clean up and moving functions to specific folder in dir

MULTI_SPORT_REFACTOR.md

@@ -1,224 +0,0 @@
-# 🏆 Multi-Sport Refactor - Implementation Guide
-
-## ✅ What's Been Done
-
-### 1. Created Sports Module Structure
-```
-src/sports/
-├── __init__.py           # Module initialization
-├── nfl_functions.py      # NFL pipeline (placeholder)
-├── nba_functions.py      # NBA pipeline (placeholder)
-└── mlb_functions.py      # MLB pipeline (placeholder)
-```
-
-### 2. Updated UI for Sport Selection
-- ✅ Changed title to "Paydirt Model Updates" (generic)
-- ✅ Added sport selection dropdown in sidebar
-- ✅ Sport-specific icons and descriptions
-- ✅ Dynamic button text based on selected sport
-- ✅ Added routing structure for NFL, NBA, MLB
-
-### 3. Placeholder Functions Created
-Each sport module has a `run_<sport>_pipeline()` function that:
-- Takes standard parameters (db, gc, gc2, discord)
-- Shows "Coming soon" message
-- Returns True for successful execution
-
-## ⚠️ What Needs to Be Fixed
-
-### Critical: Indentation Issue
-
-The NHL code in `streamlit_app.py` needs to be indented by **4 spaces** (one level) because it's now inside the `if selected_sport == "NHL":` block.
-
-**Lines to indent:** ~2193-2355 (approximately 160 lines)
-
-**Before:**
-```python
-if selected_sport == "NHL":
-    st.write("Starting prop betting table generation...")
-    build_prop_betting_table(db)
-
-try:  # ← This should be indented
-    discord.post(content="NHL Prop Betting Table refreshed")
-```
-
-**After:**
-```python
-if selected_sport == "NHL":
-    st.write("Starting prop betting table generation...")
-    build_prop_betting_table(db)
-
-    try:  # ← Now properly indented
-        discord.post(content="NHL Prop Betting Table refreshed")
-```
-
-### How to Fix:
-
-**Option 1: Manual Fix (Recommended)**
-1. Open `src/streamlit_app.py` in your editor
-2. Find line ~2194 (after `build_prop_betting_table(db)`)
-3. Select all code from line ~2195 to line ~2357 (before the `elif selected_sport ==` lines)
-4. Indent everything by 4 spaces (or one tab)
-5. The `elif` and final `client.close()` should remain at their current indentation
-
-**Option 2: Search & Replace**
-In your editor:
-- Find: `^(\s{4})(try:|except:|st\.|build_|DK_|FD_|now =|current_time|sh =|worksheet|collection|prop_trends|Overall_Proj|chunk_size|time\.sleep)`
-- Replace: `$1    $2` (adds 4 spaces)
-- This should indent most lines automatically
-
-## 📋 Current File Structure
-
-### streamlit_app.py (Main App)
-```python
-# Imports and configuration
-# ...
-
-# UI Setup
-st.set_page_config(...)
-st.title("🏆 Paydirt Model Updates")
-selected_sport = st.sidebar.selectbox(...)  # Sport selector
-
-# Main button with routing
-if st.button(...):
-    if selected_sport == "NHL":
-        # NHL pipeline (NEEDS INDENTATION FIX)
-        build_prop_betting_table(db)
-        # ... 160 lines of NHL code ...
-        
-    elif selected_sport == "NFL":
-        run_nfl_pipeline(db, gc, gc2, discord)
-        
-    elif selected_sport == "NBA":
-        run_nba_pipeline(db, gc, gc2, discord)
-        
-    elif selected_sport == "MLB":
-        run_mlb_pipeline(db, gc, gc2, discord)
-    
-    client.close()
-```
-
-## 🔮 Future Enhancements
-
-### 1. Extract NHL to Separate Module (Optional)
-For cleaner code organization, you could:
-
-```python
-# src/sports/nhl_functions.py
-def run_nhl_pipeline(db, gc, gc2, discord, slate_info, ...):
-    """Complete NHL pipeline"""
-    # Move all 160 lines of NHL code here
-    build_prop_betting_table(db)
-    # ... rest of code ...
-
-# src/streamlit_app.py  
-if selected_sport == "NHL":
-    run_nhl_pipeline(db, gc, gc2, discord, slate_info, ...)
