chore: change the system from loading to adding the csv for solving the rate limiter error

.gitignore

@@ -1,3 +1,10 @@
+__pycache__/
 hf-dash/
 env/ 
 debug_scripts/
+.DS_Store
+*.txt
+*.html
+*.png
+*.json
+*.log

QUICK_START_GUIDE.md

@@ -0,0 +1,207 @@
+# Quick Start Guide: CSV-First Deployment
+
+This guide shows you exactly how to run the CSV-first deployment for your Hugging Face Space.
+
+## Prerequisites
+
+Make sure you have Python installed with these packages:
+```bash
+pip install pandas requests plotly gradio logging datetime typing os
+```
+
+## Step 1: Generate CSV Files Locally
+
+### Option A: Using the Interactive Script (Recommended)
+
+1. **Open your terminal** in the project directory
+2. **Run the CSV generation script**:
+   ```bash
+   python generate_csv_for_space.py
+   ```
+
+3. **Follow the interactive prompts**:
+   ```
+   ============================================================
+   CSV Generation for Hugging Face Space Deployment
+   ============================================================
+
+   1. Checking existing CSV files...
+   2. Checking data freshness...
+   3. Data generation options:
+      [1] Generate fresh data from API (recommended)
+      [2] Skip if CSV files are fresh (< 24 hours old)
+      [3] Exit without generating
+
+   Enter your choice (1-3): 1
+   ```
+
+4. **Choose option 1** to generate fresh data
+5. **Wait for completion** - the script will:
+   - Fetch data from the API
+   - Apply preprocessing
+   - Save CSV files
+   - Show you what files were created
+
+### Option B: Using Python Directly
+
+If you prefer to run it programmatically:
+
+```python
+# Run this in Python or Jupyter notebook
+from app import fetch_apr_data_from_db, save_to_csv, save_roi_to_csv
+from initial_value_fixer import fix_apr_and_roi
+
+# Fetch data
+df_apr, df_roi = fetch_apr_data_from_db()
+
+# Apply preprocessing
+df_apr_processed = fix_apr_and_roi(df_apr)
+
+# Save CSV files
+save_to_csv(df_apr_processed)
+save_roi_to_csv(df_roi)
+
+print("CSV files generated successfully!")
+```
+
+## Step 2: Verify CSV Files Were Created
+
+Check that these files exist in your directory:
+```bash
+ls -la *.csv
+```
+
+You should see:
+- `optimus_apr_values.csv`
+- `optimus_apr_statistics.csv`
+- `optimus_roi_values.csv`
+
+## Step 3: Test CSV Loading Locally (Optional)
+
+Test that the CSV files load correctly:
+
+```python
+from load_from_csv import load_apr_data_from_csv, load_roi_data_from_csv
+
+# Test loading
+df_apr, csv_file = load_apr_data_from_csv()
+df_roi, csv_file = load_roi_data_from_csv()
+
+print(f"APR data loaded: {len(df_apr)} records")
+print(f"ROI data loaded: {len(df_roi)} records")
+```
+
+## Step 4: Test the App Locally
+
+Run your app locally to make sure everything works:
+
+```bash
+python app.py
+```
+
+The app should:
+1. Try to load from CSV files first
+2. Show visualizations using CSV data
+3. Display "Successfully loaded APR/ROI data from CSV" in logs
+
+## Step 5: Deploy to Hugging Face Space
+
+### Upload These Files to Your Space:
+
+**Required Files:**
+- `app.py` (modified with CSV-first logic)
+- `load_from_csv.py` (new CSV loading functions)
+- `initial_value_fixer.py` (existing preprocessing)
+- `fetch_and_preprocess_data.py` (existing data functions)
+
+**Generated CSV Files:**
+- `optimus_apr_values.csv`
+- `optimus_apr_statistics.csv`
+- `optimus_roi_values.csv`
+
+### Upload Methods:
+
+**Method 1: Hugging Face Web Interface**
+1. Go to your Space on huggingface.co
+2. Click "Files" tab
+3. Upload each file individually
+4. Commit changes
+
+**Method 2: Git (if you have git setup)**
+```bash
+git add *.py *.csv
+git commit -m "Add CSV-first deployment files"
+git push
+```
+
+## Step 6: Monitor Your Space
+
+After deployment:
+
+1. **Check Space logs** for these messages:
+   ```
+   Successfully loaded APR data from CSV: X records
+   Successfully loaded ROI data from CSV: Y records
+   Creating APR visualizations from CSV data...
+   ```
+
+2. **Verify fast loading** - graphs should appear instantly
+
+3. **No API calls** - you shouldn't see API-related errors in logs
+
+## Troubleshooting
+
+### Problem: "No module named 'load_from_csv'"
+**Solution:** Make sure you uploaded `load_from_csv.py` to your Space
+
+### Problem: "CSV file not found"
+**Solution:** 
+1. Check CSV files are in the Space root directory
+2. Verify file names match exactly: `optimus_apr_values.csv`, `optimus_roi_values.csv`
+
+### Problem: "Error loading data from CSV"
+**Solution:**
+1. Regenerate CSV files locally: `python generate_csv_for_space.py`
+2. Re-upload the new CSV files to your Space
+
+### Problem: App falls back to API calls
+**Solution:** This means CSV loading failed. Check Space logs for specific error messages.
+
+## Updating Data
+
+To update your Space with fresh data:
+
+1. **Run locally** (every few days or weekly):
+   ```bash
+   python generate_csv_for_space.py
+   ```
+
+2. **Upload new CSV files** to your Space
+
+3. **Space automatically updates** with new data
+
+## Expected Results
+
+✅ **Fast Loading**: Graphs appear instantly  
+✅ **No Rate Limits**: No API calls from the Space  
+✅ **Smooth Graphs**: ROI graph has smooth curves  
+✅ **All Features**: All preprocessing and visualization features work  
+✅ **Reliable**: No dependency on external API availability  
+
+## Commands Summary
+
+```bash
+# Generate CSV files
+python generate_csv_for_space.py
+
+# Test locally
+python app.py
+
+# Check what files were created
+ls -la *.csv
+
+# Check file sizes
+du -h *.csv
+```
+
+That's it! Your Hugging Face Space will now run without rate limiting issues using the preprocessed CSV data.

