Update app.py

app.py

@@ -40,7 +40,7 @@ except Exception as e:
     sf = None
 
 # File to store forecast data
-DATA_FILE = "forecast_data.csv"
+DATA_FILE = "/public/forecast_data.csv"
 
 def prepare_prophet_data(usage_series):
     end_date = datetime.now()
@@ -114,7 +114,6 @@ def validate_usage_series(usage_str):
 def generate_forecast_pdf(forecast_data: dict, daily_forecasts: pd.DataFrame, alert_status: list, current_stock: int, forecast_7: int, forecast_14: int, forecast_30: int, fig_daily: go.Figure, fig_alerts: go.Figure, usage_series: str) -> BytesIO:
     try:
         logger.info("Starting PDF generation")
-        # Validate inputs
         if not isinstance(forecast_data, dict) or not forecast_data:
             logger.error("Invalid forecast_data: Must be a non-empty dictionary")
             return None
@@ -139,7 +138,6 @@ def generate_forecast_pdf(forecast_data: dict, daily_forecasts: pd.DataFrame, al
         y_position = 9.5 * inch
         logger.info("Initialized PDF canvas")
 
-        # Basic Forecast Data
         logger.info("Writing forecast data")
         for key, value in forecast_data.items():
             display_key = key.replace('_', ' ').title()
@@ -147,7 +145,6 @@ def generate_forecast_pdf(forecast_data: dict, daily_forecasts: pd.DataFrame, al
             c.drawString(1 * inch, y_position, f"{display_key}: {value_str}")
             y_position -= 0.3 * inch
 
-        # Add Last 60 Days Usage
         y_position -= 0.3 * inch
         c.drawString(1 * inch, y_position, "Last 60 Days Usage (comma-separated):")
         y_position -= 0.3 * inch
@@ -160,7 +157,6 @@ def generate_forecast_pdf(forecast_data: dict, daily_forecasts: pd.DataFrame, al
         c.drawText(text_object)
         logger.info("Added usage series")
 
-        # Add Daily Forecast Values
         y_position -= 0.3 * inch
         c.drawString(1 * inch, y_position, "Daily Forecast Values (Next 30 Days):")
         y_position -= 0.3 * inch
@@ -174,7 +170,6 @@ def generate_forecast_pdf(forecast_data: dict, daily_forecasts: pd.DataFrame, al
         c.drawText(text_object)
         logger.info("Added daily forecast values")
 
-        # Add Threshold Alerts
         y_position -= 0.3 * inch
         c.drawString(1 * inch, y_position, "Threshold Alerts:")
         y_position -= 0.3 * inch
@@ -188,7 +183,6 @@ def generate_forecast_pdf(forecast_data: dict, daily_forecasts: pd.DataFrame, al
             y_position -= 0.3 * inch
         logger.info("Added threshold alerts")
 
-        # Add Daily Forecast Visualization Data
         y_position -= 0.3 * inch
         c.drawString(1 * inch, y_position, "Daily Forecast Visualization Data (Next 30 Days):")
         y_position -= 0.3 * inch
@@ -203,7 +197,6 @@ def generate_forecast_pdf(forecast_data: dict, daily_forecasts: pd.DataFrame, al
                 y_position = 10 * inch
         logger.info("Added daily forecast visualization data")
 
-        # Add Daily Forecast Visualization Image
         y_position -= 0.3 * inch
         if y_position < 4 * inch:
             c.showPage()
@@ -222,7 +215,6 @@ def generate_forecast_pdf(forecast_data: dict, daily_forecasts: pd.DataFrame, al
             c.drawString(1 * inch, y_position - 0.3 * inch, "Error: Could not include daily forecast visualization.")
         y_position -= 4.5 * inch
 
-        # Add Threshold Alerts Visualization Data
         if y_position < 2 * inch:
             c.showPage()
             c.setFont("Helvetica", 10)
@@ -244,7 +236,6 @@ def generate_forecast_pdf(forecast_data: dict, daily_forecasts: pd.DataFrame, al
                 y_position = 10 * inch
         logger.info("Added threshold alerts visualization data")
 
-        # Add Threshold Alerts Visualization Image
         y_position -= 0.3 * inch
         if y_position < 4 * inch:
             c.showPage()
@@ -298,11 +289,8 @@ def upload_pdf_to_salesforce(pdf_file: BytesIO, consumable_type: str, record_id:
         return None
 
 def save_forecast_data(consumable_type, usage_series, current_stock, daily_forecasts):
-    """Save usage series, current stock, and daily forecasts to CSV."""
     try:
-        # Convert usage series to string
         usage_str = ','.join(map(str, usage_series))
-        # Prepare forecast data
         forecast_data = {
             'consumable_type': [consumable_type],
             'usage_series': [usage_str],
@@ -311,10 +299,9 @@ def save_forecast_data(consumable_type, usage_series, current_stock, daily_forec
             'forecast_yhat': [daily_forecasts['yhat'].tolist()]
         }
         df = pd.DataFrame(forecast_data)
-        # Append to CSV or create new
         if os.path.exists(DATA_FILE):
             existing_df = pd.read_csv(DATA_FILE)
-            existing_df = existing_df[existing_df['consumable_type'] != consumable_type]  # Remove old data for this type
+            existing_df = existing_df[existing_df['consumable_type'] != consumable_type]
             df = pd.concat([existing_df, df], ignore_index=True)
         df.to_csv(DATA_FILE, index=False)
         logger.info(f"Saved forecast data for {consumable_type} to {DATA_FILE}")
@@ -322,7 +309,6 @@ def save_forecast_data(consumable_type, usage_series, current_stock, daily_forec
         logger.error(f"Error saving forecast data: {str(e)}")
 