-```
-
-### 2. Add Configuration Per Sport
-```python
-SPORT_CONFIG = {
-    "NHL": {
-        "master_sheet": "...",
-        "position_reqs": {"C": 2, "W": 3, ...},
-        "salary_cap": 50000,
-    },
-    "NFL": {
-        "master_sheet": "...",
-        # NFL-specific config
-    },
-    # ...
-}
-```
-
-### 3. Dynamic Go Binary Selection
-```python
-sport_binary_map = {
-    "NHL": "nhl",
-    "NFL": "nfl",
-    "NBA": "nba",
-    "MLB": "mlb"
-}
-
-go_binary = os.path.join(
-    app_dir, 
-    f"{site.lower()}_{sport_binary_map[sport]}_go",
-    "seed_frames"
-)
-```
-
-## 🎯 Implementation Checklist
-
-- [x] Create sports module structure
-- [x] Add placeholder functions for NFL, NBA, MLB  
-- [x] Update UI with sport selector
-- [x] Add routing logic for different sports
-- [ ] **Fix indentation of NHL code (CRITICAL)**
-- [ ] Test NHL functionality
-- [ ] Test UI with different sport selections
-- [ ] Build NFL pipeline when ready
-- [ ] Build NBA pipeline when ready
-- [ ] Build MLB pipeline when ready
-
-## 🚀 Testing the Changes
-
-Once indentation is fixed:
-
-1. **Test Sport Selection**
-   - Select each sport from dropdown
-   - Verify description updates
-   - Verify button text changes
-
-2. **Test NHL Pipeline**
-   - Select NHL
-   - Click "Generate NHL Lineups"
-   - Should work exactly as before
-
-3. **Test Other Sports**
-   - Select NFL/NBA/MLB
-   - Click generate button
-   - Should show "Coming soon" message
-
-## 📝 Notes
-
-- All NHL functionality remains unchanged
-- The refactor is purely organizational
-- Easy to add new sports by creating new module
-- Maintains backward compatibility with existing NHL code
-
-## 🐛 Known Issues
-
-1. **Indentation Error** - NHL code not indented properly (see above)
-2. **Import Order** - Sports modules imported after UI setup (works but not ideal)
-
-## 💡 Tips for Adding New Sports
-
-When you're ready to add NFL:
-
-1. Edit `src/sports/nfl_functions.py`
-2. Replace placeholder with actual implementation:
-   ```python
-   def run_nfl_pipeline(db, gc, gc2, discord):
-       st.write("Starting NFL pipeline...")
-       
-       # Build prop betting table
-       build_nfl_prop_betting_table(db)
-       
-       # Build player outcomes
-       roo_file = build_nfl_player_outcomes(db)
-       
-       # Generate seed frames
-       DK_NFL_seed_frame(db, roo_file)
-       FD_NFL_seed_frame(db, roo_file)
-       
-       st.success("✅ NFL pipeline complete!")
-       return True
-   ```
-3. Create NFL-specific helper functions in same file
-4. Update Go binaries for NFL if needed
-
----
-
-**Status:** Multi-sport UI ready, indentation fix required for full functionality
-

OPTIMIZATION_CHANGES.md

@@ -1,166 +0,0 @@
-# 🚀 Build Time Optimization - Changes Made
-
-## Problem
-Docker build was taking ~1 hour due to:
-1. **NHL_own_regress.py** training 3 ML models on every import
-2. **Heavy ML dependencies** (xgboost, lightgbm, scikit-learn)
-3. MongoDB data download during build
-
-## Solutions Implemented
-
-### 1. ✅ Removed ML Model Training at Import Time
-**Before:** NHL_own_regress.py would:
-- Connect to MongoDB
-- Download thousands of rows of historical data  
-- Train 3 models with 1000 estimators each
-- This happened EVERY time the file was imported!
-
-**After:** 
-- Models are no longer imported
-- Using simplified heuristic-based ownership prediction
-- No training at startup
-
-### 2. ✅ Simplified Ownership Prediction
-**Replaced this:**
-```python
-basic_own_df['XGB'] = np_clip(xgb_model.predict(X_current), 0, 100)
-basic_own_df['LGB'] = np_clip(lgb_model.predict(X_current), 0, 100) * 100
-basic_own_df['KNN'] = np_clip(knn_model.predict(X_current), 0, 100)
-basic_own_df['Combo'] = (XGB * .30) + (LGB * .30) + (KNN * .40)
-```
-
-**With this:**
-```python
-basic_own_df['Combo'] = (
-    (basic_own_df['value'] * 10) * 
-    (100 / (basic_own_df['Salary'] / 1000))
-) / 100
-```
-
-### 3. ✅ Reduced Python Dependencies
-**Before (12 packages):**
-- streamlit
-- pandas
-- numpy
-- altair
-- pytz
-- **ortools** (still needed - 500MB!)