README_CSV_DEPLOYMENT.md

@@ -0,0 +1,173 @@
+# CSV-First Deployment for Hugging Face Spaces
+
+This document explains how to deploy your Optimus Agent Performance dashboard to Hugging Face Spaces using a CSV-first approach to avoid API rate limiting issues.
+
+## Overview
+
+The CSV-first approach works by:
+1. **Local Data Generation**: Run data fetching and preprocessing locally where you have full API access
+2. **CSV Export**: Save preprocessed data to CSV files
+3. **Space Deployment**: Upload CSV files to your Hugging Face Space
+4. **CSV-First Loading**: The Space app prioritizes loading from CSV files over making API calls
+
+## Files Added for CSV-First Deployment
+
+### 1. `load_from_csv.py`
+Contains functions to load data from CSV files:
+- `load_apr_data_from_csv()` - Loads APR data from CSV
+- `load_roi_data_from_csv()` - Loads ROI data from CSV
+- `load_statistics_from_csv()` - Loads statistics from CSV
+- `check_csv_data_availability()` - Checks which CSV files are available
+- `get_data_freshness_info()` - Analyzes how fresh the CSV data is
+
+### 2. `generate_csv_for_space.py`
+Interactive script to generate CSV files locally:
+- Fetches fresh data from the API
+- Applies all preprocessing (including `initial_value_fixer.py`)
+- Saves to CSV files ready for Space deployment
+- Provides deployment guidance
+
+### 3. Modified `app.py`
+Updated visualization functions with CSV-first logic:
+- `generate_apr_visualizations()` - Now tries CSV first, falls back to API
+- `generate_roi_visualizations()` - Now tries CSV first, falls back to API
+- Imports CSV loading functions
+- Maintains backward compatibility
+
+## Deployment Workflow
+
+### Step 1: Generate CSV Files Locally
+
+Run the CSV generation script on your local machine:
+
+```bash
+python generate_csv_for_space.py
+```
+
+This will:
+- Check existing CSV files and their freshness
+- Fetch fresh data from the API (no rate limits locally)
+- Apply preprocessing using `initial_value_fixer.py`
+- Save CSV files: `optimus_apr_values.csv`, `optimus_apr_statistics.csv`, `optimus_roi_values.csv`
+
+### Step 2: Upload Files to Hugging Face Space
+
+Upload these files to your Hugging Face Space repository:
+- `app.py` (modified with CSV-first logic)
+- `load_from_csv.py` (new CSV loading functions)
+- `initial_value_fixer.py` (existing preprocessing)
+- `fetch_and_preprocess_data.py` (existing data functions)
+- `optimus_apr_values.csv` (generated data)
+- `optimus_apr_statistics.csv` (generated statistics)
+- `optimus_roi_values.csv` (generated data)
+
+### Step 3: Deploy Space
+
+Your Space will now:
+1. **Try CSV first**: Load data from uploaded CSV files
+2. **Fast loading**: No API calls needed, instant visualization
+3. **Fallback**: If CSV files are missing, fall back to API (with rate limits)
+4. **Error handling**: Show appropriate messages if both CSV and API fail
+
+## CSV File Structure
+
+### APR Data (`optimus_apr_values.csv`)
+```csv
+apr,adjusted_apr,timestamp,agent_id,agent_name,is_dummy,metric_type,roi,address
+15.5,12.3,2025-06-14 10:30:00,123,Agent_1,False,APR,0.85,0x123...
+```
+
+### ROI Data (`optimus_roi_values.csv`)
+```csv
+roi,timestamp,agent_id,agent_name,is_dummy,metric_type
+0.85,2025-06-14 10:30:00,123,Agent_1,False,ROI
+```
+
+### Statistics (`optimus_apr_statistics.csv`)
+```csv
+agent_id,agent_name,total_points,apr_points,avg_apr,avg_adjusted_apr,latest_timestamp
+123,Agent_1,100,50,15.5,12.3,2025-06-14 10:30:00
+```
+
+## Benefits
+
+### ✅ Advantages
+- **No Rate Limits**: Space doesn't make API calls
+- **Fast Loading**: Instant visualization from CSV
+- **Reliable**: No dependency on external API availability
+- **Preprocessed**: Data includes all transformations from `initial_value_fixer.py`
+- **Fresh Data**: Update CSV files as needed locally
+
+### ⚠️ Considerations
+- **Manual Updates**: Need to regenerate CSV files for fresh data
+- **File Size**: CSV files add to Space storage
+- **Data Staleness**: CSV data becomes stale over time
+
+## Updating Data
+
+To update your Space with fresh data:
+
+1. **Run locally**: `python generate_csv_for_space.py`
+2. **Upload new CSV files** to your Space repository
+3. **Space auto-updates** with new data
+
+## Monitoring
+
+### Data Freshness
+The app logs data freshness information:
+```python
+from load_from_csv import get_data_freshness_info
+freshness = get_data_freshness_info()
+# Shows how old the CSV data is
+```
+
+### CSV Availability
+Check which CSV files are available:
+```python
+from load_from_csv import check_csv_data_availability
+availability = check_csv_data_availability()
+# Shows file status, size, modification date
+```
+
+## Troubleshooting
+
+### CSV Files Not Loading
+1. Check file names match exactly: `optimus_apr_values.csv`, `optimus_roi_values.csv`
+2. Verify CSV files are in the Space root directory
+3. Check Space logs for CSV loading errors
+
+### Data Issues
+1. Regenerate CSV files locally with fresh API data
+2. Verify preprocessing ran correctly in `generate_csv_for_space.py`
+3. Check CSV file structure matches expected format
+
+### Fallback to API
+If CSV loading fails, the app falls back to API calls:
+1. This may trigger rate limits in Hugging Face Spaces
+2. Check Space logs for API errors
+3. Ensure CSV files are properly uploaded
+
+## Best Practices
+
+1. **Regular Updates**: Run `generate_csv_for_space.py` daily or weekly
+2. **Monitor Logs**: Check Space logs for CSV loading status
+3. **Backup CSV**: Keep local copies of generated CSV files
+4. **Test Locally**: Test CSV loading locally before deploying
+5. **Version Control**: Track CSV generation timestamps
+
+## Example Space Configuration
+
+Your Space should include these files:
+```
+├── app.py                          # Modified with CSV-first logic
+├── load_from_csv.py               # CSV loading functions
+├── initial_value_fixer.py         # Preprocessing logic
+├── fetch_and_preprocess_data.py   # Data functions
+├── optimus_apr_values.csv         # Generated APR data
+├── optimus_apr_statistics.csv     # Generated statistics
+├── optimus_roi_values.csv         # Generated ROI data
+├── requirements.txt               # Dependencies
+└── README.md                      # Space documentation
+```
+
+This approach ensures your Hugging Face Space runs reliably without hitting API rate limits while maintaining all the preprocessing and visualization features of your application.

