Update app.py

app.py

@@ -22,6 +22,7 @@ SALESFORCE_USERNAME = "vaneshdevarapalli866@agentforce.com"
 SALESFORCE_PASSWORD = "vanesh@331"
 SALESFORCE_SECURITY_TOKEN = "VRUVbBOdG0s9Q4xy0W6DB1Y6b"
 
+HUGGINGFACE_SPACE_URL = "https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME"
 HUGGINGFACE_API_URL = "https://your-huggingface-model-endpoint"
 
 # Connect to Salesforce
@@ -41,7 +42,6 @@ def connect_to_salesforce():
 
 sf = connect_to_salesforce()
 
-# Dropdown options
 equipment_choices = [
     "Bulldozer", "Crane", "Excavator", "Loader", "Forklift",
     "Backhoe", "Grader", "Scraper", "Dump Truck", "Roller"
@@ -54,12 +54,12 @@ project_choices = [
 
 ai_suggestion_choices = ["Move", "Pause Rent", "Repair", "Replace"]
 
-# Generate PDF report
+# Generate PDF and return public Hugging Face URL
 def generate_pdf_report(record_id, data_dict):
     report_id = str(uuid.uuid4())[:8]
     report_filename = f"report_{report_id}.pdf"
-    report_path = Path(f"reports/{report_filename}")
-    report_path.parent.mkdir(exist_ok=True)
+    report_path = Path(f"static/reports/{report_filename}")
+    report_path.parent.mkdir(parents=True, exist_ok=True)
 
     c = canvas.Canvas(str(report_path), pagesize=letter)
     c.setFont("Helvetica", 12)
@@ -72,9 +72,8 @@ def generate_pdf_report(record_id, data_dict):
         y -= 20
 
     c.save()
-    return str(report_path)
+    return f"{HUGGINGFACE_SPACE_URL}/file/static/reports/{report_filename}", str(report_path)
 
-# Call Hugging Face AI model
 def call_ai_model(usage_hours, idle_hours, movement_frequency, cost_per_hour, last_maintenance_str):
     try:
         payload = {
@@ -104,7 +103,6 @@ def call_ai_model(usage_hours, idle_hours, movement_frequency, cost_per_hour, la
         logger.error(f"Error calling AI model: {e}")
         return "No Action", 0.0, 0.0
 
-# Main process
 def process_equipment_utilization(equipment_name, project_name, usage_hours, idle_hours,
                                   movement_frequency, cost_per_hour, report_link, last_maintenance, ai_suggestion):
     last_maintenance_str = last_maintenance.strftime('%Y-%m-%d') if last_maintenance else None
@@ -144,9 +142,10 @@ def process_equipment_utilization(equipment_name, project_name, usage_hours, idl
             "Confidence": suggestion_confidence,
             "Utilization Score": utilization_score
         }
-        report_file_path = generate_pdf_report(record_id, summary_data)
 
-        sf.Equipment_Utilization_Record__c.update(record_id, {"Report_Link__c": report_file_path})
+        public_url, file_path = generate_pdf_report(record_id, summary_data)
+
+        sf.Equipment_Utilization_Record__c.update(record_id, {"Report_Link__c": public_url})
 
         return {
             "Salesforce_Record_Id": record_id,
@@ -154,14 +153,13 @@ def process_equipment_utilization(equipment_name, project_name, usage_hours, idl
             "AI_Suggestion": ai_suggestion,
             "Suggestion_Confidence": suggestion_confidence,
             "Utilization_Score": utilization_score,
-            "Report_Link": report_file_path,
-            "report_file_path": report_file_path
+            "Report_Link": public_url,
+            "report_file_path": file_path
         }
     except Exception as e:
         logger.error(f"Error creating or updating Salesforce record: {e}")
         return {"error": str(e)}
 
-# Gradio function
 def gradio_upload_process(equipment_name, project_name, usage_hours, idle_hours,
                          movement_frequency, cost_per_hour,
                          report_link, last_maintenance, ai_suggestion):
@@ -191,7 +189,6 @@ def gradio_upload_process(equipment_name, project_name, usage_hours, idle_hours,
     )
     return result, result.get("report_file_path")
 
-# UI
 with gr.Blocks() as app:
     gr.Markdown("## 📋 Equipment Utilization Record Uploader")
     gr.Markdown("Fill in the details below to generate AI suggestions and save them to Salesforce.")