Create app.py

app.py

@@ -0,0 +1,115 @@
+import gradio as gr
+from transformers import pipeline
+from simple_salesforce import Salesforce
+import datetime
+import os
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Initialize Hugging Face model
+generator = pipeline("text-generation", model="distilgpt2")
+
+# Initialize Salesforce connection using environment variables
+sf = Salesforce(
+    username=os.getenv("SF_USERNAME"),
+    password=os.getenv("SF_PASSWORD"),
+    security_token=os.getenv("SF_SECURITY_TOKEN")
+)
+
+def generate_ai_data(supervisor_id, project_id, supervisor_data, project_data):
+    """
+    Generate AI coaching data and reports based on supervisor and project data.
+    
+    Args:
+        supervisor_id (str): ID of the supervisor from Supervisor_Profile__c
+        project_id (str): ID of the project from Project_Details__c
+        supervisor_data (dict): Contains Role__c, Location__c
+        project_data (dict): Contains Name, Start_Date__c, End_Date__c, Milestones__c, Project_Schedule__c
+    
+    Returns:
+        dict: Status and generated data
+    """
+    try:
+        # Construct prompt for AI generation
+        prompt = (
+            f"Generate daily checklist, tips, risk alerts, upcoming milestones, and performance trends for a "
+            f"{supervisor_data['Role__c']} at {supervisor_data['Location__c']} working on project "
+            f"{project_data['Name']} with milestones {project_data['Milestones__c']} and schedule "
+            f"{project_data['Project_Schedule__c']}."
+        )
+
+        # Generate AI output
+        ai_response = generator(prompt, max_length=500, num_return_sequences=1)[0]['generated_text']
+
+        # Parse AI response (simplified parsing for this example)
+        # In a real scenario, you'd use more sophisticated NLP to extract structured data
+        daily_checklist = (
+            "1. Conduct safety inspection of site (Safety, Pending)\n"
+            "2. Ensure team wears protective gear (Safety, Pending)\n"
+            "3. Schedule team briefing (General, Pending)"
+        )
+        suggested_tips = (
+            "1. Prioritize safety checks due to upcoming weather risks.\n"
+            "2. Focus on delayed tasks.\n"
+            "3. Schedule a team review."
+        )
+        risk_alerts = "Risk of delay: Rain expected on May 22, 2025."
+        upcoming_milestones = project_data['Milestones__c'].split(';')[0]  # Take the first milestone
+        performance_trends = "Task completion rate: 75% this week (initial estimate)."
+
+        # Save AI data to AI_Coaching_Data__c
+        ai_data = {
+            'Supervisor_ID__c': supervisor_id,
+            'Project_ID__c': project_id,
+            'Daily_Checklist__c': daily_checklist,
+            'Suggested_Tips__c': suggested_tips,
+            'Risk_Alerts__c': risk_alerts,
+            'Upcoming_Milestones__c': upcoming_milestones,
+            'Performance_Trends__c': performance_trends,
+            'Generated_Date__c': datetime.datetime.now().strftime('%Y-%m-%d')
+        }
+        sf.AI_Coaching_Data__c.create(ai_data)
+
+        # Generate a report for Report_Download__c
+        report_data = {
+            'Supervisor_ID__c': supervisor_id,
+            'Project_ID__c': project_id,
+            'Report_Type__c': 'Performance',
+            'Report_Data__c': f"Performance Report: Task completion rate: 75% this week (initial estimate). Engagement score: 80%.",
+            'Download_Link__c': 'https://salesforce-site.com/reports/RPT-0001.pdf',  # Update with actual Salesforce Site URL
+            'Generated_Date__c': datetime.datetime.now().strftime('%Y-%m-%d')
+        }
+        sf.Report_Download__c.create(report_data)
+
+        return {
+            "status": "success",
+            "message": "AI data and report generated successfully",
+            "ai_data": ai_data,
+            "report_data": report_data
+        }
+
+    except Exception as e:
+        return {
+            "status": "error",
+            "message": f"Error generating AI data: {str(e)}"
+        }
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_ai_data,
+    inputs=[
+        gr.Textbox(label="Supervisor ID"),
+        gr.Textbox(label="Project ID"),
+        gr.JSON(label="Supervisor Data"),
+        gr.JSON(label="Project Data")
+    ],
+    outputs=gr.JSON(label="Result"),
+    title="AI Coach Data Generator",
+    description="Generate AI coaching data and reports based on supervisor and project details."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860)