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: gr.Markdown("# LearnLoop — CPU-only demo") 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") btn = gr.Button("Explain"); out = gr.Markdown() btn.click(generate, [q, level, step], out) return demo if __name__ == "__main__": build_app().launch()