Generate a markdown code for the following which should generate a readme file:

import re
import requests
from bs4 import BeautifulSoup
from transformers import pipeline
import torch

# ASCII art banner for VERITAS AI
def print_banner():
    banner = """
\033[94m
╦  ╦╔═╗╦═╗╦╔╦╗╔═╗╔═╗  ╔═╗╦
╚╗╔╝║╣ ╠╦╝║ ║ ╠═╣╚═╗  ╠═╣║
 ╚╝ ╚═╝╩╚═╩ ╩ ╩ ╩╚═╝  ╩ ╩╩
    Truth by AI
\033[0m"""
    print(banner)

# Function to check if the input is a URL
def is_url(input_text):
    # Simple regex to check for URL pattern
    url_pattern = re.compile(
        r'^(?:http|ftp)s?://' # http:// or https://
        r'(?:\S+)', re.IGNORECASE)
    return re.match(url_pattern, input_text) is not None

# Function to extract text from URL
def extract_text_from_url(url):
    try:
        headers = {'User-Agent': 'Mozilla/5.0'}
        response = requests.get(url, headers=headers)
        if response.status_code != 200:
            print(f"Failed to retrieve the URL. Status code: {response.status_code}")
            return None
        soup = BeautifulSoup(response.content, 'html.parser')

        # Attempt to extract text from common HTML tags
        paragraphs = soup.find_all(['p', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6'])
        text = ' '.join([para.get_text() for para in paragraphs])
        return text.strip()
    except Exception as e:
        print(f"An error occurred while extracting text from the URL: {e}")
        return None

# Function to load multiple classifiers
def load_classifiers():
    classifiers = []
    model_names = [
        "distilbert-base-uncased-finetuned-sst-2-english",
        "roberta-base-openai-detector",
        "ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli",
        "facebook/bart-large-mnli"
        # Add more valid models here
    ]

    for model_name in model_names:
        try:
            classifier = pipeline(
                "text-classification",
                model=model_name,
                tokenizer=model_name,
                device=0 if torch.cuda.is_available() else -1
            )
            classifiers.append((model_name, classifier))
            print(f"Loaded model: {model_name}")
        except Exception as e:
            print(f"Failed to load model {model_name}: {e}")

    return classifiers

# Function to predict using all classifiers with truncation
def predict_fake_news(text, classifiers):
    predictions = []
    for model_name, classifier in classifiers:
        try:
            # Get the model's tokenizer to determine max length
            tokenizer = classifier.tokenizer
            max_length = tokenizer.model_max_length if tokenizer.model_max_length < 1e5 else 512  # Fallback to 512

            # Tokenize and truncate the text
            inputs = tokenizer.encode_plus(
                text,
                max_length=max_length,
                truncation=True,
                padding=False,
                return_tensors='pt'
            )

            # Generate truncated text
            truncated_text = tokenizer.decode(inputs['input_ids'][0], skip_special_tokens=True)

            # Perform classification
            result = classifier(truncated_text)[0]
            label = result['label']
            score = result['score']
            predictions.append((label, score))
            print(f"Model {model_name} prediction: {label} with confidence {score:.4f}")
        except Exception as e:
            print(f"Error in model {model_name}: {e}")
    return predictions

# Function to aggregate predictions
def aggregate_predictions(predictions, label_mapping, model_names):
    if not predictions:
        return "Unable to make a prediction.", 0.0

    label_confidences = {}
    for i, (label, score) in enumerate(predictions):
        model_name = model_names[i]
        mapped_label = label_mapping.get(model_name, {}).get(label, label)
        if mapped_label not in label_confidences:
            label_confidences[mapped_label] = []
        label_confidences[mapped_label].append(score)

    # Calculate average confidence for each label
    avg_confidences = {label: sum(scores)/len(scores) for label, scores in label_confidences.items()}

    # Determine the label with highest average confidence
    final_label = max(avg_confidences, key=avg_confidences.get)
    final_confidence = avg_confidences[final_label]

    return final_label, final_confidence

def main():
    print_banner()
    user_input = input("Enter a URL, headline, or text: ").strip()

    if is_url(user_input):
        print("Detected input as URL. Extracting content...")
        text = extract_text_from_url(user_input)
        if not text:
            print("Unable to extract text from the provided URL.")
            return
    else:
        text = user_input
        print("Detected input as text.")

    print("\nLoading classifiers...")
    classifiers = load_classifiers()
    if not classifiers:
        print("No classifiers loaded. Exiting.")
        return

    # Define label mapping for each model
    label_mapping = {
        "distilbert-base-uncased-finetuned-sst-2-english": {"LABEL_0": "REAL", "LABEL_1": "FAKE"},
        "roberta-base-openai-detector": {"LABEL_0": "REAL", "LABEL_1": "FAKE"},
        "ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli": {"LABEL_0": "REAL", "LABEL_1": "FAKE"},
        "facebook/bart-large-mnli": {"ENTAILMENT": "REAL", "NOT_ENTAILMENT": "FAKE"}  # Example mapping
        # Add mappings for additional models accordingly
    }

    print("\nPredicting fake news...")
    predictions = predict_fake_news(text, classifiers)
    model_names = [model for model, _ in classifiers]

    verdict, confidence = aggregate_predictions(predictions, label_mapping, model_names)
    print("\n=== VERITAS AI RESULT ===")
    print(f"Verdict: {verdict}")
    print(f"Confidence: {confidence*100:.2f}%")

if __name__ == "__main__":
    main()