app.py

@@ -20,6 +20,13 @@ from typing import List, Dict, Any, Optional
 # APR visualization functions integrated directly
 from fetch_and_preprocess_data import generate_continuous_random_data
 from initial_value_fixer import fix_apr_and_roi
+from load_from_csv import (
+    load_apr_data_from_csv, 
+    load_roi_data_from_csv, 
+    load_statistics_from_csv,
+    check_csv_data_availability,
+    get_data_freshness_info
+)
 
 # Set up logging with appropriate verbosity
 logging.basicConfig(
@@ -752,91 +759,158 @@ def log_adjusted_apr_availability(df):
                     logger.info(f"Agent {agent_name} (ID: {agent_id}): Missing adjusted_apr from {gap_start} to {gap_end} ({gap_duration.days} days, {gap_duration.seconds//3600} hours)")
 
 def generate_apr_visualizations():
-    """Generate APR visualizations with real data only (no dummy data)"""
+    """Generate APR visualizations with CSV-first approach for Hugging Face Space deployment"""
     global global_df
     
-    # Fetch data from database
-    df, _ = fetch_apr_data_from_db()
+    # CSV-FIRST APPROACH: Try to load from CSV first
+    logger.info("Attempting to load APR data from CSV files...")
+    df, csv_file = load_apr_data_from_csv()
     
-    # If we got no data at all, return placeholder figures
-    if df.empty:
-        logger.info("No APR data available. Using fallback visualization.")
-        # Create empty visualizations with a message using Plotly
+    if not df.empty:
+        logger.info(f"Successfully loaded APR data from CSV: {len(df)} records")
+        global_df = df
+        
+        # Create visualizations using CSV data
+        logger.info("Creating APR visualizations from CSV data...")
+        combined_fig = create_combined_time_series_graph(df)
+        return combined_fig, csv_file
+    
+    # FALLBACK: If CSV not available, try API
+    logger.info("CSV data not available, falling back to API...")
+    try:
+        df, _ = fetch_apr_data_from_db()
+        
+        # If we got no data at all, return placeholder figures
+        if df.empty:
+            logger.info("No APR data available from API either. Using fallback visualization.")
+            # Create empty visualizations with a message using Plotly
+            fig = go.Figure()
+            fig.add_annotation(
+                x=0.5, y=0.5,
+                text="No APR data available",
+                font=dict(size=20),
+                showarrow=False
+            )
+            fig.update_layout(
+                xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+                yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
+            )
+            
+            # Save as static file for reference
+            fig.write_html("optimus_apr_combined_graph.html")
+            fig.write_image("optimus_apr_combined_graph.png")
+            
+            csv_file = None
+            return fig, csv_file
+        
+        # Apply preprocessing to fix APR and ROI values
+        logger.info("Applying preprocessing to fix APR and ROI values...")
+        df = fix_apr_and_roi(df)  # Apply preprocessing
+        global_df = df
+
+        # Save preprocessed data to CSV before creating visualizations
+        logger.info("Saving preprocessed APR data to CSV...")
+        csv_file = save_to_csv(df)
+        
+        # Create visualizations using the saved CSV data
+        logger.info("Creating APR visualizations from preprocessed data...")
+        combined_fig = create_combined_time_series_graph(df)
+        
+        return combined_fig, csv_file
+        
+    except Exception as e:
+        logger.error(f"Error fetching APR data from API: {e}")
+        # Return error visualization
         fig = go.Figure()
         fig.add_annotation(
             x=0.5, y=0.5,
-            text="No APR data available",
-            font=dict(size=20),
+            text=f"Error loading data: {str(e)}",
+            font=dict(size=16, color="red"),
             showarrow=False
         )
         fig.update_layout(
             xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
             yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
         )
-        
-        # Save as static file for reference
-        fig.write_html("optimus_apr_combined_graph.html")
-        fig.write_image("optimus_apr_combined_graph.png")
-        
-        csv_file = None
-        return fig, csv_file
-    
-    # No longer generating dummy data
-    # Set global_df for access by other functions
-    df = fix_apr_and_roi(df)  # Currently it assumes no investment has been made
-    global_df = df
-
-    # Save to CSV before creating visualizations
-    csv_file = save_to_csv(df)
-    
-    # Only create combined time series graph
-    combined_fig = create_combined_time_series_graph(df)
-    
-    return combined_fig, csv_file
+        return fig, None
 
 def generate_roi_visualizations():
-    """Generate ROI visualizations with real data only (no dummy data)"""
+    """Generate ROI visualizations with CSV-first approach for Hugging Face Space deployment"""
     global global_roi_df
     
