app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, TextGenerationPipeline

# Load model and tokenizer
model_id = "mistralai/Mistral-7B-Instruct-v0.2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

pipe = TextGenerationPipeline(model=model, tokenizer=tokenizer, max_new_tokens=256)

def generate_advice(user_input):
    if not user_input.strip():
        return "Please enter a valid finance question."

    prompt = f"[INST] You are a knowledgeable financial advisor for Indian students. Provide detailed, practical advice for this question:\n\n{user_input} [/INST]"
    
    response = pipe(prompt)[0]['generated_text']
    
    # Remove prompt from response
    cleaned = response.replace(prompt, "").strip()
    return cleaned

iface = gr.Interface(
    fn=generate_advice,
    inputs=gr.Textbox(lines=2, placeholder="Ask something like: How to invest as a college student?"),
    outputs="text",
    title="Smart Finance Advisor",
    description="Ask financial questions: budgeting, investing, saving. Targeted for Indian students. Powered by Mistral 7B."
)

iface.launch()