import bcrypt import nltk import spacy from transformers import pipeline from yfinance import download # Assuming yfinance for financial data

Initialize NLP tools

nltk.download('punkt') nlp = spacy.load('en_core_web_sm')

Define user access levels

USER_LEVELS = { 1: "Company Founders and History", 2: "Team Leads and Mid-Experience Employees", 3: "Delivery Managers, Heads, Executives" }

Define user access permissions

ACCESS_PERMISSIONS = { 1: ["company_history", "financial_news"], 2: ["company_competitors", "financial_data", "industry_news"], 3: ["advanced_news", "financial_reports", "global_comparisons"] }

def authenticate_user(username, password): # Connect to your database (replace with your connection logic) # Fetch hashed password for the username hashed_password = get_hashed_password(username) # Implement get_hashed_password()

if hashed_password is None:
    return None  # User not found

# Verify password using bcrypt.checkpw()
if bcrypt.checkpw(password.encode(), hashed_password):
    return get_user_access_level(username)  # Fetch user access level from DB (Implement get_user_access_level())
else:
    return None  # Invalid password

def get_financial_data(symbol): # Download data from Yahoo Finance using yfinance library (replace with your chosen data source and library) company_data = download(symbol, period="1d") # Modify period as needed # Process and format the data as needed for your application return processed_data # Implement data processing (processed_data)

def classify_intent(main_verb, entities): # Implement logic to classify intent based on verb and entities (e.g., financial_news, company_history) # Consider using additional NLP techniques for intent classification pass

def has_permission(user_level, intent): return intent in ACCESS_PERMISSIONS[user_level]

def retrieve_data(intent, entities): if intent == "financial_data": # Extract company symbol from entities company_symbol = [ent[0] for ent in entities if ent[1] == "ORG"][0] # Assuming "ORG" for company entity return get_financial_data(company_symbol) # Implement logic to retrieve data from other sources based on intent and entities pass

def generate_response(data): # Craft informative response based on retrieved data response = f"Here's the information you requested: {data}" # Replace with your response generation logic return response

def handle_query(user_level, query): doc = nlp(query)

# Extract entities (company names, financial terms, dates)
entities = [(ent.text, ent.label_) for ent in doc.ents]

# Identify the main verb and classify intent based on the verb and entities
intent = classify_intent(doc.lemma_[0], entities)

# Check access permission based on user level and intent
if has_permission(user_level, intent):
    data = retrieve_data(intent, entities)
    response = generate_response(data)
else:
    response = "You don't have permission to access that information."

return response

Main function

def main(): # User authentication (not implemented in this basic outline) # For demonstration purposes, assume authenticated user with access level 1 authenticated_user = "user1" user_level = 1

# Example queries (to be replaced with actual user queries)
queries = [
    "Who were the founders of Infosys?",
    "What is the latest financial news about Infosys?",
    "What are the competitors of Infosys?",
    "Show me Infosys' financial data for the last quarter.",
    "What are the latest industry trends?",
    "Show me Infosys' quarterly financial reports.",
    "Compare Infosys' performance with global competitors."
]

# Handle each query
for query in queries:
    response = handle_query(user_level, query)
    print(response)

if name == "main": main()