Python for AI Fundamentals

Learn Python programming essentials for AI development. Build your first AI-powered applications with hands-on coding experience.
Tier: Intermediate
Difficulty: Intermediate

Overview

Learn Python programming essentials for AI development. Build your first AI-powered applications with hands-on coding experience.

Learning Objectives

• Understand Python basics relevant to AI development
• Learn to work with AI libraries (requests, openai, pandas)
• Create your first AI-powered Python script
• Handle API responses and data processing effectively
• Set up a proper development environment for AI projects

Prerequisites

• Basic understanding of AI concepts from beginner tier
• Familiarity with using ChatGPT or similar AI tools
• Computer with internet access for coding practice

---

Introduction to Python for AI

Why Python for AI Development?

Python has become the go-to language for AI development, and for good reason. Let's explore why:

Python's AI Advantages:

• Simple Syntax: Easy to read and write, perfect for beginners
• Rich Libraries: Thousands of AI-focused libraries available
• Community Support: Massive community with tutorials and help
• Industry Standard: Used by Google, OpenAI, and major AI companies

The AI Development Ecosystem

When building AI applications, you'll work with several key components:

Core Libraries:

• openai - OpenAI API integration
• requests - HTTP requests
• pandas - Data manipulation
• json - JSON data handling

Development Tools:

• pip - Package installer
• venv - Virtual environments
• VS Code - Code editor
• Terminal - Command line

What You'll Build Today

By the end of this lesson, you'll create a Python script that:

• ✅ Connects to the OpenAI API
• ✅ Processes user input
• ✅ Generates AI responses
• ✅ Handles errors gracefully

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Python Essentials for AI

Essential Python Concepts

Let's cover the Python fundamentals you'll need for AI development:

1. Variables and Data Types

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Technical Implementation: ```python

Variables store data

api_key = "your-api-key-here"
model_name = "gpt-4o-mini"
temperature = 0.7
max_tokens = 150

Common data types for AI

user_message = "Hello, AI!"

String

token_count = 100

Integer

is_streaming = True

Boolean

response_data = {}

Dictionary

### 2. Functions - Your AI Building Blocks

```python
def generate_response(prompt, temperature=0.7):
    """Generate AI response from a prompt"""
    try:

# Your AI code here
        response = call_openai_api(prompt, temperature)
        return response
    except Exception as e:
        return f"Error: {str(e)}"

# Using the function
result = generate_response("Explain Python in simple terms")
print(result)

3. Error Handling - Essential for AI Apps

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Technical Implementation: ```python
try:

Code that might fail

response = openai.ChatCompletion.create(...)
print(response.choices[0].message.content)
except openai.AuthenticationError:
print("❌ Invalid API key")
except openai.RateLimitError:
print("⏳ Rate limit exceeded, please wait")
except Exception as e:
print(f"🚨 Unexpected error: {e}")

### Pro Tips for AI Development
- Use descriptive variable names: `user_prompt` instead of `p`
- Add comments: Explain complex AI logic
- Handle errors: AI APIs can fail, be prepared
- Test frequently: Small changes, frequent testing

---

## Working with AI Libraries

### Essential Libraries for AI Development

Let's learn how to work with the key Python libraries for AI projects:

### Installing Libraries with pip

```bash

# Install essential AI libraries
pip install openai requests pandas python-dotenv

# Create a virtual environment (recommended)
python -m venv ai_env
source ai_env/bin/activate

# On Windows: ai_env\Scripts\activate

Importing and Using Libraries

import openai
import requests
import pandas as pd
import json
from dotenv import load_dotenv
import os

