Chatbot
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a year ago
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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()