-- gspread
-- discordwebhook
-- pymongo
-- **xgboost** (❌ removed - 250MB)
-- **lightgbm** (❌ removed - 150MB)
-- **scikit-learn** (❌ removed - 200MB)
-
-**After (8 packages):**
-Only the essential packages remain.
-
-**Space Saved:** ~600MB in dependencies!
-
-### 4. ✅ Optimized Dockerfile
-- Removed copying of `func/` directory (not needed)
-- Only copies `src/` (the actual app)
-- Go binaries copied directly from builder stage
-
-## Expected Build Time Improvement
-
-| Phase | Before | After | Savings |
-|-------|--------|-------|---------|
-| Download Dependencies | ~15 min | ~5 min | **10 min** |
-| Install Dependencies | ~25 min | ~8 min | **17 min** |
-| Model Training | ~15 min | 0 min | **15 min** |
-| Copy Files | ~3 min | ~2 min | **1 min** |
-| Go Build | ~5 min | ~5 min | 0 min |
-| **TOTAL** | **~60 min** | **~20 min** | **~40 min (67% faster)** |
-
-## Files Modified
-
-1. **`requirements.txt`** - Removed heavy ML packages
-2. **`src/streamlit_app.py`** - Removed ML model imports, simplified prediction
-3. **`func/NHL_own_regress.py`** - Wrapped training in `if __name__ == '__main__'`
-4. **`Dockerfile`** - Removed unnecessary file copying
-
-## Trade-offs
-
-### What We Lost:
-- ML-based ownership predictions
-- Historical model accuracy metrics
-
-### What We Kept:
-- Fast build times
-- All core functionality
-- Lineup optimization (ortools)
-- Data processing
-- Google Sheets integration
-- MongoDB integration
-- Discord notifications
-
-### What We Gained:
-- **67% faster builds** (60min → 20min)
-- Faster app startup
-- Lower memory usage
-- Simpler codebase
-
-## Ownership Prediction Accuracy
-
-The simplified heuristic uses:
-- Player value (projection/salary)
-- Salary tier adjustments
-- Leverage multipliers
-
-While not as sophisticated as ML models, it's:
-- ✅ Fast (instant vs minutes)
-- ✅ Transparent (no black box)
-- ✅ Good enough for most use cases
-- ✅ Customizable with business logic
-
-## If You Need ML Models Later
-
-If you want ML-based predictions back:
-
-### Option 1: Pre-train and Pickle Models
-```python
-# Train once locally
-import pickle
-# ... train models ...
-pickle.dump(xgb_model, open('xgb_model.pkl', 'wb'))
-
-# Load in app
-xgb_model = pickle.load(open('xgb_model.pkl', 'rb'))
-```
-
-### Option 2: Use Lighter Models
-- Replace XGBoost/LightGBM with simpler sklearn models
-- Use fewer estimators (100 instead of 1000)
-- Cache predictions
-
-### Option 3: Train in Background
-- Train models async after app starts
-- Use default predictions until models ready
-- Scheduled retraining
-
-## Validation
-
-To ensure everything still works:
-
-1. ✅ App imports successfully
-2. ✅ No missing dependencies  
-3. ✅ Streamlit UI loads
-4. ✅ MongoDB connection works
-5. ✅ Google Sheets connection works
-6. ✅ Lineup optimization works (ortools)
-7. ✅ Go binaries execute
-8. ✅ Ownership predictions calculate
-
-## Deploy Now
-
-Your app should now build in ~20 minutes instead of ~60 minutes!
-
-```bash
-git add .
-git commit -m "Optimize build: Remove heavy ML dependencies"
-git push
-```
-
-Monitor the build logs - you should see it complete much faster! 🚀
-

REFACTOR_COMPLETE.md

@@ -1,176 +0,0 @@
-# ✅ Multi-Sport Refactor - Complete!
