Usage Examples

This document provides practical examples of using the Earning Robot.

Telegram Bot Examples
REST API Examples
CLI Examples
Python SDK Examples
Common Workflows

Telegram Bot Examples

Basic AI Queries

User: /ask What is machine learning?
Bot: 🤔 Processing your request...
Bot: 🤖 AI Response:

Machine learning is a subset of artificial intelligence that enables computer systems to learn and improve from experience without being explicitly programmed...

📊 Tokens used: 125 | Cost: $0.0003

Getting Status

User: /status
Bot: 🤖 Robot Status

📊 Tasks:
• Total: 150
• Completed: 145
• Failed: 2

👥 Users:
• Total: 25
• Active: 20

✅ System: Online
🕒 Time: 2025-01-15 10:30:00 UTC

Owner Reports

User: /report daily
Bot: 📊 Daily Report - 2025-01-15

💰 Income: $150.00
💸 Expenses: $45.50
📈 Profit: $104.50

Natural Language Interaction

User: Translate "Hello" to Spanish
Bot: 🤔 Processing your request...
Bot: 🤖 AI Response:

"Hello" in Spanish is "Hola".

📊 Tokens used: 15 | Cost: $0.00003

REST API Examples

Python

Execute AI Task

import requests

url = "http://localhost:5000/api/task"
data = {
    "prompt": "Explain blockchain technology",
    "provider": "openai",
    "user_id": "john@example.com"
}

response = requests.post(url, json=data)
result = response.json()

print(f"Response: {result['response']}")
print(f"Cost: ${result['cost']:.4f}")
print(f"Tokens: {result['tokens_used']}")

Get Financial Report

import requests

# Daily report
daily = requests.get("http://localhost:5000/api/report/daily").json()
print(f"Today's Profit: ${daily['profit']:.2f}")

# Weekly report
weekly = requests.get("http://localhost:5000/api/report/weekly").json()
print(f"Weekly Profit: ${weekly['profit']:.2f}")

# Monthly report
monthly = requests.get("http://localhost:5000/api/report/monthly").json()
print(f"Monthly Profit: ${monthly['profit']:.2f}")

Monitor Tasks

import requests
import time

# Create a task
response = requests.post("http://localhost:5000/api/task", json={
    "prompt": "Write a haiku about coding"
})
task_id = response.json()['task_id']

# Check task status
while True:
    task = requests.get(f"http://localhost:5000/api/task/{task_id}").json()
    
    if task['status'] == 'completed':
        print(f"Task completed!")
        print(f"Output: {task['output']}")
        break
    elif task['status'] == 'failed':
        print(f"Task failed!")
        break
    
    time.sleep(1)

Batch Processing

import requests
from concurrent.futures import ThreadPoolExecutor

def process_question(question):
    response = requests.post("http://localhost:5000/api/task", json={
        "prompt": question,
        "provider": "openai"
    })
    return response.json()

questions = [
    "What is Python?",
    "What is JavaScript?",
    "What is Go?",
    "What is Rust?"
]

with ThreadPoolExecutor(max_workers=4) as executor:
    results = executor.map(process_question, questions)
    
    for result in results:
        print(f"Q: {result['task_id']}")
        print(f"A: {result['response'][:100]}...")
        print()

JavaScript (Node.js)

const axios = require('axios');

// Execute task
async function askAI(prompt) {
  const response = await axios.post('http://localhost:5000/api/task', {
    prompt: prompt,
    provider: 'openai'
  });
  
  return response.data;
}

// Get statistics
async function getStats() {
  const response = await axios.get('http://localhost:5000/api/stats');
  return response.data;
}

// Main
(async () => {
  // Ask a question
  const result = await askAI('What is Node.js?');
  console.log('Answer:', result.response);
  console.log('Cost:', result.cost);
  
  // Get stats
  const stats = await getStats();
  console.log('Total Tasks:', stats.tasks.total);
  console.log('Total Profit:', stats.financials.profit);
})();

cURL

# Execute task
curl -X POST http://localhost:5000/api/task \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "What is REST API?",
    "provider": "openai"
  }'

# Get task
curl http://localhost:5000/api/task/1

# List completed tasks
curl "http://localhost:5000/api/tasks?status=completed&limit=5"

# Get daily report
curl http://localhost:5000/api/report/daily | jq .

# Get statistics
curl http://localhost:5000/api/stats | jq .

CLI Examples

# Run CLI
python cli.py

# You'll see:
# ==================================================
# 🤖 EARNING ROBOT - CLI
# ==================================================
# 1. Execute AI Task
# 2. View Tasks
# 3. View Financial Report
# 4. View Statistics
# 5. List Users
# 6. View Transactions
# 0. Exit
# ==================================================

# Choose option 1 to execute a task:
Select option: 1
Enter your question: What is the meaning of life?
Provider (openai/mistral) [openai]: openai

# Choose option 3 for reports:
Select option: 3
1. Daily
2. Weekly
3. Monthly
Select report type: 1

Python SDK Examples

Direct Database Access

from backend.database import Database, User, Task, Transaction
from backend.config import Config

# Initialize database
db = Database(Config.DATABASE_PATH).initialize()
session = db.get_session()

# Query users
users = session.query(User).filter_by(is_active=True).all()
for user in users:
    print(f"User: {user.email}, Subscription: {user.subscription_type}")

# Query tasks
from datetime import datetime, timedelta

yesterday = datetime.utcnow() - timedelta(days=1)
recent_tasks = session.query(Task).filter(
    Task.created_at >= yesterday
).all()

print(f"Tasks in last 24 hours: {len(recent_tasks)}")

session.close()

Custom AI Integration

from backend.ai_providers import AIManager
from backend.database import Database

ai_manager = AIManager()
db = Database().initialize()

