analysis.py

from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
from huggingface_hub import login
import os
import torch

# ✅ Get API token from environment variable
HF_TOKEN = os.getenv("HF_TOKEN")

# ✅ Authenticate with Hugging Face (without exposing the token in code)
login(HF_TOKEN)

# ✅ Load Model Efficiently
MODEL_NAME = "tiiuae/falcon-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=HF_TOKEN)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    token=HF_TOKEN,
    device_map="auto",
    torch_dtype=torch.float16,  
    load_in_8bit=True  
)

# ✅ Create LLM Pipeline
llm_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")

def analyze_spending_pattern(df):
    prompt = "Analyze the following UPI transactions:\n" + df.to_string()
    response = llm_pipeline(prompt, max_length=200)[0]["generated_text"]
    return response

def get_financial_advice(df):
    prompt = "Provide financial advice based on these UPI transactions:\n" + df.to_string()
    response = llm_pipeline(prompt, max_length=200)[0]["generated_text"]
    return response