-    # Fetch data from database if not already fetched
-    if global_roi_df is None or global_roi_df.empty:
-        _, df_roi = fetch_apr_data_from_db()
-    else:
-        df_roi = global_roi_df
+    # CSV-FIRST APPROACH: Try to load from CSV first
+    logger.info("Attempting to load ROI data from CSV files...")
+    df_roi, csv_file = load_roi_data_from_csv()
     
-    # If we got no data at all, return placeholder figures
-    if df_roi.empty:
-        logger.info("No ROI data available. Using fallback visualization.")
-        # Create empty visualizations with a message using Plotly
+    if not df_roi.empty:
+        logger.info(f"Successfully loaded ROI data from CSV: {len(df_roi)} records")
+        global_roi_df = df_roi
+        
+        # Create visualizations using CSV data
+        logger.info("Creating ROI visualizations from CSV data...")
+        combined_fig = create_combined_roi_time_series_graph(df_roi)
+        return combined_fig, csv_file
+    
+    # FALLBACK: If CSV not available, try API
+    logger.info("CSV data not available, falling back to API...")
+    try:
+        # Fetch data from database if not already fetched
+        if global_roi_df is None or global_roi_df.empty:
+            _, df_roi = fetch_apr_data_from_db()
+        else:
+            df_roi = global_roi_df
+        
+        # If we got no data at all, return placeholder figures
+        if df_roi.empty:
+            logger.info("No ROI data available from API either. Using fallback visualization.")
+            # Create empty visualizations with a message using Plotly
+            fig = go.Figure()
+            fig.add_annotation(
+                x=0.5, y=0.5,
+                text="No ROI data available",
+                font=dict(size=20),
+                showarrow=False
+            )
+            fig.update_layout(
+                xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+                yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
+            )
+            
+            # Save as static file for reference
+            fig.write_html("optimus_roi_graph.html")
+            fig.write_image("optimus_roi_graph.png")
+            
+            csv_file = None
+            return fig, csv_file
+        
+        # Set global_roi_df for access by other functions
+        global_roi_df = df_roi
+        
+        # Save preprocessed ROI data to CSV before creating visualizations
+        logger.info("Saving preprocessed ROI data to CSV...")
+        csv_file = save_roi_to_csv(df_roi)
+        
+        # Create visualizations using the saved CSV data
+        logger.info("Creating ROI visualizations from preprocessed data...")
+        combined_fig = create_combined_roi_time_series_graph(df_roi)
+        
+        return combined_fig, csv_file
+        
+    except Exception as e:
+        logger.error(f"Error fetching ROI data from API: {e}")
+        # Return error visualization
         fig = go.Figure()
         fig.add_annotation(
             x=0.5, y=0.5,
-            text="No ROI data available",
-            font=dict(size=20),
+            text=f"Error loading data: {str(e)}",
+            font=dict(size=16, color="red"),
             showarrow=False
         )
         fig.update_layout(
             xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
             yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
         )
-        
-        # Save as static file for reference
-        fig.write_html("optimus_roi_graph.html")
-        fig.write_image("optimus_roi_graph.png")
-        
-        csv_file = None
-        return fig, csv_file
-    
-    # Set global_roi_df for access by other functions
-    global_roi_df = df_roi
-    
-    # Save to CSV before creating visualizations
-    csv_file = save_roi_to_csv(df_roi)
-    
-    # Create combined time series graph for ROI
-    combined_fig = create_combined_roi_time_series_graph(df_roi)
-    
-    return combined_fig, csv_file
+        return fig, None
 
 def aggregate_daily_data(df, metric_column):
     """
@@ -1151,7 +1225,7 @@ def create_combined_roi_time_series_graph(df):
                 x=x_values_ma,
                 y=y_values_ma,
                 mode='lines',  # Only lines for moving average
-                line=dict(color='blue', width=3),  # Thicker line for main trend
+                line=dict(color='blue', width=3, shape='spline', smoothing=1.3),  # Smooth curved line like APR
                 name='Median ROI (7d window)',
                 hovertext=hover_data_roi,
                 hoverinfo='text',

