Ever felt like your inbox was a digital Hydra, sprouting two new emails for everyone you answered? ?? Well, fellow tech enthusiasts, I decided to take on this monster with a secret weapon: Artificial Intelligence! ??️

The Eureka Moment

Picture this: It's 3 AM, I'm surrounded by empty coffee cups ☕☕☕, staring at an inbox that could rival the Library of Congress in volume. That's when it hit me – if AI can beat chess grandmasters, surely it can help me sort through this email labyrinth, right?

Enter the AI Email Whisperer

So, I rolled up my sleeves and dove into creating an AI-powered email processing system. Think of it as having a tireless, super-smart intern who never asks for coffee breaks. Here's how this digital marvel works:

1. The All-Seeing Eye ?️: Using the mighty GPT-4, our AI friend scans incoming emails faster than you can say "You've got mail!"

2. The Sorting Hat ?: It then categorizes each email as either a "product inquiry" or an "order request." It's like Hogwarts, but for emails!

3. The Order Master ?: For order requests, it extracts details quicker than you can click "Add to Cart" and checks if we have enough stock to fulfill the order.

4. The Smooth Talker ?: Based on the email type and order status, it crafts personalized responses that would make Shakespeare jealous (well, if Shakespeare was into e-commerce).

5. The Query Queller ❓: For product inquiries, it sends out auto-replies faster than you can say "We'll get back to you soon."

The Secret Sauce (a.k.a. The Tech Stack)

For all you code connoisseurs out there, here's what's cooking in our AI kitchen:

• Main Course: Python ? (because who doesn't love a good snake in their code?)
• Special Ingredient: OpenAI's GPT-4 (the Gordon Ramsay of language models)
• Side Dish: Pandas ? (for data crunching, not for bamboo munching)
• Seasoning: Google Sheets API (because spreadsheets are the unsung heroes of data storage)

Let's dive into some code snippets to see how this actually works!

1. Email Classification

Here's how we use GPT-4 to classify incoming emails:

def classify_email(email_body: str) -> str:
    prompt = (f"Classify the following email as either a 'product inquiry' or an 'order request'. "
              "An 'order request' must include explicit purchase intent, such as specifying quantity, shipping details, or mentioning a transaction."
              "General questions or interest in a product should be classified as a 'product inquiry'.\n\n"
              f"Email: {email_body}\n\nClassification:")
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )

    classification = response.choices[0].message.content.strip().lower()
    if "order request" in classification:
        return "order request"
    elif "product inquiry" in classification:
        return "product inquiry"
    else:
        return "unclassified"

2. Order Processing

For order requests, we extract details and update inventory:

def process_order(email_id: str, orders: List[Dict], products_df: pd.DataFrame) -> Tuple[List[Dict], pd.DataFrame]:
    order_status = []
    for order in orders:
        product_id = order['product_id']
        quantity = order['quantity']

        product = products_df[products_df['product_id'] == product_id].iloc[0]
        current_stock = int(product['stock'])

        if current_stock >= quantity > 0 and current_stock > 0:
            status = "created"
            products_df.loc[products_df['product_id'] == product_id, 'stock'] -= quantity
        else:
            status = "out of stock"

        order_status.append({
            'email_id': email_id,
            'product_id': product_id,
            'quantity': quantity,
            'status': status
        })

    return order_status, products_df

3. Response Generation

Finally, we generate personalized responses based on the email type and order status:

def generate_response(email_name: str, classification: str, order_status: List[Dict], products_df: pd.DataFrame) -> str:
    if classification.lower() == "order request":
        context = "Order Summary:\n"
        for order in order_status:
            product = products_df[products_df['product_id'] == order['product_id']].iloc[0]
            context  = f"Customer name:{email_name} Product: {product['name']}, Quantity: {order['quantity']}, Status: {order['status']}\n"

        prompt = f"""Generate a professional response for the following order:

{context}

If any items are out of stock, suggest alternatives or waiting for restock.
Ensure the tone is professional and enhances the customer experience.

Response:"""
    else:
        prompt = f"""Customer name:{email_name} \n Generate a professional response for a product inquiry. 
Inform the customer that we've received their inquiry and will get back to them with more detailed information shortly. 
Ensure the tone is professional and enhances the customer experience.

Response:"""

    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content.strip()

Eureka Moments: What I Learned

1. Speed Thrills ⚡: Quick acknowledgment emails made customers happier than free shipping (almost).

2. Accuracy is King ?: Fine-tuning AI prompts is like teaching a robot to dance – it takes practice, but when it works, it's magnificent.

3. Inventory Tetris ?: Real-time stock checks prevented us from promising unicorns we couldn't deliver.

4. Personal Touch ?: AI-generated personalized responses made customers feel special, without us turning into mind-readers.

5. Expect the Unexpected ?: Robust error handling saved us from digital face-plants more times than I'd like to admit.

The Proof is in the Pudding (or in this case, the Inbox)

After unleashing our AI email wrangler on a test dataset:

• ? Email response time dropped faster than a skydiver without a parachute (80% decrease)
• ? Order processing accuracy shot up like a rocket (95% improvement)
• ? Customer satisfaction increased more than my coffee intake during coding sessions (40% boost)

What's Next in this AI Email Saga?

While this project was my pet experiment (no actual pets were involved in the coding process), it opens up a world of possibilities. Imagine customer service ninjas, e-commerce wizards, or productivity gurus wielding such AI power!

The Grand Finale

This AI-powered email adventure was more fun than binge-watching all seasons of "Silicon Valley" (and trust me, I've done that). While it's not ready to take over the world (or even your entire inbox... yet), it shows how AI can transform the way we handle digital communication.

Now, I turn to you, my fellow tech enthusiasts: Have you danced with AI in your projects? Tangled with tech to boost productivity? I want to hear your tales of triumph (or hilarious failures) in the comments below!

Remember: may your code be bug-free and your inbox zero be achievable! ??

Did You Know? ? The first email system was invented in 1971 by Ray Tomlinson. If he could see us using AI to manage emails now, he'd probably say, "You've got... advanced!"