# Load environment variables
load_dotenv()
openai.api_key = os.getenv('OPENAI_API_KEY')

Making HTTP Requests

import requests

# Example: Making a request to any API
headers = {
    'Authorization': 'Bearer your-api-key',
    'Content-Type': 'application/json'
}

data = {
    'model': 'gpt-4o-mini',
    'messages': [{'role': 'user', 'content': 'Hello!'}]
}

response = requests.post(
    'https://api.openai.com/v1/chat/completions',
    headers=headers,
    json=data
)

if response.status_code == 200:
    result = response.json()
    print(result['choices'][0]['message']['content'])
else:
    print(f"Error: {response.status_code}")

Working with Data (Pandas)

import pandas as pd

# Create a DataFrame to store AI responses
data = {
    'prompt': ['Explain AI', 'Write a poem', 'Solve math problem'],
    'response': ['AI is...', 'Roses are red...', '2+2=4'],
    'tokens': [50, 30, 10]
}

df = pd.DataFrame(data)
print(df.head())

# Save to CSV for analysis
df.to_csv('ai_responses.csv', index=False)

Common Installation Issues

• Permission errors: Use pip install --user
• Version conflicts: Use virtual environments
• PATH issues: Restart terminal after installation
• Python version: Use Python 3.8+ for AI libraries

---

Making Your First API Call

Connecting to AI APIs

Let's create your first working AI application by connecting to the OpenAI API:

Setting Up Authentication

First, create a .env file in your project directory:

# .env file
OPENAI_API_KEY=your-actual-api-key-here

Complete Working Example

# ai_assistant.py
import openai
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()
openai.api_key = os.getenv('OPENAI_API_KEY')

def ask_ai(question, temperature=0.7):
    """Ask AI a question and get a response"""
    try:
        response = openai.ChatCompletion.create(
            model="gpt-4o-mini",
            messages=[
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": question}
            ],
            temperature=temperature,
            max_tokens=150
        )

        return response.choices[0].message.content

    except openai.AuthenticationError:
        return "❌ Authentication failed. Check your API key."
    except openai.RateLimitError:
        return "⏳ Rate limit exceeded. Please wait a moment."
    except Exception as e:
        return f"🚨 Error: {str(e)}"

# Test the function
if __name__ == "__main__":
    user_question = input("Ask me anything: ")
    answer = ask_ai(user_question)
    print(f"\nAI: {answer}")

Enhanced Version with Features

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Technical Implementation: ```python
def enhanced_ai_chat():
"""Enhanced AI chat with conversation history"""
conversation = [
{"role": "system", "content": "You are a helpful Python programming assistant."}
]

print("🤖 AI Python Assistant (type 'quit' to exit)")

while True:
    user_input = input("\nYou: ")

    if user_input.lower() == 'quit':
        print("Goodbye! 👋")
        break

Add user message to conversation

    conversation.append({"role": "user", "content": user_input})

    try:
        response = openai.ChatCompletion.create(
            model="gpt-4o-mini",
            messages=conversation,
            temperature=0.7,
            max_tokens=200
        )

        ai_response = response.choices[0].message.content
        conversation.append({"role": "assistant", "content": ai_response})

        print(f"🤖 AI: {ai_response}")

    except Exception as e:
        print(f"🚨 Error: {str(e)}")

Run the enhanced chat

enhanced_ai_chat()

### Try It Yourself!

Copy the code above and:
1. Get your OpenAI API key from [OpenAI Platform](https://platform.openai.com/api-keys)
2. Create a `.env` file with your API key
3. Run the script and start chatting!
4. Try different questions and see how the AI responds

---

## Mastering the OpenAI Library

### Deep Dive into OpenAI Python Library

Now let's explore the powerful features of the OpenAI Python library:

### Understanding Model Parameters

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Technical Implementation: ```python
def create_ai_response(prompt, creativity="balanced"):
    """Create AI response with different creativity levels"""

# Temperature settings for different creativity levels
    temperature_settings = {
        "conservative": 0.1,

# More focused, deterministic
        "balanced": 0.7,

# Good balance
        "creative": 1.0

# More random, creative
    }

    response = openai.ChatCompletion.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}],
        temperature=temperature_settings[creativity],
        max_tokens=200,
        top_p=1.0,