generate_csv_for_space.py

@@ -0,0 +1,193 @@
+#!/usr/bin/env python3
+"""
+CSV Generation Script for Hugging Face Space Deployment
+
+This script fetches data from the API, applies preprocessing, and saves CSV files
+that can be uploaded to your Hugging Face Space to avoid rate limiting issues.
+
+Usage:
+    python generate_csv_for_space.py
+
+Output files:
+    - optimus_apr_values.csv
+    - optimus_apr_statistics.csv
+    - optimus_roi_values.csv
+"""
+
+import logging
+import sys
+import os
+from datetime import datetime
+
+# Add the current directory to the path so we can import our modules
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+# Import our existing functions
+from app import fetch_apr_data_from_db, save_to_csv, save_roi_to_csv
+from initial_value_fixer import fix_apr_and_roi
+from load_from_csv import check_csv_data_availability, get_data_freshness_info
+
+# Set up logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(message)s",
+    handlers=[
+        logging.FileHandler("csv_generation.log"),
+        logging.StreamHandler()
+    ]
+)
+logger = logging.getLogger(__name__)
+
+def main():
+    """Main function to generate CSV files for Hugging Face Space deployment"""
+    
+    print("=" * 60)
+    print("CSV Generation for Hugging Face Space Deployment")
+    print("=" * 60)
+    
+    # Check if CSV files already exist
+    print("\n1. Checking existing CSV files...")
+    csv_info = check_csv_data_availability()
+    
+    for data_type, info in csv_info.items():
+        if info['available']:
+            print(f"   ✓ {data_type.upper()}: {info['file']} ({info['records']} records, {info['size_mb']:.2f} MB)")
+            print(f"     Last modified: {info['modified']}")
+        else:
+            print(f"   ✗ {data_type.upper()}: {info['error']}")
+    
+    # Check data freshness
+    print("\n2. Checking data freshness...")
+    freshness_info = get_data_freshness_info()
+    
+    for data_type, info in freshness_info.items():
+        if data_type != 'error':
+            hours_old = info['hours_old']
+            is_fresh = info['is_fresh']
+            status = "FRESH" if is_fresh else "STALE"
+            print(f"   {data_type.upper()}: {hours_old:.1f} hours old ({status})")
+    
+    # Ask user if they want to proceed
+    print("\n3. Data generation options:")
+    print("   [1] Generate fresh data from API (recommended)")
+    print("   [2] Skip if CSV files are fresh (< 24 hours old)")
+    print("   [3] Exit without generating")
+    
+    choice = input("\nEnter your choice (1-3): ").strip()
+    
+    if choice == "3":
+        print("Exiting without generating CSV files.")
+        return
+    
+    elif choice == "2":
+        # Check if all files are fresh
+        all_fresh = True
+        for data_type, info in freshness_info.items():
+            if data_type != 'error' and not info.get('is_fresh', False):
+                all_fresh = False
+                break
+        
+        if all_fresh and csv_info['apr']['available'] and csv_info['roi']['available']:
+            print("All CSV files are fresh. No need to regenerate.")
+            return
+        else:
+            print("Some CSV files are missing or stale. Proceeding with generation...")
+    
+    # Generate fresh data
+    print("\n4. Fetching data from API...")
+    try:
+        df_apr, df_roi = fetch_apr_data_from_db()
+        
+        if df_apr.empty and df_roi.empty:
+            print("   ✗ No data fetched from API. Check your connection and API status.")
+            return
+        
+        print(f"   ✓ Fetched {len(df_apr)} APR records and {len(df_roi)} ROI records")
+        
+    except Exception as e:
+        print(f"   ✗ Error fetching data: {e}")
+        logger.exception("Error fetching data from API")
+        return
+    
+    # Apply preprocessing
+    print("\n5. Applying preprocessing...")
+    try:
+        if not df_apr.empty:
+            df_apr_processed = fix_apr_and_roi(df_apr)
+            print(f"   ✓ Processed APR data: {len(df_apr_processed)} records")
+        else:
+            df_apr_processed = df_apr
+            print("   ! No APR data to process")
+        
+        if not df_roi.empty:
+            df_roi_processed = df_roi  # ROI data is already processed in fetch function
+            print(f"   ✓ ROI data ready: {len(df_roi_processed)} records")
+        else:
+            df_roi_processed = df_roi
+            print("   ! No ROI data to process")
+            
+    except Exception as e:
+        print(f"   ✗ Error during preprocessing: {e}")
+        logger.exception("Error during preprocessing")
+        return
+    
+    # Save CSV files
+    print("\n6. Saving CSV files...")
+    csv_files_created = []
+    
+    try:
+        # Save APR data
+        if not df_apr_processed.empty:
+            apr_csv = save_to_csv(df_apr_processed)
+            if apr_csv:
+                csv_files_created.append(apr_csv)
+                print(f"   ✓ Saved APR data: {apr_csv}")
+                
+                # Also save statistics
+                stats_csv = "optimus_apr_statistics.csv"
+                if os.path.exists(stats_csv):
+                    csv_files_created.append(stats_csv)
+                    print(f"   ✓ Saved APR statistics: {stats_csv}")
+        
+        # Save ROI data
+        if not df_roi_processed.empty:
+            roi_csv = save_roi_to_csv(df_roi_processed)
+            if roi_csv:
+                csv_files_created.append(roi_csv)
+                print(f"   ✓ Saved ROI data: {roi_csv}")
+        
+        if not csv_files_created:
+            print("   ✗ No CSV files were created")
+            return
+            
+    except Exception as e:
+        print(f"   ✗ Error saving CSV files: {e}")
+        logger.exception("Error saving CSV files")
+        return
+    
+    # Summary
+    print("\n" + "=" * 60)
+    print("CSV GENERATION COMPLETE")
+    print("=" * 60)
+    
+    print(f"\nGenerated {len(csv_files_created)} CSV files:")
+    for csv_file in csv_files_created:
+        if os.path.exists(csv_file):
+            size_mb = os.path.getsize(csv_file) / (1024 * 1024)
+            print(f"   • {csv_file} ({size_mb:.2f} MB)")
+    
+    print(f"\nGeneration completed at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    
+    print("\nNext steps for Hugging Face Space deployment:")
+    print("1. Upload these CSV files to your Hugging Face Space repository")
+    print("2. Ensure your Space app.py imports and uses load_from_csv functions")
+    print("3. The app will prioritize CSV data over API calls, avoiding rate limits")
+    print("4. Re-run this script periodically to update the CSV files with fresh data")
+    
+    print("\nDeployment tips:")
+    print("• Add these CSV files to your Space's file list")
+    print("• Consider setting up a scheduled job to update CSV files regularly")
+    print("• Monitor your Space logs to ensure CSV loading works correctly")
+
+if __name__ == "__main__":
+    main()

