Spaces:
Sleeping
Sleeping
File size: 3,978 Bytes
b7bfb2d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | 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()
|