Workflow Automatisieren

from openai import OpenAI

client = OpenAI()

def classify_email(email_text):
    """Classify an email and suggest an action."""
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": """Classify emails into:
- urgent: needs immediate response
- support: customer support request
- sales: sales inquiry
- newsletter: promotional/newsletter
- internal: internal team communication

Respond with JSON: {"category": "...", "priority": 1-5, 
"suggested_action": "...", "summary": "..."}"""},
            {"role": "user", "content": email_text}
        ],
        response_format={"type": "json_object"}
    )
    return response.choices[0].message.content

def auto_reply(email_text, category):
    """Generate a context-appropriate auto-reply."""
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": f"""Write a professional 
reply for a {category} email. Be helpful and concise. 
Include that someone will follow up within 24 hours."""},
            {"role": "user", "content": email_text}
        ]
    )
    return response.choices[0].message.content

def summarize_document(document_text):
    """Extract key information from a document."""
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": """Analyze this document and 
extract:
1. A brief summary (2-3 sentences)
2. Key points (bullet list)
3. Action items (if any)
4. Important dates or deadlines

Format your response clearly with headers."""},
            {"role": "user", "content": document_text}
        ]
    )
    return response.choices[0].message.content

def process_invoice(invoice_text):
    """Extract structured data from an invoice."""
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": """Extract invoice data as JSON:
{"vendor": "", "invoice_number": "", "date": "", 
 "due_date": "", "items": [{"description": "", 
 "quantity": 0, "unit_price": 0}], 
 "total": 0, "currency": ""}"""},
            {"role": "user", "content": invoice_text}
        ],
        response_format={"type": "json_object"}
    )
    return response.choices[0].message.content

import csv
import json

def analyze_csv_data(file_path):
    """Read CSV data and generate AI-powered insights."""
    with open(file_path, 'r') as f:
        reader = csv.DictReader(f)
        data = list(reader)
    
    # Send a sample + summary to the AI
    sample = data[:10]  # First 10 rows as sample
    summary = {
        "total_rows": len(data),
        "columns": list(data[0].keys()) if data else [],
        "sample_data": sample
    }
    
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": """You are a data analyst. 
Analyze this dataset and provide:
1. Data quality assessment
2. Key patterns or trends
3. Anomalies or outliers
4. Actionable recommendations"""},
            {"role": "user", "content": json.dumps(summary, indent=2)}
        ]
    )
    return response.choices[0].message.content

def clean_and_transform(data, rules):
    """Apply AI-suggested cleaning rules to data."""
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": """Generate Python code to 
clean this data based on the rules provided. Return only 
executable Python code."""},
            {"role": "user", "content": f"Data sample: {data[:3]}\n\nRules: {rules}"}
        ]
    )
    return response.choices[0].message.content

import schedule
import time

def run_email_automation():
    """Check and process new emails every 15 minutes."""
    print("Checking for new emails...")
    # Your email processing logic here

def run_daily_report():
    """Generate and send daily summary report."""
    print("Generating daily report...")
    # Your reporting logic here

# Schedule automated tasks
schedule.every(15).minutes.do(run_email_automation)
schedule.every().day.at("09:00").do(run_daily_report)

print("Automation scheduler started...")
while True:
    schedule.run_pending()
    time.sleep(60)