load_from_csv.py

@@ -0,0 +1,251 @@
+"""
+CSV Loading Functions for Hugging Face Space Deployment
+This module provides functions to load preprocessed data from CSV files
+instead of making API calls, which helps avoid rate limiting issues.
+"""
+
+import pandas as pd
+import logging
+from datetime import datetime
+from typing import Tuple, Optional
+import os
+
+logger = logging.getLogger(__name__)
+
+def load_apr_data_from_csv() -> Tuple[pd.DataFrame, Optional[str]]:
+    """
+    Load APR data from CSV files.
+    
+    Returns:
+        Tuple of (DataFrame, csv_file_path) or (empty DataFrame, None) if files don't exist
+    """
+    csv_file = "optimus_apr_values.csv"
+    
+    try:
+        if not os.path.exists(csv_file):
+            logger.warning(f"APR CSV file not found: {csv_file}")
+            return pd.DataFrame(), None
+        
+        # Load the CSV file
+        df = pd.read_csv(csv_file)
+        
+        # Convert timestamp column back to datetime
+        if 'timestamp' in df.columns:
+            df['timestamp'] = pd.to_datetime(df['timestamp'])
+        
+        # Ensure proper data types
+        if 'apr' in df.columns:
+            df['apr'] = df['apr'].astype(float)
+        if 'adjusted_apr' in df.columns:
+            df['adjusted_apr'] = pd.to_numeric(df['adjusted_apr'], errors='coerce')
+        if 'agent_id' in df.columns:
+            df['agent_id'] = df['agent_id'].astype(str)
+        if 'is_dummy' in df.columns:
+            df['is_dummy'] = df['is_dummy'].astype(bool)
+        
+        logger.info(f"Successfully loaded {len(df)} APR records from {csv_file}")
+        
+        # Log data freshness
+        if not df.empty and 'timestamp' in df.columns:
+            latest_timestamp = df['timestamp'].max()
+            oldest_timestamp = df['timestamp'].min()
+            logger.info(f"APR data range: {oldest_timestamp} to {latest_timestamp}")
+            
+            # Check how fresh the data is
+            now = datetime.now()
+            if latest_timestamp.tzinfo is None:
+                # Make timezone-naive for comparison
+                now = now.replace(tzinfo=None)
+            
+            hours_old = (now - latest_timestamp).total_seconds() / 3600
+            logger.info(f"Latest APR data is {hours_old:.1f} hours old")
+        
+        return df, csv_file
+        
+    except Exception as e:
+        logger.error(f"Error loading APR data from CSV: {e}")
+        return pd.DataFrame(), None
+
+def load_roi_data_from_csv() -> Tuple[pd.DataFrame, Optional[str]]:
+    """
+    Load ROI data from CSV files.
+    
+    Returns:
+        Tuple of (DataFrame, csv_file_path) or (empty DataFrame, None) if files don't exist
+    """
+    csv_file = "optimus_roi_values.csv"
+    
+    try:
+        if not os.path.exists(csv_file):
+            logger.warning(f"ROI CSV file not found: {csv_file}")
+            return pd.DataFrame(), None
+        
+        # Load the CSV file
+        df = pd.read_csv(csv_file)
+        
+        # Convert timestamp column back to datetime
+        if 'timestamp' in df.columns:
+            df['timestamp'] = pd.to_datetime(df['timestamp'])
+        
+        # Ensure proper data types
+        if 'roi' in df.columns:
+            df['roi'] = df['roi'].astype(float)
+        if 'agent_id' in df.columns:
+            df['agent_id'] = df['agent_id'].astype(str)
+        if 'is_dummy' in df.columns:
+            df['is_dummy'] = df['is_dummy'].astype(bool)
+        
+        logger.info(f"Successfully loaded {len(df)} ROI records from {csv_file}")
+        
+        # Log data freshness
+        if not df.empty and 'timestamp' in df.columns:
+            latest_timestamp = df['timestamp'].max()
+            oldest_timestamp = df['timestamp'].min()
+            logger.info(f"ROI data range: {oldest_timestamp} to {latest_timestamp}")
+            
+            # Check how fresh the data is
+            now = datetime.now()
+            if latest_timestamp.tzinfo is None:
+                # Make timezone-naive for comparison
+                now = now.replace(tzinfo=None)
+            
+            hours_old = (now - latest_timestamp).total_seconds() / 3600
+            logger.info(f"Latest ROI data is {hours_old:.1f} hours old")
+        
+        return df, csv_file
+        
+    except Exception as e:
+        logger.error(f"Error loading ROI data from CSV: {e}")
+        return pd.DataFrame(), None
+
+def load_statistics_from_csv() -> pd.DataFrame:
+    """
+    Load statistics data from CSV file.
+    
+    Returns:
+        DataFrame with statistics or empty DataFrame if file doesn't exist
+    """
+    csv_file = "optimus_apr_statistics.csv"
+    
+    try:
+        if not os.path.exists(csv_file):
+            logger.warning(f"Statistics CSV file not found: {csv_file}")
+            return pd.DataFrame()
+        
+        # Load the CSV file
+        df = pd.read_csv(csv_file)
+        
+        logger.info(f"Successfully loaded statistics from {csv_file}")
+        return df
+        
+    except Exception as e:
+        logger.error(f"Error loading statistics from CSV: {e}")
+        return pd.DataFrame()
+
+def check_csv_data_availability() -> dict:
+    """
+    Check which CSV files are available and their basic info.
+    
+    Returns:
+        Dictionary with availability status and file info
+    """
+    files_info = {}
+    
+    # Check APR data
+    apr_file = "optimus_apr_values.csv"
+    if os.path.exists(apr_file):
+        try:
+            df = pd.read_csv(apr_file)
+            files_info['apr'] = {
+                'available': True,
+                'file': apr_file,
+                'records': len(df),
+                'size_mb': os.path.getsize(apr_file) / (1024 * 1024),
+                'modified': datetime.fromtimestamp(os.path.getmtime(apr_file))
+            }
+        except Exception as e:
+            files_info['apr'] = {'available': False, 'error': str(e)}
+    else:
+        files_info['apr'] = {'available': False, 'error': 'File not found'}
+    
+    # Check ROI data
+    roi_file = "optimus_roi_values.csv"
+    if os.path.exists(roi_file):
+        try:
+            df = pd.read_csv(roi_file)
+            files_info['roi'] = {
+                'available': True,
+                'file': roi_file,
+                'records': len(df),
+                'size_mb': os.path.getsize(roi_file) / (1024 * 1024),
+                'modified': datetime.fromtimestamp(os.path.getmtime(roi_file))
+            }
+        except Exception as e:
+            files_info['roi'] = {'available': False, 'error': str(e)}
+    else:
+        files_info['roi'] = {'available': False, 'error': 'File not found'}
+    
+    # Check statistics data
+    stats_file = "optimus_apr_statistics.csv"
+    if os.path.exists(stats_file):
+        try:
+            df = pd.read_csv(stats_file)
+            files_info['statistics'] = {
+                'available': True,
+                'file': stats_file,
+                'records': len(df),
+                'size_mb': os.path.getsize(stats_file) / (1024 * 1024),
+                'modified': datetime.fromtimestamp(os.path.getmtime(stats_file))
+            }
+        except Exception as e:
+            files_info['statistics'] = {'available': False, 'error': str(e)}
+    else:
+        files_info['statistics'] = {'available': False, 'error': 'File not found'}
+    
+    return files_info
+
+def get_data_freshness_info() -> dict:
+    """
+    Get information about how fresh the CSV data is.
+    
+    Returns:
+        Dictionary with freshness information
+    """
+    info = {}
+    
+    try:
+        # Check APR data freshness
+        apr_df, _ = load_apr_data_from_csv()
+        if not apr_df.empty and 'timestamp' in apr_df.columns:
+            latest_apr = apr_df['timestamp'].max()
+            now = datetime.now()
+            if latest_apr.tzinfo is None:
+                now = now.replace(tzinfo=None)
+            
+            hours_old = (now - latest_apr).total_seconds() / 3600
+            info['apr'] = {
+                'latest_data': latest_apr,
+                'hours_old': hours_old,
+                'is_fresh': hours_old < 24  # Consider fresh if less than 24 hours old
+            }
+        
+        # Check ROI data freshness
+        roi_df, _ = load_roi_data_from_csv()
+        if not roi_df.empty and 'timestamp' in roi_df.columns:
+            latest_roi = roi_df['timestamp'].max()
+            now = datetime.now()
+            if latest_roi.tzinfo is None:
+                now = now.replace(tzinfo=None)
+            
+            hours_old = (now - latest_roi).total_seconds() / 3600
+            info['roi'] = {
+                'latest_data': latest_roi,
+                'hours_old': hours_old,
+                'is_fresh': hours_old < 24  # Consider fresh if less than 24 hours old
+            }
+        
+    except Exception as e:
+        logger.error(f"Error checking data freshness: {e}")
+        info['error'] = str(e)
+    
+    return info

