performance indicators, leaderboards, charts, filters, and artist details.
+- **Exportable Reports:** Generates enriched artist datasets and identity verification audit reports.
+
+## 📊 HIFFI Ranking Methodology
+
+The HIFFI composite score uses four weighted performance factors:
+
+| Ranking Factor | Weight | Description |
+|---|---:|---|
+| Recent Momentum | 35% | Measures recent audience or performance growth. |
+| View Velocity | 30% | Measures how quickly an artist's content gains views. |
+| Audience Reach | 20% | Represents the size of the artist's audience. |
+| Consistency | 15% | Measures performance stability over time. |
+| **Total** | **100%** | |
+
+The factors are normalized to a common scale before being combined into the raw score.
+
+### Confidence-Adjusted Score
+
+The system can adjust the raw score according to the confidence of the artist identity match:
+
+`Final Score = Raw Score × (0.80 + 0.20 × Confidence Score)`
+
+The confidence score ranges from `0.00` to `1.00`.
+
+This adjustment reduces the influence of uncertain identity matches. Additional eligibility rules may be applied to prevent unverified accounts from entering the official leaderboard.
+
+### Breakout Velocity Index (BVI)
+
+The BVI provides a separate perspective on artist performance, focusing on growth and view velocity relative to the artist's existing audience size.
+
+It complements the overall HIFFI ranking by helping surface emerging artists who may not yet have a large established audience.
+
+*Note: The exact BVI calculation depends on the implementation in the scoring module.*
+
+## 🏗️ System Architecture
+
+The pipeline follows this workflow:
+
+```text
+Artist Source Data / Soundcharts Export
+                  |
+                  v
+        Data Cleaning & Validation
+                  |
+                  v
+       Artist Identity Verification
+        (MusicBrainz / Wikidata)
+                  |
+                  v
+       YouTube Metrics Collection
+                  |
+                  v
+       Historical Snapshot Storage
+                (SQLite)
+                  |
+                  v
+        Scoring & Ranking Engine
+          |                 |
+          v                 v
+   HIFFI Composite       Breakout Velocity
+       Score                 Index
+          |                 |
+          +--------+--------+
+                   |
+                   v
+          Enriched Artist Dataset
+                   |
+                   v
+        Streamlit Analytics Dashboard
+                   |
+                   v
+        CSV / Excel Report Exports
+```
+
+## 📁 Repository Structure
+
+```text
+hiffi-hip-hop-500/
+├── collectors/                     # Data collection modules
+├── verifiers/                      # Artist identity verification
+├── core/                           # Data processing and scoring logic
+├── data/                           # Input datasets and reference data
+├── app.py                          # Streamlit analytics dashboard
+├── main.py                         # Pipeline orchestration
+├── enriched_artists.csv            # Enriched artist metrics and rankings
+├── identity_verification_audit.xlsx # Identity audit and review flags
+├── requirements.txt                # Python dependencies
+└── README.md                       # Project documentation
+```
+
+*The structure above describes the expected project layout. File names and generated artifacts should match the actual repository.*
+
+## 📦 Key Deliverables
+
+| Deliverable | Purpose |
+|---|---|
+| `enriched_artists.csv` | Stores enriched artist records, performance metrics, scores, rankings, and health classifications. |
+| `identity_verification_audit.xlsx` | Records identity matching results, confidence scores, verification flags, and review requirements. |
+| `app.py` | Provides the interactive Streamlit dashboard for exploring artist performance. |
+| SQLite database | Stores historical metric snapshots used for time-based performance analysis. |
+
+## 🛠️ Technology Stack
+
+- **Language:** Python
+- **Data Processing:** pandas, NumPy
+- **Data Sources:** Soundcharts exports and YouTube Data API v3
+- **Identity Verification:** MusicBrainz and Wikidata, where available
+- **Database:** SQLite
+- **Dashboard:** Streamlit
+- **Visualizations:** Plotly
+- **Exports:** CSV and Excel
+
+## 🚀 Getting Started
+
+### 1. Clone the Repository
+
+```bash
+git clone <repository-url>
+cd hiffi-hip-hop-500
+```
+
+Replace `<repository-url>` with the actual repository URL.
+
+### 2. Create a Virtual Environment
+
+```bash
+python -m venv .venv
+```
+
+Activate it:
+
+**Windows**
+```bash
+.venv\Scripts\activate
+```
+
+**macOS / Linux**
+```bash
+source .venv/bin/activate
+```
+
+### 3. Install Dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+### 4. Configure API Credentials
+
+Set up the required API credentials, including the YouTube Data API v3 key, if the collection pipeline requires it.
+
+Store credentials in environment variables or a local `.env` file. Do not commit API keys, passwords, or other secrets to the repository.
+
+### 5. Run the Data Pipeline
+
+```bash
+python main.py
+```
+
+This runs the pipeline orchestrator, subject to the project's configured data sources, credentials, and implementation.
+
+### 6. Launch the Dashboard
+
+```bash
+streamlit run app.py
+```
+
+Open the local URL displayed in the terminal to access the dashboard.
+
+*Run commands should be verified against the actual repository before use.*
+
+## 📈 Dashboard Capabilities
+
+The dashboard is designed to provide:
+
+- **KPI Overview:** Summary counts for monitored artists and channel health categories.
+- **Top Artist Leaderboard:** Artists ranked by their HIFFI composite scores.
+- **Breakout Leaderboard:** Artists highlighted by the BVI.
+- **Identity Audit View:** Confidence scores, verification flags, and eligibility information.
+- **Performance Charts:** Comparisons of view velocity and recent growth.
+- **Category Filters:** Explore artist groups and subgenres.
+- **Artist Details:** Review available channel information and verification results.
+- **Data Exports:** Download filtered results in CSV or Excel format.
+
+## 🔍 Data Quality and Reliability
+
+The system is designed to improve the reliability of artist rankings by:
+
+- Avoiding identity matches based only on similar names.
+- Flagging uncertain or conflicting account information.
+- Distinguishing unavailable metrics from genuine zero values.
+- Considering both recent growth and established audience reach.
+- Tracking historical snapshots to support time-based calculations.
+- Maintaining audit records for review and transparency.
+
+Automated identity matching supports verification but does not guarantee that every account is official. Uncertain matches should be reviewed before being treated as verified.
+
+## 🔐 Security and Responsible Data Handling
+
+- Keep API credentials outside source control.
+- Use environment variables or approved secret-management tools.
+- Respect API quotas, platform terms, and applicable data-use policies.
+- Validate source data before including it in published rankings.
+
+## 🎯 Project Objective
+
+The goal of the HIFFI Hip-Hop 500 Intelligence Engine is to create a transparent, repeatable, and data-driven approach to hip-hop artist analysis.
+
+By combining data quality checks, identity verification, historical performance tracking, weighted ranking, and interactive analytics, the platform supports more informed artist discovery and performance evaluation.

