Update app.py

app.py

@@ -2,15 +2,15 @@ import datetime
 import logging
 import sys
 import uuid
-import base64
 from pathlib import Path
 import csv
+import pandas as pd
 import gradio as gr
 from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
 from simple_salesforce import Salesforce
 
-# Logging setup
+# Configure logging
 logging.basicConfig(
     level=logging.INFO,
     format='%(asctime)s - %(levelname)s - %(message)s',
@@ -23,28 +23,26 @@ SALESFORCE_USERNAME = "vaneshdevarapalli866@agentforce.com"
 SALESFORCE_PASSWORD = "vanesh@331"
 SALESFORCE_SECURITY_TOKEN = "VRUVbBOdG0s9Q4xy0W6DB1Y6b"
 
-# Your exact Salesforce instance URL (from your org URL)
-SALESFORCE_INSTANCE_URL = "https://orgfarm-ef617b5a6b-dev-ed.develop.lightning.force.com"
-
+# Connect to Salesforce
 def connect_to_salesforce():
     try:
-        sf = Salesforce(
+        sf_instance = Salesforce(
             username=SALESFORCE_USERNAME,
             password=SALESFORCE_PASSWORD,
             security_token=SALESFORCE_SECURITY_TOKEN,
-            domain="login"  # initial login domain
+            domain="login"
         )
-        # Override instance URL explicitly with your org URL
-        sf.instance_url = SALESFORCE_INSTANCE_URL
-        sf.base_url = f"{sf.instance_url}/services/data/v{sf.sf_version}"
-        logger.info(f"Connected to Salesforce instance at {sf.instance_url}")
-        return sf
+        # Set your Salesforce instance URL here
+        sf_instance.instance_url = "https://yourinstance.salesforce.com"  # <-- Replace with your actual Salesforce instance URL
+        logger.info("Connected to Salesforce successfully.")
+        return sf_instance
     except Exception as e:
         logger.error(f"Salesforce connection failed: {e}")
         raise
 
 sf = connect_to_salesforce()
 
+# Choices
 equipment_choices = [
     "Bulldozer", "Crane", "Excavator", "Loader", "Forklift",
     "Backhoe", "Grader", "Scraper", "Dump Truck", "Roller"
@@ -57,6 +55,7 @@ project_choices = [
 
 ai_suggestion_choices = ["Move", "Pause Rent", "Repair", "Replace"]
 
+# Generate PDF report
 def generate_pdf_report(record_id, data_dict):
     report_id = str(uuid.uuid4())[:8]
     report_filename = f"report_{report_id}.pdf"
@@ -74,9 +73,12 @@ def generate_pdf_report(record_id, data_dict):
         y -= 20
 
     c.save()
-    logger.info(f"Generated PDF report at {report_path}")
-    return str(report_path)
 
+    # Upload to Salesforce and generate public URL for download
+    public_url = upload_file_to_salesforce(sf, str(report_path), report_filename, record_id)
+    return public_url
+
+# Generate CSV report
 def generate_csv_report(record_id, data_dict):
     report_id = str(uuid.uuid4())[:8]
     csv_filename = f"report_{report_id}.csv"
@@ -89,8 +91,10 @@ def generate_csv_report(record_id, data_dict):
         writer.writerow(["Salesforce Record ID", record_id])
         for k, v in data_dict.items():
             writer.writerow([k, v])
-    logger.info(f"Generated CSV report at {csv_path}")
-    return str(csv_path)
+
+    # Upload to Salesforce and generate public URL for download
+    public_url = upload_file_to_salesforce(sf, str(csv_path), csv_filename, record_id)
+    return public_url
 
 def upload_file_to_salesforce(sf, file_path, file_name, parent_record_id):
     with open(file_path, "rb") as f:
@@ -128,79 +132,98 @@ def upload_file_to_salesforce(sf, file_path, file_name, parent_record_id):
     logger.info(f"Uploaded file {file_name} to Salesforce with public URL: {public_url}")
     return public_url
 