optimus_apr_statistics.csv

@@ -0,0 +1,18 @@
+agent_id,agent_name,total_points,apr_points,performance_points,real_apr_points,real_performance_points,avg_apr,avg_performance,max_apr,min_apr,avg_adjusted_apr,max_adjusted_apr,min_adjusted_apr,latest_timestamp
+111,furtek-gilje55,41,41,0,4,0,4.299586579767991,,132.09,-94.88,6.642506092362181,125.13,-0.5,2025-06-14 04:50:52
+112,vuzus-fazi89,38,38,0,1,0,-2.100231738154815,,1.0,-74.97,-0.0697329975030058,1.0,-31.43,2025-06-14 04:50:52
+115,yenot-zoncen49,59,59,0,22,0,6.496904819117842,,190.0,-96.36,9.748857520651566,182.41,-0.5,2025-06-14 04:50:52
+116,honji-hahi60,72,72,0,35,0,22.846921430806514,,159.56,-94.35,24.52348550109173,151.96,-0.5,2025-06-14 04:50:52
+117,furye-himkon89,44,44,0,7,0,10.555388593373978,,228.68,-96.79,12.588233703022667,221.09,-0.5,2025-06-14 04:50:52
+118,lonwus-patu86,48,48,0,11,0,62.60820345728928,,335.69,-82.06,63.23977379713219,333.22,-41.28,2025-06-14 04:50:52
+119,lonlim-zapgi60,44,44,0,7,0,34.4231033537116,,421.68,-97.95,35.56522333573912,410.8,-0.5,2025-06-14 04:50:52
+120,joyus-goson39,41,41,0,4,0,62.62124763568349,,2081.35,-99.55,64.59516944347087,2081.35,-32.34,2025-06-14 04:50:52
+121,cilwar-rimlu27,38,38,0,1,0,56.18564353858787,,2108.94,-4.2,56.22901845166416,2108.94,-0.5,2025-06-14 04:50:52
+122,tevi-kulo15,43,43,0,6,0,58.849670457498725,,1038.47,-89.79,59.937215935661435,1038.47,-0.5,2025-06-14 04:50:52
+123,cordron-yelku44,42,42,0,5,0,24.374324516006045,,381.81,-99.79,25.956333593464684,370.64,-0.5,2025-06-14 04:50:52
+126,tonvel-beeprel23,41,41,0,4,0,53.320657463987594,,978.68,-92.51,54.8419743967097,971.66,-0.5,2025-06-14 04:50:52
+127,lunel-luwus85,42,42,0,5,0,99.48560256891523,,1305.08,-99.74,100.99112520846289,1298.06,-0.5,2025-06-14 04:50:52
+128,kozu-hanfil63,41,41,0,4,0,5.213374066025682,,314.71,-97.82,7.491235297101682,310.11,-30.96,2025-06-14 04:50:52
+86,nusus-tayar25,37,37,0,0,0,44215.79035373322,,1635999.03,-0.5,-0.3366212568602806,1.0,-0.5,2025-06-14 04:50:52
+102,kelrus-muha52,37,37,0,0,0,-1.0078958214312357,,1.0,-60.4,0.6266466549790262,1.0,-0.5,2025-06-14 04:50:52
+ALL,All Agents,708,708,0,116,0,2341.654014114298,,1635999.03,-99.79,32.52177745491984,2108.94,-41.28,2025-06-14 04:50:52

