import gradio as gr
import sympy as sp
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_ID = "Qwen/Qwen2.5-0.5B-Instruct"  
SYSTEM_PROMPT = "You are a helpful tutor. Match the user's level."

tok = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.float32,  # CPU
    device_map=None
)
model.eval()

def verify_math(expr_str: str) -> str:
    try:
        expr = sp.sympify(expr_str)
        simplified = sp.simplify(expr)
        return f"Simplified: ${sp.latex(simplified)}$"
    except Exception as e:
        return f"Could not verify with SymPy: {e}"

def generate(question: str, level: str, step_by_step: bool) -> str:
    if not question.strip():
        return "Please enter a question."
    style = f"Level: {level}. {'Explain step-by-step.' if step_by_step else 'Be concise.'}"
    prompt = f"System: {SYSTEM_PROMPT}\n{style}\nUser: {question}\nAssistant:"
    inputs = tok(prompt, return_tensors="pt")
    with torch.no_grad():
        out = model.generate(
            **inputs,
            max_new_tokens=192,
            do_sample=True,
            temperature=0.7,
            top_p=0.95,
            pad_token_id=tok.eos_token_id
        )
    text = tok.decode(out[0], skip_special_tokens=True)
    if "Assistant:" in text:
        text = text.split("Assistant:", 1)[1].strip()
    is_math = any(ch in question for ch in "+-*/=^") or question.lower().startswith(("simplify","derive","integrate"))
    sympy_note = verify_math(question) if is_math else "No math verification needed."
    return f"{text}\n\n---\n**SymPy check:** {sympy_note}\n_Status: Transformers CPU_"

def build_app():
    with gr.Blocks(title="LearnLoop — CPU Space") as demo:
        
    # CSS styles and adding colours
        gr.HTML("""
        <style>
        /* Button colours */
        #explain-btn { 
            background-color: #5499C7;  /* blue Explain */
            color: white; 
            border-radius: 8px; 
        }
        #reset-btn { 
            background-color: #EC7063;  /* red Reset */
            color: white; 
            border-radius: 8px; 
        }
        /* Hover-efect */
        #explain-btn:hover, #reset-btn:hover { 
            opacity: 0.85; 
        }
        </style>
        """)
        
        # prints using instructions 
        gr.Markdown("""
        # **LearnL**<span style="font-size:1.2em; color: #21618C">∞</span>**p — AI Tutor**
        This app uses the [Qwen 2.5 model](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) 
        to explain questions at different skill levels. It can also verify 
        mathematical expressions using the SymPy library.

        **How to use:**  
        1️⃣ Type your question or a mathematical expression  
        2️⃣ Select your level (Beginner, Intermediate, Advanced)  
        3️⃣ Choose whether you want a step-by-step explanation  
        4️⃣ Press **"Explain"**

        💬 You can ask your question in **Finnish or English** —  
        LearnLoop will reply in the same language you use.
        """)

        q = gr.Textbox(label="Your question", placeholder="e.g., simplify (x^2 - 1)/(x - 1)")
        level = gr.Dropdown(choices=["Beginner","Intermediate","Advanced"], value="Beginner", label="Level")
        step = gr.Checkbox(value=True, label="Step-by-step")
        
        # Mardown for results
        out = gr.Markdown()

        # buttons next to each other
        with gr.Row():
            btn = gr.Button("Explain", elem_id="explain-btn")
            reset_btn = gr.ClearButton([q, out], value="Reset", elem_id="reset-btn")

        # connect button to generate function
        btn.click(generate, [q, level, step], out)

        
    return demo

if __name__ == "__main__":
    build_app().launch()