app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset


huggingface-cli login

# Load LLaMA model and tokenizer from Hugging Face
model_name = "meta-llama/Llama-3.2-1B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Load financial dataset to enrich responses
dataset = load_dataset("gbharti/finance-alpaca")

# Helper function to extract dataset info (optional enhancement)
def get_insight_from_dataset():
    sample = dataset["train"].shuffle(seed=42).select([0])[0]
    return f"Example insight: {sample['text']}"

# Function to process user input and generate financial advice
def financial_advisor(user_input):
    # Tokenize the user input
    inputs = tokenizer(user_input, return_tensors="pt")
    
    # Generate response using the LLaMA model
    outputs = model.generate(**inputs, max_length=256, num_return_sequences=1)
    advice = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Get additional insight from dataset to enrich advice (optional)
    insight = get_insight_from_dataset()

    # Combine the advice and the insight
    full_response = f"Advice: {advice}\n\n{insight}"
    return full_response

# Create Gradio Interface
interface = gr.Interface(
    fn=financial_advisor,
    inputs=gr.Textbox(lines=5, placeholder="Enter your financial question..."),
    outputs="text",
    title="AI Financial Advisor",
    description="Ask me anything related to finance, investments, savings, and more.",
    examples=[
        "Should I invest in stocks or real estate?",
        "How can I save more money on a tight budget?",
        "What are some good investment options for retirement?",
    ]
)

# Launch the Gradio app
if __name__ == "__main__":
    interface.launch()