app.py

from flask import Flask, request, jsonify
from transformers import pipeline
from simple_salesforce import Salesforce
import json
import datetime

app = Flask(__name__)

# Initialize Hugging Face LLM for text generation
# Using distilgpt2 instead of distilbert-base-uncased
generator = pipeline('text-generation', model='distilgpt2')

# Salesforce credentials (replace with your actual credentials)
sf = Salesforce(
    username='your.email@example.com',  # Replace with your Salesforce username
    password='mypassword',              # Replace with your Salesforce password
    security_token='XXXXXXXXXXXXXXXXXXXXXXXX',  # Replace with your security token
    domain='test'  # Use 'login' for production org
)

# Helper function to generate AI content
def generate_ai_content(prompt):
    result = generator(prompt, max_length=100, num_return_sequences=1)
    return result[0]['generated_text']

# Endpoint to receive new project data from Salesforce
@app.route('/generate_coaching_data', methods=['POST'])
def generate_coaching_data():
    data = request.get_json()
    supervisor_id = data['supervisor_id']
    project_id = data['project_id']
    project_name = data['project_name']
    milestones = data['milestones']
    schedule = data['schedule']

    # Generate AI content
    checklist_prompt = f"Generate a daily checklist for a site supervisor working on {project_name} with milestones: {milestones}"
    tips_prompt = f"Generate daily tips for a site supervisor on {project_name}"
    risk_prompt = f"Identify risks for {project_name} with schedule: {schedule}"
    performance_prompt = f"Generate performance trends for {project_name}"
    upcoming_prompt = f"List upcoming milestones for {project_name}"

    checklist = generate_ai_content(checklist_prompt)
    tips = generate_ai_content(tips_prompt)
    risks = generate_ai_content(risk_prompt)
    performance = generate_ai_content(performance_prompt)
    upcoming = generate_ai_content(upcoming_prompt)

    # Simulate task filters (in real-world, you'd parse the checklist)
    task_all = checklist
    task_pending = f"Pending: {checklist[:50]}"
    task_completed = "Completed: None"
    task_safety = f"Safety: Ensure PPE compliance"
    task_high_priority = f"High Priority: {checklist[:30]}"
    completion_rate = 75  # Simulated

    # Store AI-generated data in Salesforce
    sf.AI_Coaching_Data__c.create({
        'Supervisor_ID__c': supervisor_id,
        'Project_ID__c': project_id,
        'Daily_Checklist__c': checklist,
        'Suggested_Tips__c': tips,
        'Risk_Alerts__c': risks,
        'Performance_Trends__c': performance,
        'Upcoming_Milestones__c': upcoming,
        'Completion_Rate__c': completion_rate,
        'Task_Filter_All__c': task_all,
        'Task_Filter_Pending__c': task_pending,
        'Task_Filter_Completed__c': task_completed,
        'Task_Filter_Safety__c': task_safety,
        'Task_Filter_High_Priority__c': task_high_priority
    })

    # Generate a report (simplified PDF link simulation)
    report_link = f"https://example.com/report_{project_id}.pdf"
    sf.Report_Download__c.create({
        'Supervisor_ID__c': supervisor_id,
        'Project_ID__c': project_id,
        'Download_Link__c': report_link,
        'Report_Type__c': 'Performance'
    })

    return jsonify({'status': 'success', 'message': 'Coaching data generated and stored'})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)