Update app.py

app.py

@@ -1,6 +1,8 @@
+```python
 import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
+import os
 from datetime import datetime
 from model import predict_delay, get_weather_condition
 from utils import validate_inputs, generate_heatmap
@@ -14,13 +16,13 @@ from simple_salesforce import Salesforce
 # Streamlit app configuration
 st.set_page_config(page_title="Delay 🚀", layout="wide")
 
-# Salesforce connection (using Streamlit secrets)
+# Salesforce connection (using environment variables)
 try:
     sf = Salesforce(
-        username=st.secrets["SF_USERNAME"],
-        password=st.secrets["SF_PASSWORD"],
-        security_token=st.secrets["SF_SECURITY_TOKEN"],
-        instance_url=st.secrets["SF_INSTANCE_URL"]
+        username=os.environ.get("SF_USERNAME"),
+        password=os.environ.get("SF_PASSWORD"),
+        security_token=os.environ.get("SF_SECURITY_TOKEN"),
+        instance_url=os.environ.get("SF_INSTANCE_URL")
     )
 except Exception as e:
     st.error(f"Failed to connect to Salesforce: {str(e)}")
@@ -128,7 +130,6 @@ def save_to_salesforce(input_data, prediction):
             "Weather_Forecast_Date__c": input_data["weather_forecast_date"],
             "Delay_Probability__c": prediction["delay_probability"],
             "AI_Insights__c": prediction["ai_insights"],
-            # Store high_risk_phases as a formatted string
             "High_Risk_Phases__c": "; ".join(format_high_risk_phases(prediction["high_risk_phases"]))
         }
         # Create a new record in Delay_Predictor__c
@@ -220,4 +221,5 @@ if submit_button:
                 st.success("Prediction data successfully saved to Salesforce!")
             
             st.session_state.prediction = prediction
-            st.session_state.input_data = input_data
+            st.session_state.input_data = input_data
+```