from transformers import pipeline
from huggingface_hub import login
import pandas as pd

# 🔹 OPTIONAL: Authenticate if using a gated/private model
# login(token="your_huggingface_token")  # Uncomment and replace if needed

# ✅ Use a Free Model (Mistral-7B-v0.1 OR Gemma-2B)
MODEL_NAME = "tiiuae/falcon-7b-instruct"  # Open-source alternative  # Publicly available
# MODEL_NAME = "google/gemma-2b"  # Alternative (smaller but open-access)

# Load the text generation model
llm_pipeline = pipeline("text-generation", model=MODEL_NAME, device_map="auto")

def analyze_spending_pattern(df):
    """
    Analyze the user's spending behavior.
    """
    prompt = f"""
    Here is the user's spending data:
    {df.to_string(index=False)}
    
    Identify spending trends, categorize expenses, and highlight areas for cost-saving.
    """
    response = llm_pipeline(prompt, max_length=200, do_sample=True)
    return response[0]['generated_text']

def get_financial_advice(df):
    """
    Provide personalized financial recommendations.
    """
    prompt = f"""
    Given the following transaction history:
    {df.to_string(index=False)}
    
    Provide personalized recommendations to reduce expenses and improve financial health.
    """
    response = llm_pipeline(prompt, max_length=200, do_sample=True)
    return response[0]['generated_text']