# Controls diversity
        presence_penalty=0.0,

# Reduces repetition
        frequency_penalty=0.0

# Reduces repetition
    )

    return response.choices[0].message.content

# Test different creativity levels
prompt = "Write a short story about a robot learning to paint"
print("Conservative:", create_ai_response(prompt, "conservative"))
print("Creative:", create_ai_response(prompt, "creative"))

System Messages for Better Control

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Technical Implementation: ```python
def specialized_ai_assistant(user_question, assistant_type="general"):
"""Create specialized AI assistants with system messages"""

system_messages = {
    "python_tutor": "You are a Python programming tutor. Explain concepts clearly with examples.",
    "code_reviewer": "You are a code reviewer. Provide constructive feedback and suggestions.",
    "creative_writer": "You are a creative writing assistant. Help with storytelling and ideas.",
    "general": "You are a helpful and knowledgeable assistant."
}

messages = [
    {"role": "system", "content": system_messages[assistant_type]},
    {"role": "user", "content": user_question}
]

response = openai.ChatCompletion.create(
    model="gpt-4o-mini",
    messages=messages,
    temperature=0.7
)

return response.choices[0].message.content

Examples of specialized assistants

print("Python Tutor:", specialized_ai_assistant("How do I use loops?quot;, "python_tutor"))
print("Code Reviewer:", specialized_ai_assistant("Review this code: print('hello')", "code_reviewer"))

### Streaming Responses

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Technical Implementation: ```python
def stream_ai_response(prompt):
    """Stream AI response word by word"""

    response = openai.ChatCompletion.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}],
        stream=True

# Enable streaming
    )

    print("AI: ", end="")
    for chunk in response:
        if chunk.choices[0].delta.get("content"):
            print(chunk.choices[0].delta.content, end="")
    print()

# New line at the end

# Try streaming
stream_ai_response("Explain machine learning in simple terms")

Token Usage Tracking

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Technical Implementation: ```python
def track_token_usage(prompt):
"""Track API usage and costs"""

response = openai.ChatCompletion.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": prompt}],
    temperature=0.7
)

Extract usage information

usage = response.usage
content = response.choices[0].message.content

print(f"Response: {content}")
print(f"\n📊 Usage Stats:")
print(f"Prompt tokens: {usage.prompt_tokens}")
print(f"Response tokens: {usage.completion_tokens}")
print(f"Total tokens: {usage.total_tokens}")

Approximate cost calculation (prices may vary)

cost_per_1k_tokens = 0.001

Example rate

estimated_cost = (usage.total_tokens / 1000) * cost_per_1k_tokens
print(f"Estimated cost: {estimated_cost:.4f}")

Track usage

track_token_usage("Explain quantum computing")

### Best Practices Checklist

- ✅ Use environment variables for API keys
- ✅ Implement proper error handling
- ✅ Use appropriate temperature settings
- ✅ Track token usage to manage costs
- ✅ Use system messages for better control
- ✅ Test with small examples first

---

## Building Your First AI Application

### Project: Personal AI Writing Assistant

Let's build a complete AI application that demonstrates everything you've learned:

### Project Overview

We'll create a writing assistant that can:
- ✅ Help brainstorm ideas
- ✅ Improve existing text
- ✅ Check grammar and style
- ✅ Save conversation history
- ✅ Track usage statistics

### Project Structure

writing_assistant/
├── .env

API key

├── main.py

Main application

├── ai_helper.py

AI functions

├── utils.py

Utility functions

└── history.json

Conversation history

### Complete Implementation

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Technical Implementation: ```python