-
-## 🎉 What's Working
-
-Your Streamlit app now supports multiple sports! Here's what's been implemented:
-
-### 1. **Sport Selection UI** ✅
-- Dropdown selector in sidebar with sport icons:
-  - 🏒 NHL
-  - 🏈 NFL  
-  - 🏀 NBA
-  - ⚾ MLB
-
-### 2. **Modular Structure** ✅
-```
-src/sports/
-├── __init__.py          # Module initialization
-├── nfl_functions.py     # NFL pipeline (placeholder)
-├── nba_functions.py     # NBA pipeline (placeholder)
-└── mlb_functions.py     # MLB pipeline (placeholder)
-```
-
-### 3. **Sport Routing** ✅
-The app now routes to different pipelines based on sport selection:
-- NHL: Full pipeline (existing code)
-- NFL/NBA/MLB: Placeholder "Coming soon" messages
-
-## 🚀 How to Use
-
-1. **Launch the app**
-   ```bash
-   streamlit run src/streamlit_app.py
-   ```
-
-2. **Select a sport** from the sidebar dropdown
-
-3. **Click the Generate button** (button text changes based on sport)
-
-4. **See results**:
-   - NHL: Runs full lineup generation
-   - Other sports: Shows "Coming soon" message
-
-## 📝 Current Features
-
-### NHL (Fully Functional)
-- ✅ Prop betting table generation
-- ✅ DK player level outcomes
-- ✅ FD player level outcomes  
-- ✅ Stack matrix generation (DK & FD)
-- ✅ PP stack matrix generation
-- ✅ Seed frame generation (DK & FD)
-- ✅ MongoDB updates
-- ✅ Google Sheets updates
-- ✅ Discord notifications
-
-### NFL, NBA, MLB (Placeholder)
-- ✅ "Coming soon" messages
-- ✅ Ready for implementation
-- ✅ Same structure as NHL
-
-## 🔧 Adding New Sports
-
-When you're ready to implement NFL:
-
-1. **Edit `src/sports/nfl_functions.py`**:
-```python
-def run_nfl_pipeline(db, gc, gc2, discord):
-    st.write("🏈 Starting NFL pipeline...")
-    
-    # Your NFL-specific code here
-    build_nfl_prop_betting_table(db)
-    roo_file = build_nfl_player_outcomes(db)
-    DK_NFL_seed_frame(db, roo_file)
-    FD_NFL_seed_frame(db, roo_file)
-    
-    st.success("✅ NFL complete!")
-    return True
-```
-
-2. **Create NFL-specific helper functions** in the same file
-
-3. **Test** by selecting NFL from dropdown
-
-## ⚠️ Known Issues (Minor)
-
-### Indentation Inconsistency
-Some lines in the NHL block (lines ~2195-2206) have inconsistent indentation. This doesn't affect functionality but may show linter warnings.
-
-**To fix manually** (optional):
-1. Open `src/streamlit_app.py`
-2. Find line 2195 (`try:`)
-3. Add 4 spaces to lines 2195-2206
-4. Everything between `build_prop_betting_table(db)` and `elif selected_sport == "NFL"` should be indented 8 spaces
-
-**Impact**: None - code works correctly despite formatting inconsistency
-
-## 📊 File Changes Summary
-
-### Modified:
-- `src/streamlit_app.py` - Added sport selection UI and routing
-
-### Created:
-- `src/sports/__init__.py`
-- `src/sports/nfl_functions.py`
-- `src/sports/nba_functions.py`
-- `src/sports/mlb_functions.py`
-- `MULTI_SPORT_REFACTOR.md` (implementation guide)
-- `REFACTOR_COMPLETE.md` (this file)
-
-## 🎯 Next Steps
-
-1. **Test NHL functionality** - Verify everything still works
-2. **Implement NFL** when ready
-3. **Implement NBA** when ready
-4. **Implement MLB** when ready
-
-## 🏗️ Architecture Benefits
-
-### Before:
-- Single sport (NHL) hardcoded
-- No easy way to add new sports
-- Everything in one massive file
-
-### After:
-- ✅ Multi-sport support
-- ✅ Easy to add new sports
-- ✅ Modular structure
-- ✅ Clean separation of concerns
-- ✅ Professional UI with sport selection
-
-## 💡 Tips
-
-### Sport Icons
-Update icons in `streamlit_app.py`:
-```python
-sport_icons = {
-    "NHL": "🏒",
-    "NFL": "🏈",
-    "NBA": "🏀",
-    "MLB": "⚾",
-    "SOCCER": "⚽",  # Easy to add more!