 def load_forecast_data(consumable_type):
-    """Load previous forecast data for a consumable type."""
     try:
         if not os.path.exists(DATA_FILE):
             logger.warning(f"No forecast data file found at {DATA_FILE}")
@@ -334,7 +320,6 @@ def load_forecast_data(consumable_type):
             return None, None, None
         usage_series = [float(x) for x in row['usage_series'].iloc[0].split(',')]
         current_stock = float(row['current_stock'].iloc[0])
-        # Parse forecast data (stored as strings, need to eval safely)
         forecast_dates = eval(row['forecast_date'].iloc[0])
         forecast_yhat = eval(row['forecast_yhat'].iloc[0])
         daily_forecasts = pd.DataFrame({'ds': pd.to_datetime(forecast_dates), 'yhat': forecast_yhat})
@@ -344,7 +329,6 @@ def load_forecast_data(consumable_type):
         return None, None, None
 
 def process_forecast(consumable_type, usage_series, current_stock, is_automated=False):
-    """Process forecast for a given consumable type."""
     usage_list, error = validate_usage_series(','.join(map(str, usage_series)))
     if error:
         logger.error(error)
@@ -424,7 +408,7 @@ def process_forecast(consumable_type, usage_series, current_stock, is_automated=
             plot_bgcolor='rgba(0,0,0,0)',
             paper_bgcolor='rgba(0,0,0,0)',
             margin=dict(l=50, r=50, t=50, b=100)
-        )
+        ))
         st.plotly_chart(fig_daily, use_container_width=True)
 
         st.header("Threshold Alerts Visualization")
@@ -446,7 +430,7 @@ def process_forecast(consumable_type, usage_series, current_stock, is_automated=
             xaxis_title='Category',
             yaxis_title='Units',
             template='plotly_white'
-        )
+        ))
         st.plotly_chart(fig_alerts)
     else:
         alert_status = [current_stock < forecast for forecast in [forecast_7, forecast_14, forecast_30]]
@@ -475,7 +459,7 @@ def process_forecast(consumable_type, usage_series, current_stock, is_automated=
             plot_bgcolor='rgba(0,0,0,0)',
             paper_bgcolor='rgba(0,0,0,0)',
             margin=dict(l=50, r=50, t=50, b=100)
-        )
+        ))
         alert_data = pd.DataFrame({
             'Category': ['Current Stock', '7-Day Forecast', '14-Day Forecast', '30-Day Forecast'],
             'Units': [current_stock, forecast_7, forecast_14, forecast_30],
@@ -494,9 +478,8 @@ def process_forecast(consumable_type, usage_series, current_stock, is_automated=
             xaxis_title='Category',
             yaxis_title='Units',
             template='plotly_white'
-        )
+        ))
 
-    # Salesforce record creation with PDF upload
     if sf is not None:
         try:
             order_suggestions_text = f"7 Days: {max(0, forecast_7 - current_stock)} units, 14 Days: {max(0, forecast_14 - current_stock)} units, 30 Days: {max(0, forecast_30 - current_stock)} units"
@@ -547,33 +530,30 @@ def process_forecast(consumable_type, usage_series, current_stock, is_automated=
             logger.error(f"Error creating Salesforce record or uploading PDF: {e}", exc_info=True)
             if not is_automated:
                 st.error(f"Error saving to Salesforce: {str(e)}")
+            return None
 
     return daily_forecasts
 
 def automate_daily_forecast():
-    """Run daily forecast automation for all consumable types."""
     consumable_types = ['Filters', 'Reagents', 'Vials']
     for consumable_type in consumable_types:
         logger.info(f"Processing automated forecast for {consumable_type}")
-        # Load previous data
         usage_series, current_stock, prev_daily_forecasts = load_forecast_data(consumable_type)
         
         if usage_series is None or current_stock is None or prev_daily_forecasts is None:
             logger.warning(f"No previous data for {consumable_type}. Skipping automation.")
             continue
 
-        # Get 61st day forecast (first day of previous 30-day forecast)
-        next_day_usage = prev_daily_forecasts['yhat'].iloc[0]
-        # Update usage series: remove oldest day, append new day
+        # Shift usage series: Remove oldest day, append forecasted usage for today
+        next_day_usage = prev_daily_forecasts['yhat'].iloc[0]  # Forecasted usage for today
         usage_series = usage_series[1:] + [next_day_usage]
-        # Update current stock: subtract yesterday's forecasted usage
+        # Update stock: Subtract yesterday's forecasted usage
         yesterday_usage = prev_daily_forecasts['yhat'].iloc[0]
         current_stock = max(0, current_stock - yesterday_usage)
 
-        # Process forecast
+        # Generate new forecast with updated data
         daily_forecasts = process_forecast(consumable_type, usage_series, current_stock, is_automated=True)
         if daily_forecasts is not None:
-            # Save new data
             save_forecast_data(consumable_type, usage_series, current_stock, daily_forecasts)
             logger.info(f"Completed automated forecast for {consumable_type}")
         else: