# FinGPT Compliance Agents - Inference Example

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# Load the model
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
model = PeftModel.from_pretrained(base_model, "your-username/fingpt-compliance-agents")
tokenizer = AutoTokenizer.from_pretrained("your-username/fingpt-compliance-agents")

# Example usage
def analyze_financial_text(text):
    prompt = f"Analyze this financial text: {text}"
    inputs = tokenizer(prompt, return_tensors="pt")
    with torch.no_grad():
        outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Test the model
result = analyze_financial_text("Company X reported strong quarterly earnings with 15% revenue growth.")
print(result)