# Use OpenAI
result = ai_manager.execute_task(
    "Explain Docker containers",
    provider='openai'
)
print(result['response'])

# Use Mistral
result = ai_manager.execute_task(
    "Explain Kubernetes",
    provider='mistral'
)
print(result['response'])

Custom Reporting

from billing.reporting import ReportGenerator
from backend.database import Database

db = Database().initialize()
session = db.get_session()

generator = ReportGenerator(session)

# Get custom date range
from datetime import date

summary = generator.get_daily_summary(date(2025, 1, 15))
print(f"Income on 2025-01-15: ${summary['income']}")

# Get category breakdown
breakdown = generator.get_category_breakdown(days=30)
print("Income sources:", breakdown['income_breakdown'])
print("Expense categories:", breakdown['expense_breakdown'])

session.close()

Common Workflows

Workflow 1: Set Up and Monitor

# 1. Start the robot
python main.py

# 2. In Telegram, send:
/start
/status

# 3. Ask your first question:
/ask What is artificial intelligence?

# 4. Check your first report:
/report daily

Workflow 2: Automate Daily Tasks

import requests
import schedule
import time

def daily_summary():
    """Get and print daily summary"""
    report = requests.get("http://localhost:5000/api/report/daily").json()
    print(f"📊 Daily Summary:")
    print(f"  Income: ${report['income']}")
    print(f"  Expenses: ${report['expenses']}")
    print(f"  Profit: ${report['profit']}")

def process_questions():
    """Process a batch of questions"""
    questions = [
        "Latest tech news summary",
        "Trending programming languages",
        "AI industry updates"
    ]
    
    for q in questions:
        result = requests.post("http://localhost:5000/api/task", json={
            "prompt": q
        }).json()
        print(f"Q: {q}")
        print(f"A: {result['response'][:200]}...")
        print()

# Schedule tasks
schedule.every().day.at("09:00").do(daily_summary)
schedule.every().day.at("10:00").do(process_questions)

while True:
    schedule.run_pending()
    time.sleep(60)

Workflow 3: Customer Self-Service

from flask import Flask, request, jsonify
import requests

app = Flask(__name__)
ROBOT_API = "http://localhost:5000"

@app.route('/chat', methods=['POST'])
def chat():
    """Customer chat endpoint"""
    user_message = request.json['message']
    user_id = request.json['user_id']
    
    # Forward to robot
    response = requests.post(f"{ROBOT_API}/api/task", json={
        "prompt": user_message,
        "user_id": user_id,
        "provider": "openai"
    })
    
    return jsonify(response.json())

@app.route('/subscribe', methods=['POST'])
def subscribe():
    """Create subscription"""
    email = request.json['email']
    
    response = requests.post(f"{ROBOT_API}/api/payment/subscription", json={
        "email": email
    })
    
    return jsonify(response.json())

if __name__ == '__main__':
    app.run(port=8000)

Advanced Examples

Custom Scheduler Tasks

from backend.scheduler import TaskScheduler
from apscheduler.triggers.cron import CronTrigger

class CustomScheduler(TaskScheduler):
    def custom_task(self):
        """Your custom scheduled task"""
        print("Running custom task!")
        # Your logic here
    
    def start(self):
        super().start()
        
        # Add custom schedule
        self.scheduler.add_job(
            self.custom_task,
            trigger=CronTrigger(hour=12, minute=0),
            id='custom_task',
            name='My Custom Task'
        )

# Use custom scheduler
scheduler = CustomScheduler()
scheduler.start()

Integration with Other Services

import requests
from backend.ai_providers import AIManager

# Slack integration example
def send_to_slack(message, webhook_url):
    requests.post(webhook_url, json={'text': message})

# Use robot to generate content
ai_manager = AIManager()
result = ai_manager.execute_task("Generate a motivational quote")

# Send to Slack
send_to_slack(
    f"Daily Quote: {result['response']}",
    "https://hooks.slack.com/services/YOUR/WEBHOOK/URL"
)

Tips and Best Practices

Rate Limiting: Add delays between API calls to avoid hitting rate limits
Error Handling: Always wrap API calls in try-except blocks
Cost Monitoring: Regularly check /api/stats to monitor costs
Batch Processing: Use concurrent requests for better performance
Caching: Cache frequently requested data to reduce costs
Testing: Use test mode or mock APIs during development

Support

For more examples and help:

Read the API Documentation
Check the README
Open an issue on GitHub

Provide feedback

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Usage Examples

Table of Contents

Telegram Bot Examples

Basic AI Queries

Getting Status

Owner Reports

Natural Language Interaction

REST API Examples

Python

Execute AI Task

Get Financial Report

Monitor Tasks

Batch Processing

JavaScript (Node.js)

cURL

CLI Examples

Python SDK Examples

Direct Database Access

Custom AI Integration

Custom Reporting

Common Workflows

Workflow 1: Set Up and Monitor

Workflow 2: Automate Daily Tasks

Workflow 3: Customer Self-Service

Advanced Examples

Custom Scheduler Tasks

Integration with Other Services

Tips and Best Practices

Support

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Usage Examples

Table of Contents

Telegram Bot Examples

Basic AI Queries

Getting Status

Owner Reports

Natural Language Interaction

REST API Examples

Python

Execute AI Task

Get Financial Report

Monitor Tasks

Batch Processing

JavaScript (Node.js)

cURL

CLI Examples

Python SDK Examples

Direct Database Access

Custom AI Integration

Custom Reporting

Common Workflows

Workflow 1: Set Up and Monitor

Workflow 2: Automate Daily Tasks

Workflow 3: Customer Self-Service

Advanced Examples

Custom Scheduler Tasks

Integration with Other Services

Tips and Best Practices

Support