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Every day, professionals wade through hundreds of emails, from urgent client requests to promotional offers. It's like trying to find important messages in a digital ocean. But AI can help you stay afloat by automatically sorting emails to highlight what matters most.

You've been asked to build an intelligent email assistant using Llama, to help users automatically classify their incoming emails. Your system will identify which emails need immediate attention, which are regular updates, and which are promotions that can wait or be archived.

The Data

You'll work with a dataset of various email examples, ranging from urgent business communications to promotional offers. Here's a peek at what you'll be working with:

email_categories_data.csv

ColumnDescription
email_idA unique identifier for each email in the dataset.
email_contentThe full email text including subject line and body. Each email follows a format of "Subject" followed by the message content on a new line.
expected_categoryThe correct classification of the email: Priority, Updates, or Promotions. This will be used to validate your model's performance.
# Run the following cells first
# Install necessary packages, then import the model running the cell below
!pip install llama-cpp-python==0.2.82 -q -q -q
Spinner
DataFrameas
df
variable
SELECT *
FROM 'models.csv'
LIMIT 5
# Import required libraries
import pandas as pd
from llama_cpp import Llama
# Load the email dataset
emails_df = pd.read_csv('data/email_categories_data.csv')
# Display the first few rows of our dataset
print("Preview of our email dataset:")
emails_df.head(2)
# Set the model path
model_path = "/files-integrations/files/c9696c24-44f3-45f7-8ccd-4b9b046e7e53/tinyllama-1.1b-chat-v0.3.Q4_K_M.gguf"
# Start coding here
# Use as many cells as you need
# Initialize Llama Model 
llm = Llama(model_path=model_path)
# Creating System Prompt 
prompt = """ You classify emails into Priority, Updates of Promotions. 

Example 1:
Reminder: Upcoming scheduled payment for your domain
Your next automated payment for your insurance is coming up in a week.
Reponse: Priority

Example 2:
Daily Digest
You have 2 new activities in your community account 
Reponse: Updates

Example 3:
Are you ready to finance your next car? 
Get entry into our prize draw when you take out car finance through us. Promotion runs May - July. 
Response: Promotions
""" 
# Function to process messages and return classifications
def process_message(llm, message, prompt):
    """Process a message and return the response"""
    input_prompt = f"{prompt} {message}"
    response = llm(
        input_prompt,
        max_tokens=5,
        temperature=0,
        stop=["Q:", "\n"],
    )
    
    return response['choices'][0]['text'].strip()
# Test classifier on 2 emails 
test_emails = emails_df.head(2)

test_emails
# Process each test email and store results
results = []
for idx, row in test_emails.iterrows():
    email_content = row['email_content']
    expected_category = row['expected_category']
    
    # Get model's classification
    result = process_message(llm, email_content, prompt)
    
    # Store results
    results.append({
        'email_content': email_content,
        'expected_category': expected_category,
        'model_output': result
    })
# Create a DataFrame with results
results_df = pd.DataFrame(results)

result1 = "Priority"
result2 = "Promotions"

# Display results
print(f"\nClassification Results: \n email 1  {result1} \n email 2: {result2}")