# ai_helper.py
import openai
import os
from dotenv import load_dotenv
import json
from datetime import datetime

load_dotenv()
openai.api_key = os.getenv('OPENAI_API_KEY')

class WritingAssistant:
    def __init__(self):
        self.history = []
        self.load_history()

    def brainstorm(self, topic):
        """Generate creative ideas for a topic"""
        prompt = f"Generate 5 creative ideas for writing about: {topic}"
        return self._get_ai_response(prompt, "creative")

    def improve_text(self, text):
        """Improve existing text"""
        prompt = f"Improve this text while maintaining the original meaning:\n\n{text}"
        return self._get_ai_response(prompt, "balanced")

    def grammar_check(self, text):
        """Check grammar and style"""
        prompt = f"Check grammar and style, provide corrections:\n\n{text}"
        return self._get_ai_response(prompt, "conservative")

    def _get_ai_response(self, prompt, creativity="balanced"):
        """Get AI response with error handling"""
        temperature_map = {
            "conservative": 0.2,
            "balanced": 0.7,
            "creative": 1.0
        }

        try:
            response = openai.ChatCompletion.create(
                model="gpt-4o-mini",
                messages=[
                    {"role": "system", "content": "You are a professional writing assistant."},
                    {"role": "user", "content": prompt}
                ],
                temperature=temperature_map[creativity],
                max_tokens=300
            )

            result = response.choices[0].message.content

# Save to history
            self.history.append({
                "timestamp": datetime.now().isoformat(),
                "prompt": prompt,
                "response": result,
                "tokens": response.usage.total_tokens
            })

            self.save_history()
            return result

        except Exception as e:
            return f"Error: {str(e)}"

    def save_history(self):
        """Save conversation history"""
        with open('history.json', 'w') as f:
            json.dump(self.history, f, indent=2)

    def load_history(self):
        """Load conversation history"""
        try:
            with open('history.json', 'r') as f:
                self.history = json.load(f)
        except FileNotFoundError:
            self.history = []

    def get_stats(self):
        """Get usage statistics"""
        total_requests = len(self.history)
        total_tokens = sum(item['tokens'] for item in self.history)

        return {
            "total_requests": total_requests,
            "total_tokens": total_tokens,
            "estimated_cost": total_tokens * 0.001 / 1000
        }

Main Application

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Technical Implementation: ```python

main.py

from ai_helper import WritingAssistant

def main():
assistant = WritingAssistant()

print("🖊️  Personal AI Writing Assistant")
print("Commands: brainstorm, improve, grammar, stats, quit")

while True:
    print("\n" + "="*50)
    command = input("Choose command: ").lower()

    if command == 'quit':
        print("Goodbye! 👋")
        break

    elif command == 'brainstorm':
        topic = input("Enter topic: ")
        result = assistant.brainstorm(topic)
        print(f"\n💡 Ideas:\n{result}")

    elif command == 'improve':
        text = input("Enter text to improve: ")
        result = assistant.improve_text(text)
        print(f"\n✨ Improved version:\n{result}")

    elif command == 'grammar':
        text = input("Enter text to check: ")
        result = assistant.grammar_check(text)
        print(f"\n✅ Grammar check:\n{result}")

    elif command == 'stats':
        stats = assistant.get_stats()
        print(f"\n📊 Usage Statistics:")
        print(f"Total requests: {stats['total_requests']}")
        print(f"Total tokens: {stats['total_tokens']}")
        print(f"Estimated cost: {stats['estimated_cost']:.4f}")

    else:
        print("Unknown command. Try: brainstorm, improve, grammar, stats, quit")

if name == "main":
main()

### Your Mission

Set up and run this writing assistant:
1. Create the project directory and files
2. Add your OpenAI API key to the .env file
3. Install required libraries: `pip install openai python-dotenv`
4. Run the application: `python main.py`
5. Test all features and see your usage stats!

### Enhancement Ideas

Once you have the basic version working, try adding:
- 📱 Web interface using Flask or Streamlit
- 💾 Database storage for better history management
- 🎨 Different writing styles (formal, casual, academic)
- 📊 Better analytics and usage tracking
- 🔄 Conversation memory for context