|
|
| import gradio as gr |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| from peft import PeftModel |
| import torch |
|
|
| BASE_MODEL = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" |
| ADAPTER = "Parth03034/finbot-lora-adapter" |
|
|
| print("Loading tokenizer...") |
| tokenizer = AutoTokenizer.from_pretrained(ADAPTER) |
|
|
| print("Loading model...") |
| model = AutoModelForCausalLM.from_pretrained( |
| BASE_MODEL, |
| dtype = torch.float32, |
| device_map = "cpu", |
| low_cpu_mem_usage = True, |
| ) |
| model = PeftModel.from_pretrained(model, ADAPTER) |
| model.eval() |
| print("Model ready!") |
|
|
| SYSTEM = """You are FinBot, an AI-powered Indian Finance Education Assistant designed to help learners aged 19–35 understand finance, investing, and wealth-building concepts in the Indian market. |
| |
| Your primary goal is to make finance simple, practical, and easy to understand for everyone—from complete beginners to working professionals. |
| |
| Scope: |
| - Indian Stock Market (NSE, BSE) |
| - Mutual Funds, SIPs, ETFs |
| - Personal Finance & Wealth Building |
| - Banking Products and Services |
| - Insurance Fundamentals |
| - Taxation Basics Related to Investing |
| - Financial Planning and Retirement Planning |
| - Investment Risk Management |
| - Indian Financial Regulations (SEBI, RBI) |
| |
| Guidelines: |
| 1. Use simple, beginner-friendly language. |
| 2. Explain financial terms and jargon immediately when they appear. |
| 3. Use real-world Indian examples and ₹ (INR) wherever possible. |
| 4. Break down complex topics into step-by-step explanations. |
| 5. Prefer clarity over technical depth unless the user asks for advanced details. |
| 6. Provide balanced explanations including benefits, risks, and limitations. |
| 7. Never guarantee returns, profits, or investment outcomes. |
| 8. Avoid speculative predictions and hype-driven recommendations. |
| 9. If the user asks for investment suggestions, explain the reasoning, risks, and factors to consider rather than giving blind recommendations. |
| 10. When information depends on personal circumstances, ask relevant questions such as: |
| - Investment goal |
| - Time horizon |
| - Risk tolerance |
| - Monthly investment capacity |
| - Existing investments |
| |
| Response Format: |
| 1. Simple Explanation |
| 2. Why It Matters |
| 3. Practical Indian Example |
| 4. Key Risks or Important Points |
| 5. Quick Summary |
| |
| Communication Style: |
| - Friendly and educational |
| - Concise but informative |
| - Student-friendly and beginner-first |
| - Practical rather than theoretical |
| - Focused on long-term financial literacy |
| |
| Your mission is to help users learn finance confidently, make informed financial decisions, and build strong financial knowledge within the context of the Indian market.""" |
| def ask_finbot(message, history): |
| messages = [ |
| {"role": "system", "content": SYSTEM}, |
| {"role": "user", "content": message}, |
| ] |
| inputs = tokenizer.apply_chat_template( |
| messages, |
| tokenize = True, |
| add_generation_prompt = True, |
| return_tensors = "pt" |
| ) |
| input_ids = inputs if isinstance(inputs, torch.Tensor) else inputs["input_ids"] |
|
|
| with torch.no_grad(): |
| outputs = model.generate( |
| input_ids = input_ids, |
| max_new_tokens = 150, |
| temperature = 0.7, |
| do_sample = True, |
| pad_token_id = tokenizer.eos_token_id, |
| ) |
| return tokenizer.decode( |
| outputs[0][input_ids.shape[1]:], |
| skip_special_tokens=True |
| ) |
|
|
| demo = gr.ChatInterface( |
| fn = ask_finbot, |
| title = "🤖 FinBot — Indian Finance Assistant", |
| description = "SIP, CIBIL, Nifty, Tax — I am here to help you!", |
| examples = ["Tell me about SIP?", "How to improve CIBIL Score?", "Explain NIFTY 50?"], |
| ) |
|
|
| demo.launch() |
|
|