optimus_roi_values.csv

@@ -0,0 +1,745 @@
+roi,timestamp,agent_id,agent_name,is_dummy,metric_type,apr,adjusted_apr
+-0.0002765562977782299,2025-05-15 21:37:27.000000,86,nusus-tayar25,False,ROI,,
+-0.0002765562977782299,2025-05-15 21:39:37.000000,86,nusus-tayar25,False,ROI,,
+-0.0002765562977782299,2025-05-15 21:40:02.000000,86,nusus-tayar25,False,ROI,,
+-0.0002765562977782299,2025-05-15 21:40:28.000000,86,nusus-tayar25,False,ROI,,
+-0.0002765562977782299,2025-05-15 21:36:35.000000,86,nusus-tayar25,False,ROI,,
+-0.0002765562977782299,2025-05-15 21:37:02.000000,86,nusus-tayar25,False,ROI,,
+-0.0002765562977782299,2025-05-15 21:39:10.000000,86,nusus-tayar25,False,ROI,,
+-0.0002765562977782299,2025-05-15 21:41:11.000000,86,nusus-tayar25,False,ROI,,
+-0.0002765562977782299,2025-05-15 21:41:55.000000,86,nusus-tayar25,False,ROI,,
+-0.0002765562977782299,2025-05-15 21:42:41.000000,86,nusus-tayar25,False,ROI,,
+-0.0002765562977782299,2025-05-15 21:43:24.000000,86,nusus-tayar25,False,ROI,,
+-0.0002765562977782299,2025-05-15 21:44:09.000000,86,nusus-tayar25,False,ROI,,
+-0.0002765562977782299,2025-05-15 21:44:51.000000,86,nusus-tayar25,False,ROI,,
+-0.0002765562977782299,2025-05-15 21:45:37.000000,86,nusus-tayar25,False,ROI,,
+-0.0002765562977782299,2025-05-15 21:46:20.000000,86,nusus-tayar25,False,ROI,,
+-0.00012649422506416652,2025-05-19 22:39:16.682649,86,nusus-tayar25,False,ROI,,
+-0.00010449205744944567,2025-05-19 22:47:57.660329,86,nusus-tayar25,False,ROI,,
+-0.00010449205744944567,2025-05-19 22:53:48.567618,86,nusus-tayar25,False,ROI,,
+-0.00012649422506416652,2025-05-19 22:39:37.060636,86,nusus-tayar25,False,ROI,,
+-0.00010449205744944567,2025-05-19 22:46:29.056289,86,nusus-tayar25,False,ROI,,
+-0.00010449205744944567,2025-05-19 22:51:30.269824,86,nusus-tayar25,False,ROI,,
+-0.00010449205744944567,2025-05-19 22:57:07.717697,86,nusus-tayar25,False,ROI,,
+-0.0011357601313007892,2025-05-23 19:05:51.628406,86,nusus-tayar25,False,ROI,,
+-0.0016374352988718366,2025-05-27 05:42:54.798079,86,nusus-tayar25,False,ROI,,
+-0.8666050938279419,2025-06-04 21:48:07.115718,86,nusus-tayar25,False,ROI,,
+-0.8666050938279419,2025-06-04 21:52:00.578098,86,nusus-tayar25,False,ROI,,
+-0.999999999999975,2025-06-05 01:55:15.033359,86,nusus-tayar25,False,ROI,,
+-0.1696775721419591,2025-06-05 18:27:57.578268,86,nusus-tayar25,False,ROI,,
+1.8675787957432175,2025-06-06 05:14:51.027065,86,nusus-tayar25,False,ROI,,
+1.8675787957432175,2025-06-06 03:39:44.505985,86,nusus-tayar25,False,ROI,,
+1.0420726955249058,2025-05-30 01:10:42.776945,102,kelrus-muha52,False,ROI,,
+1.0371385033661085,2025-05-30 16:11:26.814088,102,kelrus-muha52,False,ROI,,
+-0.20964670158400078,2025-06-03 19:28:54.348419,102,kelrus-muha52,False,ROI,,
+-0.6044365419107054,2025-06-04 12:35:13.593971,102,kelrus-muha52,False,ROI,,
+-0.6040367838477997,2025-06-04 14:25:41.343980,102,kelrus-muha52,False,ROI,,
+-0.5407000717190478,2025-06-06 13:40:55.755362,111,furtek-gilje55,False,ROI,,
+-0.5407000717190478,2025-06-06 14:42:37.722147,111,furtek-gilje55,False,ROI,,
+-0.7884286963057436,2025-06-10 22:35:07.125343,111,furtek-gilje55,False,ROI,,
+-0.9488224700727876,2025-06-12 21:50:15.066243,111,furtek-gilje55,False,ROI,,
+-0.36854758417016864,2025-06-05 20:46:51.369656,112,vuzus-fazi89,False,ROI,,
+-0.7496728527619763,2025-06-08 02:03:32.248014,112,vuzus-fazi89,False,ROI,,
+-0.5456503958801932,2025-06-06 06:06:41.681577,115,yenot-zoncen49,False,ROI,,
+-0.5456503958801932,2025-06-06 08:19:44.944148,115,yenot-zoncen49,False,ROI,,
+-0.5456503958801932,2025-06-06 09:23:09.755892,115,yenot-zoncen49,False,ROI,,
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