-def call_ai_model(usage_hours, idle_hours):
-    total = usage_hours + idle_hours
-    ratio = usage_hours / total if total > 0 else 0
-    if ratio < 0.3:
-        return "Pause Rent", 0.7, ratio * 100
-    elif ratio < 0.6:
-        return "Move", 0.8, ratio * 100
-    elif ratio < 0.8:
-        return "Repair", 0.9, ratio * 100
-    else:
-        return "Replace", 0.95, ratio * 100
+def call_ai_model(usage_hours, idle_hours, movement_frequency, cost_per_hour, last_maintenance_str):
+    try:
+        total_time = usage_hours + idle_hours
+        utilization_ratio = usage_hours / total_time if total_time > 0 else 0
+
+        if utilization_ratio < 0.3:
+            suggestion = "Pause Rent"
+        elif utilization_ratio < 0.6:
+            suggestion = "Move"
+        elif utilization_ratio < 0.8:
+            suggestion = "Repair"
+        else:
+            suggestion = "Replace"
+
+        confidence = min(1.0, utilization_ratio + 0.1)
+        utilization_score = utilization_ratio * 100
+
+        logger.info(f"AI Model Prediction: suggestion={suggestion}, confidence={confidence:.2f}, utilization_score={utilization_score:.2f}")
+
+        return suggestion, confidence, utilization_score
+
+    except Exception as e:
+        logger.error(f"Error in AI model prediction: {e}")
+        return "No Action", 0.0, 0.0
 
 def process_equipment_utilization(equipment_name, project_name, usage_hours, idle_hours,
                                   movement_frequency, cost_per_hour, last_maintenance, ai_suggestion):
+    
     if not ai_suggestion:
-        ai_suggestion, confidence, utilization_score = call_ai_model(usage_hours, idle_hours)
+        last_maintenance_str = last_maintenance.strftime('%Y-%m-%d') if last_maintenance else ""
+        ai_suggestion, suggestion_confidence, utilization_score = call_ai_model(
+            usage_hours, idle_hours, movement_frequency, cost_per_hour, last_maintenance_str
+        )
     else:
-        confidence = 0.9
-        utilization_score = 85.0
+        suggestion_confidence = 0.9
+        utilization_score = 0.85
 
     summary_data = {
         "Equipment Name": equipment_name,
         "Project": project_name,
         "Usage Hours": usage_hours,
         "Idle Hours": idle_hours,
-        "Movement Frequency": movement_frequency,
+        "Suggestion": ai_suggestion,
+        "Confidence": suggestion_confidence,
+        "Utilization Score": utilization_score,
         "Cost per Hour": cost_per_hour,
-        "Last Maintenance": last_maintenance if last_maintenance else "N/A",
-        "AI Suggestion": ai_suggestion,
-        "Suggestion Confidence": confidence,
-        "Utilization Score": utilization_score
-    }
-
-    record_data = {
-        "Equipment_Name__c": equipment_name,
-        "Project_Name__c": project_name,
-        "Usage_Hours__c": usage_hours,
-        "Idle_Hours__c": idle_hours,
-        "Movement_Frequency__c": movement_frequency,
-        "Cost_per_Hour__c": cost_per_hour,
-        "AI_Suggestion__c": ai_suggestion,
-        "Suggestion_Confidence__c": confidence * 100,
-        "Utilization_Score__c": utilization_score,
-        "Last_Maintenance__c": last_maintenance,
-        "Report_Link__c": "Pending",
-        "CSV_Report_Link__c": "Pending"
+        "Last Maintenance": last_maintenance.strftime('%Y-%m-%d') if last_maintenance else "N/A"
     }
 
