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


MODEL_ID = "Qwen/Qwen2.5-1.5B-Instruct"  
SYSTEM_PROMPT = """
You are a helpful tutor who always avoid hashtags, emojis, or social media style text.

"""


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 level_style(level: str, step: bool) -> str:
    if level == "Beginner":
        return (
            "Simple and short answer to the questions."
            "Use short sentences and sometimes give small examples. "
            + ("Show each step clearly." if step else "Keep it short and clear.")
        )
    elif level == "Intermediate":
        return (
            "Explain with moderate detail. Use correct terminology but keep it approachable. "
            + ("Give step-by-step reasoning." if step else "Keep it short and clear.")
        )
    else:  # Advanced
        return (
            "Use precise and technical language. Assume the user has strong background knowledge involving the matter."
            + ("Show reasoning steps briefly." if step else "Be concise and analytical.")
        )

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 = level_style(level, step_by_step)
    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, # was 384 
            do_sample=True, # False was True
            temperature=0.7,
            top_p=0.9,
            repetition_penalty=1.2,
            no_repeat_ngram_size=3,
            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_"

# Building app and IU
def build_app():
    with gr.Blocks(title="LearnLoop — CPU Space") as demo:
        
# CSS styles and adding colours
        gr.HTML("""
        <style>
       
       .gradio-container {
            background-color: #EDF6FA !important;  /* haalea sininen */
            padding: 24px;
            border-radius: 12px;
            box-shadow: 0 4px 12px rgba(0,0,0,0.05);
        }
                
        /* buttons */
        button {
            border-radius: 8px;
            transition: all 0.2s ease-in-out;
            font-weight: 500;
            letter-spacing: 0.5px;
        }
        button:hover {
            opacity: 0.9;
            transform: translateY(-1px);
        }
        button:active {
            filter: brightness(85%);
            transform: scale(0.98);
        }
        /* Explain ja Reset buttons */
        #explain-btn { 
            background-color: #5499C7;
            color: white;
            border: 2px solid #2E86C1;
        }
        #reset-btn { 
            background-color: #EC7063;
            color: white;
            border: 2px solid #CB4335;
        }
        #explain-btn:hover, #reset-btn:hover { 
            opacity: 0.85;
        }
        #explain-btn:active, #reset-btn:active {
            filter: brightness(85%);
            transform: scale(0.98);
        }
        </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"** or **Enter** on your keyboard.  
        5️⃣ If you want to enter a new question, you can press **"Reset"** or simply **type a new question**.  
        
        💬 You can ask your question in **English**.
        """)

        # User's feed
        q = gr.Textbox(label="Your question", placeholder="e.g., simplify (x^2 - 1)/(x - 1)", elem_id="question-box")
        level = gr.Dropdown(choices=["Beginner", "Intermediate", "Advanced"], value="Beginner", label="Level")
        step = gr.Checkbox(value=True, label="Step-by-step")

        
        # Results
        loading = gr.Markdown(visible=False)  # spinner hided at first
        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, loading], value="Reset", elem_id="reset-btn")

        # connect to generate function with spinner
        def wrapped_generate(q_val, level_val, step_val):
            # Näytetään spinner ensin
            loading_text = "⏳ Generating explanation..."
            result = generate(q_val, level_val, step_val)
            # hide spinner when ready
            return "", result 

        btn.click(fn=wrapped_generate, inputs=[q, level, step], outputs=[loading, out])
        q.submit(fn=wrapped_generate, inputs=[q, level, step], outputs=[loading, out])

    return demo

# start the app
if __name__ == "__main__":
    build_app().launch()