python code for an AI to handle replies for emails for the transportation Industry. This Ai should become better has it reads replies from humans (dispatchers) and be able to give better responses. It should be able to collect data from PDF and store them for later use

 Certainly! Here's a basic Python script outlining how you might start building an AI model for handling email replies in the transportation industry. This example uses a simple machine learning approach with the scikit-learn library for text classification. Keep in mind that this is just a starting point and a real-world implementation would require more sophisticated techniques and potentially other libraries/frameworks.

python
import os
import re
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
from sklearn.metrics import accuracy_score
import joblib  # for saving and loading models

# Function to clean text data
def clean_text(text):
    text = text.lower()  # Convert text to lowercase
    text = re.sub(r'\W', ' ', text)  # Remove non-word characters
    text = re.sub(r'\s+', ' ', text)  # Remove extra whitespaces
    return text

# Function to load data from PDF files
def load_data_from_pdf(directory):
    data = []
    for filename in os.listdir(directory):
        if filename.endswith(".pdf"):
            # Use PDF parsing library (e.g., PyPDF2) to extract text from PDF
            # Example: text = parse_pdf(os.path.join(directory, filename))
            text = "Sample text extracted from PDF"  # Placeholder
            data.append(text)
    return data

# Load and preprocess email data
emails_directory = "email_data"
email_data = load_data_from_pdf(emails_directory)

# Load and preprocess replies data
replies_directory = "reply_data"
reply_data = load_data_from_pdf(replies_directory)

# Label the data: 0 for email, 1 for reply
email_labels = [0] * len(email_data)
reply_labels = [1] * len(reply_data)

# Combine email and reply data
all_data = email_data + reply_data
all_labels = email_labels + reply_labels

# Clean text data
cleaned_data = [clean_text(text) for text in all_data]

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(cleaned_data, all_labels, test_size=0.2, random_state=42)

# Convert text data into TF-IDF vectors
vectorizer = TfidfVectorizer(max_features=1000)  # Limit to top 1000 features
X_train_tfidf = vectorizer.fit_transform(X_train)
X_test_tfidf = vectorizer.transform(X_test)

# Train a Linear SVM classifier
classifier = LinearSVC()
classifier.fit(X_train_tfidf, y_train)

# Predict on test data
y_pred = classifier.predict(X_test_tfidf)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

# Save the model and vectorizer for later use
joblib.dump(classifier, 'email_reply_classifier.pkl')
joblib.dump(vectorizer, 'tfidf_vectorizer.pkl')

This script is a simplified example. In a real-world scenario, you would need to:

1. Implement proper PDF text extraction.
2. Collect a more extensive and diverse dataset of emails and replies.
3. Experiment with different machine learning algorithms and hyperparameters to improve performance.
4. Handle more complex email-reply interactions, possibly using techniques like sequence-to-sequence models or transformers.
5. Integrate with email APIs for sending and receiving emails.

Remember, building an effective AI for this task requires a combination of domain expertise, data availability, and experimentation with various techniques.

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Here's a starter Python code template to build a foundational framework for the AI you described. This is a high-level overview, and each section will require further development and specific libraries: ```python # Import necessary libraries import imaplib # for email access from pdfminer.high_level import extract_text # for PDF parsing import pandas as pd # for data manipulation from transformers import AutoModelForSeq2SeqLM, AutoTokenizer # for AI model # Email Credentials (replace with your credentials) username = "your_email@your_provider.com" password = "your_email_password" # Function to connect to email server and retrieve emails def get_emails(): imap_server = imaplib.IMAP4_SSL("imap.your_provider.com") # Replace with your provider's server address imap_server.login(username, password) imap_server.select("INBOX") # Replace with inbox name if different status, data = imap_server.search(None, "ALL") # Parse data to extract email information (e.g., sender, subject, body) # ... (implement logic to parse email data) return emails # Replace with your parsed email data structure # Function to extract text from PDF attachments def extract_pdf_data(attachment): with open(attachment, 'rb') as f: text = extract_text(f) return text # Function to preprocess email data (e.g., remove irrelevant text) def preprocess_email(email_text): # Implement logic to clean and prepare email text for AI model preprocessed_text = email_text return preprocessed_text # Function to load and use the AI model def generate_response(email_text): # Load pre-trained AI model for email response generation model_name = "facebook/bart-base" # Replace with your chosen model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) # Preprocess email text preprocessed_text = preprocess_email(email_text) # Tokenize and encode the text inputs = tokenizer(preprocessed_text, return_tensors="pt") # Generate response using the AI model outputs = model.generate(**inputs) decoded_response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] return decoded_response # Main program flow emails = get_emails() for email in emails: # Extract relevant data from email (e.g., sender, subject, body) sender = email["sender"] # Replace with your data extraction logic subject = email["subject"] # Replace with your data extraction logic body_text = email["body"] # Replace with your data extraction logic # Check for PDF attachments if email["has_attachments"]: # Replace with your attachment checking logic for attachment in email["attachments"]: # Replace with your attachment handling logic pdf_data = extract_pdf_data(attachment) # Store extracted PDF data (implement data storage logic) # Preprocess email body text preprocessed_text = preprocess_email(body_text) # Generate response using AI model response = generate_response(preprocessed_text) # Send generated response (implement logic to send email response) # ... # Close email connection imap_server.close() imap_server.logout() ``` **Explanation:** 1. **Imports:** Necessary libraries are imported for email access, PDF parsing, data manipulation, and the AI model. 2. **Email Credentials:** Replace placeholders with your actual email credentials. 3. **`get_emails` function:** Retrieves emails from the server and parses relevant data (sender, subject, body). This section needs further development for specific data extraction. 4. **`extract_pdf_data` function:** Extracts text from PDF attachments using the `pdfminer` library. 5. **`preprocess_email` function:** Cleans and prepares email text for the AI model. You'll need to implement your specific cleaning logic here. 6. **`generate_response` function:** Loads a pre-trained AI model for email response generation. Replace the model name with your chosen model. The function then preprocesses the email text, tokenizes it, and generates a response using the AI model. 7.