-}
-```
-
-### Sport Descriptions
-Update descriptions:
-```python
-sport_descriptions = {
-    "NHL": "Generate NHL lineup projections...",
-    "NFL": "Generate NFL lineup projections...",
-    # ...
-}
-```
-
-## 🎨 UI Improvements Made
-
-1. **Generic branding**: "Paydirt Model Updates" instead of "Paydirt NHL..."
-2. **Sport selector**: Prominent dropdown with icons
-3. **Dynamic content**: Button and messages change based on sport
-4. **Clean layout**: Organized sidebar and main content
-5. **Status indicators**: Connection status for MongoDB and Google Sheets
-
-## ✨ Success Metrics
-
-- ✅ All NHL functionality preserved
-- ✅ Easy to add new sports (3 lines of code)
-- ✅ Professional multi-sport UI
-- ✅ Backward compatible
-- ✅ Ready for production
-
----
-
-**Status**: Multi-sport refactor complete and ready for use! 🚀
-
-To deploy: `git push` (build should complete in ~10-20 minutes)
-

src/database.py

@@ -0,0 +1,59 @@
+import streamlit as st
+import os
+from pymongo import MongoClient
+from gspread.auth import service_account_from_dict
+
+# Helper function to get secrets
+def get_secret(key, default=None):
+    """Get secret from Streamlit secrets or environment variables"""
+    try:
+        return st.secrets[key]
+    except:
+        return os.getenv(key, default)
+
+# Load credentials from Streamlit secrets or environment variables
+scope = ['https://www.googleapis.com/auth/spreadsheets',
+      "https://www.googleapis.com/auth/drive"]
+
+# Google Sheets credentials
+credentials = {
+  "type": "service_account",
+  "project_id": get_secret("GOOGLE_PROJECT_ID_1", "model-sheets-connect"),
+  "private_key_id": get_secret("GOOGLE_PRIVATE_KEY_ID_1", "0e0bc2fdef04e771172fe5807392b9d6639d945e"),
+  "private_key": get_secret("GOOGLE_PRIVATE_KEY_1", "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n"),
+  "client_email": get_secret("GOOGLE_CLIENT_EMAIL_1", "gspread-connection@model-sheets-connect.iam.gserviceaccount.com"),
+  "client_id": get_secret("GOOGLE_CLIENT_ID_1", "100369174533302798535"),
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com"
+}
+
+credentials2 = {
+  "type": "service_account",
+  "project_id": get_secret("GOOGLE_PROJECT_ID_2", "sheets-api-connect-378620"),
+  "private_key_id": get_secret("GOOGLE_PRIVATE_KEY_ID_2", "1005124050c80d085e2c5b344345715978dd9cc9"),
+  "private_key": get_secret("GOOGLE_PRIVATE_KEY_2", "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n"),
+  "client_email": get_secret("GOOGLE_CLIENT_EMAIL_2", "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com"),
+  "client_id": get_secret("GOOGLE_CLIENT_ID_2", "106625872877651920064"),
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
+}
+
+gc = service_account_from_dict(credentials)
+gc2 = service_account_from_dict(credentials2)
+
+# MongoDB URI
+uri = get_secret("MONGODB_URI", "mongodb+srv://multichem:Xr1q5wZdXPbxdUmJ@testcluster.lgwtp5i.mongodb.net/?retryWrites=true&w=majority")
+
+client = MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=10000, w=0)
+
+nhl_db = client['NHL_Database']
+
+# Google Sheets URL
+NHL_Master_hold = get_secret("MASTER_SHEET_URL", 'https://docs.google.com/spreadsheets/d/1NmKa-b-2D3w7rRxwMPSchh31GKfJ1XcDI2GU8rXWnHI/edit#gid=578660863')
+
+# Discord Webhook
+discord = Discord(url=get_secret("DISCORD_WEBHOOK_URL", "https://discord.com/api/webhooks/1244687568394780672/COng4Gz1JFdoS-zCCcB24tQWo1upansxWFdfIv16_HZIb_j7-glZoGd4TXAGJDLIRiIJ"))