app.py

from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import torch
import gradio as gr
import os

token = os.getenv("MyToken2")  


if not token:
    raise ValueError("Hugging Face token not found!")

 
model_id = "meta-llama/Llama-3.2-1B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id, token=token)
model = AutoModelForCausalLM.from_pretrained(model_id, token=token)

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
 

def generate_response(financial_goal, risk_tolerance, investment_horizon, monthly_income, monthly_expense, current_savings):
    prompt = f"""
    I need financial advice based on the following information:
 
    Financial Goal: {financial_goal}
    Risk Tolerance: {risk_tolerance}
    Investment Horizon (years): {investment_horizon}
    Monthly Income (€): {monthly_income}
    Monthly Expense (€): {monthly_expense}
    Current Savings (€): {current_savings}
 
    Please provide a detailed financial plan.
    """
 
    
    outputs = pipe(
        prompt,
        max_new_tokens=300,
    )
    response = outputs[0]["generated_text"]
    return response
 

iface = gr.Interface(
    fn=generate_response,
    inputs=[
        gr.Textbox(label="Financial Goal"),
        gr.Textbox(label="Risk Tolerance"),
        gr.Number(label="Investment Horizon (years)"),
        gr.Number(label="Monthly Income (€)"),
        gr.Number(label="Monthly Expense (€)"),
        gr.Number(label="Current Savings (€)"),
    ],
    outputs=gr.Textbox(label="Financial Advice"),
    title="Financial Advisor",
    description="Enter your financial details to receive personalized advice.",
)
 
# Launch the Gradio app
iface.launch()