Chatty 2023

Check this script for me

import requests
from bs4 import BeautifulSoup
import spacy

# Load the English language model for NER and sentiment analysis
nlp = spacy.load('en_core_web_sm')

def make_api_request(url, headers):
    # Send an API request with the provided headers
    response = requests.get(url, headers=headers)
    
    # Process the API response
    processed_data = process_api_response(response)
    
    # Return the processed information
    return processed_data

def process_api_response(response):
    # Process the API response and extract the required data
    # ...
    processed_data = "Data extracted from API response"
    
    # Return the processed data
    return processed_data

def scrape_website(url):
    if "google.com" in url:
        # Custom logic for Google website
        # ...
        processed_data = "Data extracted from Google"
    elif "youtube.com" in url:
        # Custom logic for YouTube website
        # ...
        processed_data = "Data extracted from YouTube"
    elif "reddit.com" in url:
        # Custom logic for Reddit website
        # ...
        processed_data = "Data extracted from Reddit"
    elif "quora.com" in url:
        # Custom logic for Quora website
        # ...
        processed_data = "Data extracted from Quora"
    else:
        processed_data = "Website not supported"
    
    # If the website is not supported, try making an API request
    if processed_data == "Website not supported":
        api_key = "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"  # Replace with your API key
        
        # Construct the API request URL and headers based on the desired API
        api_url = "https://api.openai.com/v1/chat/completions"  # Replace with the API endpoint
        headers = {"Authorization": "Bearer " + api_key}  # Replace with the required headers
        
        # Make the API request and extract the required data
        processed_data = make_api_request(api_url, headers)
    
    # Return the processed information
    return processed_data

def analyze_sentiment(text):
    # Analyze the sentiment of the given text
    # ...

def extract_entities(text):
    # Extract named entities from the given text
    # ...

def process_data(data, query):
    # Perform processing on the data and user query
    # Find relevant information based on the user query
    # Generate an appropriate response
    
    # Analyze the sentiment of the user query
    query_sentiment = analyze_sentiment(query)
    
    # Extract named entities from the user query
    query_entities = extract_entities(query)
    
    # Use the processed data and the user query information to generate a response
    response = "Based on your query, the sentiment is: " + str(query_sentiment) + "\n"
    response += "The named entities in your query are: " + ", ".join(query_entities)
    
    # Return the response
    return response

# Start the chatbot
chatbot()