Update app.py

app.py

@@ -4,6 +4,10 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import os
 from datetime import datetime
+import json
+import requests
+import base64
+import logging
 from model import predict_delay, get_weather_condition
 from utils import validate_inputs, generate_heatmap
 from reportlab.lib.pagesizes import letter
@@ -12,10 +16,6 @@ from reportlab.lib.styles import getSampleStyleSheet
 from reportlab.lib.units import inch
 from io import BytesIO
 from simple_salesforce import Salesforce
-import base64
-import logging
-import json
-import requests
 
 # Configure logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -28,7 +28,7 @@ st.set_page_config(page_title="Delay 🚀", layout="wide")
 try:
     sf_instance_url = os.environ.get("SF_INSTANCE_URL")
     if not sf_instance_url:
-        raise ValueError("SF_INSTANCE_URL environment variable is not set")
+        raise ValueError("SF_INSTANCE_URL environment variable not set")
     if "lightning.force.com" in sf_instance_url:
         logger.warning("SF_INSTANCE_URL contains lightning.force.com; consider using my.salesforce.com for reliable PDF downloads")
     sf = Salesforce(
@@ -197,7 +197,7 @@ def save_to_salesforce(input_data, prediction, pdf_buffer):
     if sf is None:
         return "Salesforce connection not established."
     try:
-        # Prepare data for Project_Delay_Predictor__c object
+        # Prepare data for Delay_Predictor__c object
         sf_data = {
             "Project_Name__c": input_data["project_name"],
             "Phase__c": input_data["phase"],
@@ -216,13 +216,17 @@ def save_to_salesforce(input_data, prediction, pdf_buffer):
             "AI_Insights__c": prediction["ai_insights"],
             "High_Risk_Phases__c": "; ".join(format_high_risk_phases(prediction["high_risk_phases"]))
         }
-        # Create a new record in Project_Delay_Predictor__c
-        result = sf.Project_Delay_Predictor__c.create(sf_data)
+        logger.info(f"Attempting to save to Salesforce Delay_Predictor__c: {sf_data}")
+        
+        # Create a new record in Delay_Predictor__c
+        result = sf.Delay_Predictor__c.create(sf_data)
         if not result["success"]:
+            logger.error(f"Salesforce save failed: {result['errors']}")
             return f"Salesforce save failed: {result['errors']}"
 
         # Get the record ID
         record_id = result["id"]
+        logger.info(f"Created Salesforce record ID: {record_id}")
 
         # Upload PDF as ContentVersion
         pdf_data = pdf_buffer.getvalue()
@@ -235,6 +239,7 @@ def save_to_salesforce(input_data, prediction, pdf_buffer):
         }
         cv_result = sf.ContentVersion.create(content_version)
         if not cv_result["success"]:
+            logger.error(f"Failed to upload PDF to Salesforce: {cv_result['errors']}")
             return f"Failed to upload PDF to Salesforce: {cv_result['errors']}"
 
         # Get the ContentVersion ID
@@ -252,8 +257,8 @@ def save_to_salesforce(input_data, prediction, pdf_buffer):
         pdf_url = f"{sf_instance_url}/sfc/servlet.shepherd/document/download/{content_document_id}"
         logger.info(f"Generated PDF URL: {pdf_url}")
 
-        # Update the Project_Delay_Predictor__c record with the PDF URL
-        update_result = sf.Project_Delay_Predictor__c.update(record_id, {"PDF_Report__c": pdf_url})
+        # Update the Delay_Predictor__c record with the PDF URL
+        update_result = sf.Delay_Predictor__c.update(record_id, {"PDF_Report__c": pdf_url})
         if update_result != 204:
             logger.error(f"Failed to update PDF_Report__c with URL: {pdf_url}")
             return f"Failed to update PDF_Report__c field: {update_result}"
@@ -326,7 +331,7 @@ if submit_button:
             else:
                 input_data["weather_impact_score"] = weather_data["weather_impact_score"]
                 input_data["weather_condition"] = weather_data["weather_condition"]
-                st.write(f"**Weather Data for {project_location} on {input_data['weather_forecast_date']}:**")
+                st.write(f"**Weather Data for {project_location} on {input_data['weather_forecast_date']}**:")
                 st.write(f"- Condition: {weather_data['weather_condition']}")
                 st.write(f"- Impact Score: {weather_data['weather_impact_score']}")
                 st.write(f"- Temperature: {weather_data['temperature']}°C")