-    response = sf.Equipment_Utilization_Record__c.create(record_data)
-    record_id = response.get("id")
-    logger.info(f"Created Equipment Record with ID: {record_id}")
-
-    pdf_path = generate_pdf_report(record_id, summary_data)
-    csv_path = generate_csv_report(record_id, summary_data)
-
-    pdf_file_name = f"EquipmentReport_{record_id}.pdf"
-    csv_file_name = f"EquipmentReport_{record_id}.csv"
-
-    public_pdf_url = upload_file_to_salesforce(sf, pdf_path, pdf_file_name, record_id)
-    public_csv_url = upload_file_to_salesforce(sf, csv_path, csv_file_name, record_id)
-
-    sf.Equipment_Utilization_Record__c.update(record_id, {
-        "Report_Link__c": public_pdf_url,
-        "CSV_Report_Link__c": public_csv_url
-    })
-    logger.info(f"Updated Equipment Record {record_id} with report links")
-
-    return {
-        "Salesforce_Record_Id": record_id,
-        "PDF_Report_Link": public_pdf_url,
-        "CSV_Report_Link": public_csv_url,
-        "Summary": summary_data
-    }
+    try:
+        record_data = {
+            "Equipment_Name__c": equipment_name,
+            "Project_Name__c": project_name,
+            "Usage_Hours__c": usage_hours,
+            "Idle_Hours__c": idle_hours,
+            "AI_Suggestion__c": ai_suggestion,
+            "Suggestion_Confidence__c": suggestion_confidence * 100,
+            "Utilization_Score__c": utilization_score * 100,
+            "Cost_per_Hour__c": cost_per_hour,
+            "Report_Link__c": "Pending",
+            "Last_Maintenance__c": last_maintenance.strftime('%Y-%m-%d') if last_maintenance else None,
+            "Dashboard_Flag__c": False
+        }
+        logger.info(f"Creating Salesforce record with data: {record_data}")
+
+        response = sf.Equipment_Utilization_Record__c.create(record_data)
+        record_id = response.get("id")
+        logger.info(f"Successfully created Salesforce record with ID: {record_id}")
+
+        # Generate CSV and PDF and get URLs
+        pdf_url = generate_pdf_report(record_id, summary_data)
+        csv_url = generate_csv_report(record_id, summary_data)
+
+        sf.Equipment_Utilization_Record__c.update(record_id, {"Report_Link__c": pdf_url})
+        logger.info(f"Updated Salesforce record {record_id} with Report_Link__c: {pdf_url}")
+
+        # Convert datetime to string here to avoid JSON serialization error
+        summary_data["Last Maintenance"] = summary_data["Last Maintenance"] if isinstance(summary_data["Last Maintenance"], str) else summary_data["Last Maintenance"].strftime('%Y-%m-%d')
+
+        return {
+            "Salesforce_Record_Id": record_id,
+            "status": "Success",
+            "AI_Suggestion": ai_suggestion,
+            "Suggestion_Confidence": suggestion_confidence,
+            "Utilization_Score": utilization_score,
+            "Report_Link": pdf_url,
+            "CSV_Report_Link": csv_url,
+            "Summary": summary_data
+        }
+    except Exception as e:
+        logger.error(f"Error creating or updating Salesforce record: {e}")
+        return {"error": str(e)}
 
 def gradio_upload_process(equipment_name, project_name, usage_hours, idle_hours,
                          movement_frequency, cost_per_hour, last_maintenance, ai_suggestion):
@@ -210,12 +233,12 @@ def gradio_upload_process(equipment_name, project_name, usage_hours, idle_hours,
         movement_frequency = float(movement_frequency)
         cost_per_hour = float(cost_per_hour)
     except Exception as e:
-        return {"error": f"Invalid numeric input: {str(e)}"}
+        return {"error": f"Invalid numeric input: {str(e)}"}, None
 
     try:
-        last_maintenance_val = last_maintenance if last_maintenance else None
+        last_maintenance_dt = datetime.datetime.strptime(last_maintenance, "%Y-%m-%d") if last_maintenance else None
     except Exception as e:
-        return {"error": f"Invalid date format for Last Maintenance (expected YYYY-MM-DD): {str(e)}"}
+        return {"error": f"Invalid date format for Last Maintenance (expected YYYY-MM-DD): {str(e)}"}, None
 
     result = process_equipment_utilization(
         equipment_name,
@@ -224,48 +247,90 @@ def gradio_upload_process(equipment_name, project_name, usage_hours, idle_hours,
         idle_hours,
         movement_frequency,
         cost_per_hour,
-        last_maintenance_val,
+        last_maintenance_dt,
         ai_suggestion
     )
-    return result
+    return result, result.get("report_file_path")
 
