app.py

import numpy as np
import pandas as pd
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr

# Load a free model from Hugging Face
model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"  # Small model that works well for simple tasks
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)

# Financial knowledge base - simple templates and responses
financial_templates = {
    "budget": "Here's a simple budget template based on the 50/30/20 rule:\n- 50% for needs (rent, groceries, utilities)\n- 30% for wants (dining out, entertainment)\n- 20% for savings and debt repayment",
    "emergency_fund": "An emergency fund should ideally cover 3-6 months of expenses. Start with a goal of $1,000, then build from there.",
    "debt": "Focus on high-interest debt first (like credit cards). Consider the debt avalanche (highest interest first) or debt snowball (smallest balance first) methods.",
    "investing": "For beginners, consider index funds or ETFs for diversification. Time in the market beats timing the market.",
    "retirement": "Take advantage of employer matches in retirement accounts - it's free money. Start early to benefit from compound interest."
}

# Define guided chat flow
def guided_response(user_message, chat_history):
    # Check if we should use a template response
    for key, template in financial_templates.items():
        if key in user_message.lower():
            return template
    
    # For more general queries, use the AI model
    prompt = f"""<human>I need financial advice: {user_message}</human>
<assistant>"""
    
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        inputs["input_ids"],
        max_length=512,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    # Extract just the assistant's response
    if "<assistant>" in response:
        response = response.split("<assistant>")[1].strip()
    
    return response

# Create budget calculator function
def calculate_budget(monthly_income, housing, utilities, groceries, transportation):
    total_needs = housing + utilities + groceries + transportation
    needs_percent = (total_needs / monthly_income) * 100
    
    available_for_wants = monthly_income * 0.3
    available_for_savings = monthly_income * 0.2
    
    return f"""Based on the 50/30/20 rule:
    
    Current spending on needs: ${total_needs:.2f} ({needs_percent:.1f}% of income)
    Recommended max for needs: ${monthly_income * 0.5:.2f} (50%)
    
    Available for wants: ${available_for_wants:.2f} (30%)
    Recommended for savings/debt: ${available_for_savings:.2f} (20%)
    
    {'Your needs expenses are within recommended limits!' if needs_percent <= 50 else 'Your needs expenses exceed 50% of income. Consider areas to reduce spending.'}
    """

# Setup Gradio interface with tabs
with gr.Blocks() as app:
    gr.Markdown("# Financial Advisor Bot")
    
    with gr.Tab("Chat Advisor"):
        chatbot = gr.Chatbot(height=400)
        msg = gr.Textbox(label="Ask a question about personal finance")
        clear = gr.Button("Clear")
        
        def respond(message, chat_history):
            bot_message = guided_response(message, chat_history)
            chat_history.append((message, bot_message))
            return "", chat_history
        
        msg.submit(respond, [msg, chatbot], [msg, chatbot])
        clear.click(lambda: None, None, chatbot, queue=False)
    
    with gr.Tab("Budget Calculator"):
        gr.Markdown("## 50/30/20 Budget Calculator")
        with gr.Row():
            income = gr.Number(label="Monthly Income (after tax)")
        
        with gr.Row():
            gr.Markdown("### Monthly Expenses (Needs)")
        with gr.Row():
            housing = gr.Number(label="Housing", value=0)
            utilities = gr.Number(label="Utilities", value=0)
            groceries = gr.Number(label="Groceries", value=0)
            transport = gr.Number(label="Transportation", value=0)
        
        calculate_btn = gr.Button("Calculate Budget")
        output = gr.Textbox(label="Budget Analysis", lines=10)
        
        calculate_btn.click(
            calculate_budget, 
            inputs=[income, housing, utilities, groceries, transport], 
            outputs=output
        )

# Launch the app in Colab
app.launch(share=True)  # share=True creates a public link you can share with others