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
def chatbot():
while True:
user_input = input("User: ")
# Check if the user wants to exit the chat
if user_input.lower() in ['exit', 'quit']:
print("Chatbot: Goodbye!")
break
# Scrape the website or make API request based on user input
scraped_data = scrape_website(user_input)
# Process the scraped data and user query to generate a response
response = process_data(scraped_data, user_input)
print("Chatbot: " + response)
# Start the chatbot
chatbot()