 with gr.Blocks() as app:
-    gr.Markdown("## 📋 Equipment Utilization Record Creator")
-    gr.Markdown("Fill in the details below and submit to generate AI suggestions and save data to Salesforce.")
-
-    with gr.Row():
-        equipment_dropdown = gr.Dropdown(choices=equipment_choices, label="Equipment Name")
-        project_dropdown = gr.Dropdown(choices=project_choices, label="Project Name")
-        ai_suggestion_dropdown = gr.Dropdown(choices=[""] + ai_suggestion_choices, label="AI Suggestion (optional)")
-
-    with gr.Row():
-        usage_hours = gr.Number(label="Usage Hours", value=0, minimum=0)
-        idle_hours = gr.Number(label="Idle Hours", value=0, minimum=0)
-
-    with gr.Row():
-        movement_frequency = gr.Number(label="Movement Frequency", value=0, minimum=0)
-        cost_per_hour = gr.Number(label="Cost per Hour", value=0, minimum=0)
-
-    last_maintenance = gr.Textbox(label="Last Maintenance Date (YYYY-MM-DD)", placeholder="Optional")
-
-    submit_button = gr.Button("Submit")
-
-    output = gr.JSON(label="Salesforce Record Creation Result")
-
-    submit_button.click(
-        fn=gradio_upload_process,
-        inputs=[
-            equipment_dropdown,
-            project_dropdown,
-            usage_hours,
-            idle_hours,
-            movement_frequency,
-            cost_per_hour,
-            last_maintenance,
-            ai_suggestion_dropdown
-        ],
-        outputs=[output]
-    )
+    gr.Markdown("## 📋 Equipment Utilization Record Uploader")
+    gr.Markdown("Fill in the details below to generate AI suggestions and save them to Salesforce.")
+
+    with gr.Group():
+        with gr.Row():
+            equipment_dropdown = gr.Dropdown(choices=equipment_choices, label="🔧 Equipment Name", interactive=True)
+            project_dropdown = gr.Dropdown(choices=project_choices, label="🏍️ Project Name", interactive=True)
+            ai_suggestion_dropdown = gr.Dropdown(choices=[""] + ai_suggestion_choices, label="🧐 AI Suggestion (optional)", interactive=True)
+
+        gr.Markdown("---")
+
+        with gr.Row():
+            usage_hours = gr.Number(label="⏱️ Usage Hours", value=0, minimum=0)
+            idle_hours = gr.Number(label="🕒 Idle Hours", value=0, minimum=0)
+
+        with gr.Row():
+            movement_frequency = gr.Number(label="📈 Movement Frequency", value=0, minimum=0)
+            cost_per_hour = gr.Number(label="💰 Cost per Hour", value=0, minimum=0)
+
+        with gr.Row():
+            last_maintenance = gr.Textbox(label="🛠️ Last Maintenance Date (YYYY-MM-DD)", placeholder="Optional")
+
+        gr.Markdown("---")
+
+        with gr.Row():
+            submit_button = gr.Button("🚀 Submit", variant="primary")
+            output = gr.JSON(label="📄 Salesforce Record Creation Result")
+            report_file_output = gr.File(label="📃 Download PDF Report")
+
+        submit_button.click(
+            fn=gradio_upload_process,
+            inputs=[
+                equipment_dropdown, project_dropdown,
+                usage_hours, idle_hours,
+                movement_frequency, cost_per_hour,
+                last_maintenance, ai_suggestion_dropdown
+            ],
+            outputs=[output, report_file_output]
+        )
+
+        # CSV Upload
+        csv_upload = gr.File(label="📂 Upload CSV file", file_types=[".csv"])
+        csv_output = gr.JSON(label="📄 Batch Upload Results")
+
+        csv_upload.change(
+            fn=lambda file: process_csv_upload(file.name) if file else {},
+            inputs=csv_upload,
+            outputs=csv_output
+        )
+
+    app.css = """
+        .gradio-container {
+            background-color: #000000 !important;
+            padding: 20px;
+            color: #ffffff !important;
+        }
+        .gr-button-primary {
+            background-color: #1e90ff !important;
+            color: white !important;
+        }
+        .gr-button-primary:hover {
+            background-color: #0d6efd !important;
+        }
+        .gr-markdown {
+            font-size: 18px;
+            font-weight: 600;
+            color: #ffffff !important;
+        }
+        label {
+            font-weight: 500;
+            color: #ffffff !important;
+        }
+        input, textarea, select {
+            background-color: #1c1c1c !important;
+            color: #ffffff !important;
+            border: 1px solid #444 !important;
+        }
+    """
 
 if __name__ == "__main__":
     app.launch()