Update app.py

app.py

@@ -2,7 +2,6 @@ import streamlit as st
 import pandas as pd
 from prophet import Prophet
 from datetime import datetime, timedelta
-import numpy as np
 
 # Prepare data for Prophet
 def prepare_prophet_data(usage_series):
@@ -13,19 +12,19 @@ def prepare_prophet_data(usage_series):
         'ds': dates,
         'y': usage_series
     })
-    prophet_df['cap'] = 60  # Max observed usage
+    prophet_df['cap'] = 30  # Lowered to 30
     prophet_df['floor'] = 0
     return prophet_df
 
-# Train or update Prophet model with user-provided usage series
+# Train or update Prophet model
 def train_model_with_usage(usage_series):
-    print("Training with changepoint_prior_scale=0.002, usage:", usage_series)  # Debug
+    print("Training with changepoint_prior_scale=0.001, usage:", usage_series)  # Debug to logs
     prophet_df = prepare_prophet_data(usage_series)
     model = Prophet(
         yearly_seasonality=False,
         weekly_seasonality=True,
         daily_seasonality=True,
-        changepoint_prior_scale=0.002,
+        changepoint_prior_scale=0.001,  # Lowered to 0.001
         growth='logistic'
     )
     model.fit(prophet_df)
@@ -34,11 +33,11 @@ def train_model_with_usage(usage_series):
 # Function to make forecasts
 def make_forecast(model, periods):
     future = model.make_future_dataframe(periods=periods)
-    future['cap'] = 60
+    future['cap'] = 30
     future['floor'] = 0
     forecast = model.predict(future)
     daily_forecasts = forecast['yhat'].tail(periods).tolist()
-    print(f"Daily forecasts for {periods} days:", [round(y) for y in daily_forecasts])  # Debug
+    print(f"Daily forecasts for {periods} days:", [round(y) for y in daily_forecasts])  # Debug to logs
     return round(sum(max(0, y) for y in daily_forecasts))  # Clip negative values
 
 # Function to validate input
@@ -70,7 +69,7 @@ def main():
         if error:
             st.error(error)
             return
-        st.write("Debug: Input usage series:", usage_list)  # Debug
+        print("Input usage series:", usage_list)  # Debug to logs
         
         try:
             model = train_model_with_usage(usage_list)