app.py

@@ -4,6 +4,7 @@ HIFFI Artist Intelligence - Executive Dashboard
 Clean dark-themed design with aligned dataframes, responsive cards, and scouting dossier.
 """
 
+import io
 import os
 import pandas as pd
 import plotly.express as px
@@ -107,13 +108,37 @@ def load_data():
         "daily_view_velocity",
         "momentum_30d",
         "confidence_score",
+        "hiffi_rank",
+        "breakout_rank",
     ]
     for col in numeric_cols:
         if col in df.columns:
             df[col] = pd.to_numeric(df[col], errors="coerce")
+
+    # Fallback assignment for subgenre if not yet computed in pipeline
+    if "subgenre" not in df.columns:
+        def infer_subgenre(r):
+            subs = r.get("subscriber_count", 0) or 0
+            bvi = r.get("breakout_velocity_index", 0) or 0
+            if bvi >= 70:
+                return "Breakout / Emerging"
+            elif subs >= 15000000:
+                return "Legends"
+            else:
+                return "Trap & Street"
+        df["subgenre"] = df.apply(infer_subgenre, axis=1)
+
     return df
 
 
+def to_excel_bytes(dataframe: pd.DataFrame) -> bytes:
+    """Converts dataframe to downloadable in-memory Excel spreadsheet."""
+    output = io.BytesIO()
+    with pd.ExcelWriter(output, engine="openpyxl") as writer:
+        dataframe.to_excel(writer, index=False, sheet_name="HIFFI_Cohort")
+    return output.getvalue()
+
+
 df = load_data()
 
 # ----------------- Sidebar Controls -----------------
@@ -135,6 +160,17 @@ with st.sidebar:
     st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
     st.markdown("##### 🛡️ Filters")
 
+    # Subgenre / Vertical Filter
+    all_verticals = sorted(df["subgenre"].dropna().unique().tolist())
+    target_presets = ["Legends", "Trap & Street", "UK Drill", "Lyricists", "Breakout / Emerging"]
+    vertical_choices = [v for v in target_presets if v in all_verticals] + [v for v in all_verticals if v not in target_presets]
+    
+    selected_verticals = st.multiselect(
+        "Hip-Hop Vertical",
+        options=vertical_choices,
+        default=vertical_choices,
+    )
+
     available_statuses = df["health_status"].dropna().unique().tolist()
     selected_statuses = st.multiselect(
         "Channel Status",
@@ -150,15 +186,41 @@ with st.sidebar:
         step=0.05,
     )
 
-    st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
-    st.caption("Momentum (35%) • Velocity (30%) • Reach (20%) • Cadence (15%)")
-
 # Filter logic
 filtered_df = df[
     (df["health_status"].isin(selected_statuses))
+    & (df["subgenre"].isin(selected_verticals))
     & (df["confidence_score"] >= min_conf)
 ].copy()
 
+# Sidebar Data Export Buttons (Filters applied)
+with st.sidebar:
+    st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
+    st.markdown("##### 💾 Export Filtered Cohort")
+    
+    col_csv, col_xlsx = st.columns(2)
+    
+    csv_payload = filtered_df.to_csv(index=False).encode("utf-8")
+    col_csv.download_button(
+        label="📥 CSV",
+        data=csv_payload,
+        file_name="hiffi_filtered_cohort.csv",
+        mime="text/csv",
+        use_container_width=True,
+    )
+
+    excel_payload = to_excel_bytes(filtered_df)
+    col_xlsx.download_button(
+        label="📊 Excel",
+        data=excel_payload,
+        file_name="hiffi_filtered_cohort.xlsx",
+        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+        use_container_width=True,
+    )
+
+    st.markdown("<div class='custom-divider'></div>", unsafe_allow_html=True)
+    st.caption("Momentum (35%) • Velocity (30%) • Reach (20%) • Cadence (15%)")
+
 # ----------------- Main Header -----------------
 head_col1, head_col2 = st.columns([3, 1])
 with head_col1:
@@ -218,7 +280,7 @@ with k4:
         <div class="kpi-box">
             <div class="kpi-title" style="color: #ef4444;">Dormant / Excluded</div>
             <div class="kpi-value" style="color: #ef4444;">{zombies}</div>
-            <div class="kpi-subtitle">Disqualified (<NA> Rank)</div>
+            <div class="kpi-subtitle">Disqualified (&lt;NA&gt; Rank)</div>
         </div>
         """,
         unsafe_allow_html=True,
@@ -243,14 +305,14 @@ if len(top3) >= 3:
         (p3, top3.iloc[2], "🥉 RANK #3", "podium-bronze", "#f97316"),
     ]
 
-    for col, row, rank_label, css_class, accent_color in podium_data:
+    for col, row_data, rank_label, css_class, accent_color in podium_data:
         badge_cls = (
             "badge-active"
-            if row["health_status"] == "ACTIVE_HEALTHY"
+            if row_data["health_status"] == "ACTIVE_HEALTHY"
             else "badge-evergreen"
         )
-        bvi_str = f"{row['breakout_velocity_index']:.1f}" if pd.notna(row.get("breakout_velocity_index")) else "N/A"
+        bvi_str = f"{row_data['breakout_velocity_index']:.1f}" if pd.notna(row_data.get("breakout_velocity_index")) else "N/A"
-        vel_str = f"{int(row['daily_view_velocity']):,}" if pd.notna(row.get("daily_view_velocity")) else "N/A"
+        vel_str = f"{int(row_data['daily_view_velocity']):,}" if pd.notna(row_data.get("daily_view_velocity")) else "N/A"
 
         with col:
             st.markdown(
@@ -258,14 +320,14 @@ if len(top3) >= 3:
                 <div class="podium-tile {css_class}">
                     <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 6px;">
                         <span style="font-weight: 800; color: {accent_color}; font-size: 0.85rem;">{rank_label}</span>
-                        <span class="badge {badge_cls}">{row['health_status'].replace('_', ' ')}</span>
+                        <span class="badge {badge_cls}">{row_data['health_status'].replace('_', ' ')}</span>
                     </div>
-                    <div style="font-size: 1.35rem; font-weight: 800; color: #ffffff; margin-bottom: 2px;">{row['name']}</div>
+                    <div style="font-size: 1.35rem; font-weight: 800; color: #ffffff; margin-bottom: 2px;">{row_data['name']}</div>
-                    <div style="font-size: 0.8rem; color: #6b7280; margin-bottom: 12px;">{row.get('channel_title', '')}</div>
+                    <div style="font-size: 0.8rem; color: #6b7280; margin-bottom: 12px;">{row_data.get('channel_title', '')}</div>
                     <div style="display: flex; justify-content: space-between; background: #0b0f17; padding: 10px 14px; border-radius: 8px;">
                         <div>
                             <div style="font-size: 0.68rem; color: #9ca3af; text-transform: uppercase;">HIFFI Score</div>
-                            <div style="font-size: 1.25rem; font-weight: 800; color: {accent_color};">{row['hiffi_score']:.1f}</div>
+                            <div style="font-size: 1.25rem; font-weight: 800; color: {accent_color};">{row_data['hiffi_score']:.1f}</div>
                         </div>
                         <div>
                             <div style="font-size: 0.68rem; color: #9ca3af; text-transform: uppercase;">Breakout Index</div>
@@ -295,6 +357,7 @@ if view_mode == "🏆 Overall HIFFI Rank":
     cols = [
         "hiffi_rank",
         "name",
+        "subgenre",
         "hiffi_score",
         "breakout_rank",
         "breakout_velocity_index",
@@ -314,6 +377,7 @@ elif view_mode == "🚀 Breakout Velocity (BVI)":
     cols = [
         "breakout_rank",
         "name",
+        "subgenre",
         "breakout_velocity_index",
         "hiffi_rank",
         "hiffi_score",
@@ -325,6 +389,7 @@ else:
     table_df = filtered_df.sort_values(by=["is_rank_eligible", "hiffi_score"], ascending=[False, False]).copy()
     cols = [
         "name",
+        "subgenre",
         "hiffi_score",
         "breakout_velocity_index",
         "daily_view_velocity",
@@ -334,14 +399,18 @@ else:
         "is_rank_eligible",
     ]
 
+# Keep only columns that exist in DataFrame to prevent KeyError
+render_cols = [c for c in cols if c in table_df.columns]
+
 st.dataframe(
-    table_df[cols].reset_index(drop=True),
+    table_df[render_cols].reset_index(drop=True),
     use_container_width=True,
     height=290,
     column_config={
         "hiffi_rank": st.column_config.NumberColumn("Rank", format="%d", width="small"),
         "breakout_rank": st.column_config.NumberColumn("BVI Rank", format="%d", width="small"),
         "name": st.column_config.TextColumn("Artist", width="medium"),
+        "subgenre": st.column_config.TextColumn("Vertical", width="medium"),
         "hiffi_score": st.column_config.ProgressColumn(
             "HIFFI Score",
             format="%.1f",
@@ -451,6 +520,18 @@ st.markdown("#### 🔎 Artist Dossier")
 selected_name = st.selectbox("Inspect Artist Details:", df["name"].unique(), label_visibility="collapsed")
 row = df[df["name"] == selected_name].iloc[0]
 
+# Resolve YouTube channel direct link
+yt_url = str(row.get("youtube_url", "")).strip()
+yt_id = str(row.get("channel_id", row.get("youtube_channel_id", ""))).strip()
+
+if (not yt_url or yt_url == "nan") and yt_id and yt_id != "nan":
+    if yt_id.startswith("UC"):
+        yt_url = f"https://www.youtube.com/channel/{yt_id}"
+    elif yt_id.startswith("@"):
+        yt_url = f"https://www.youtube.com/{yt_id}"
+    else:
+        yt_url = f"https://www.youtube.com/@{yt_id}"
+
 d1, d2, d3, d4 = st.columns(4)
 
 with d1:
@@ -475,15 +556,25 @@ badge_cls = (
     else "badge-dormant"
 )
 
+# Dossier info box with integrated Official YouTube Button
 st.markdown(
     f"""
-    <div style="background: #111827; padding: 12px 16px; border-radius: 8px; border: 1px solid #1f2937; margin-top: 10px; display: flex; gap: 24px; align-items: center; font-size: 0.85rem;">
+    <div style="background: #111827; padding: 14px 18px; border-radius: 8px; border: 1px solid #1f2937; margin-top: 10px; display: flex; justify-content: space-between; align-items: center; font-size: 0.85rem;">
+        <div style="display: flex; gap: 24px; align-items: center;">
             <div><b>Artist:</b> {row['name']}</div>
+            <div><b>Vertical:</b> <span style="color: #a855f7;">{row.get('subgenre', 'N/A')}</span></div>
             <div><b>Channel:</b> {row.get('channel_title', 'Unknown')}</div>
             <div><b>Status:</b> <span class="badge {badge_cls}">{row.get('health_status', 'UNKNOWN')}</span></div>
             <div><b>Zombie Flag:</b> {'⚠️ Yes' if row.get('is_zombie') else '✅ Clean'}</div>
             <div><b>Eligible for Rank:</b> {'✅ Yes' if row.get('is_rank_eligible') else '❌ Disqualified'}</div>
         </div>
+    </div>
     """,
     unsafe_allow_html=True,
 )
+
+st.write("")
+if yt_url and yt_url != "nan":
+    st.link_button("▶ Open Official YouTube Channel", yt_url, use_container_width=False)
+else:
+    st.caption("⚠️ No verified YouTube channel link available for this artist.")