File size: 4,243 Bytes
4e80910 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | import os
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
BASE_MODEL = os.environ.get("BASE_MODEL", "Qwen/Qwen2.5-0.5B-Instruct")
ADAPTER_RL_PATH = os.environ.get("ADAPTER_RL_PATH", "./adapter_dpo") # commit folder into Space repo
SYSTEM_PROMPT = """You are a retail recommendation assistant.
You recommend at most 3 items that complement the user's cart and intent.
You must be:
- Relevant to the cart + intent
- Constraint-aware (budget, urgency, compatibility, brand preferences)
- Non-pushy and honest (no made-up specs or guarantees)
- Concise and structured
Output format:
Recommendations:
1) <item> — <one-line reason>
2) ...
Why these:
- ...
Compatibility / checks:
- ...
Optional next step:
- (only if helpful)
"""
def build_prompt(user_intent, cart, budget, urgency, brand_avoid):
cart_list = [c.strip() for c in cart.split(",") if c.strip()]
constraints = {
"budget_usd": budget,
"shipping_urgency": urgency,
"brand_avoid": [b.strip() for b in brand_avoid.split(",") if b.strip()]
}
user_block = (
f"User intent: {user_intent}\n"
f"Cart: {', '.join(cart_list)}\n"
f"Constraints: {constraints}\n"
"Generate recommendations following the required format."
)
return f"<|system|>\n{SYSTEM_PROMPT}\n<|user|>\n{user_block}\n<|assistant|>\n"
@torch.inference_mode()
def generate(model, tok, prompt, max_new_tokens=220, temperature=0.7):
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=True if temperature > 0 else False,
temperature=temperature,
pad_token_id=tok.eos_token_id,
)
text = tok.decode(out[0], skip_special_tokens=True)
# Best-effort: return only assistant completion
if "<|assistant|>" in text:
return text.split("<|assistant|>", 1)[-1].strip()
return text.strip()
def load_models():
tok = AutoTokenizer.from_pretrained(BASE_MODEL, use_fast=True)
if tok.pad_token is None:
tok.pad_token = tok.eos_token
base = AutoModelForCausalLM.from_pretrained(
BASE_MODEL,
device_map="auto",
torch_dtype="auto",
)
rl = None
if os.path.exists(ADAPTER_RL_PATH):
rl = PeftModel.from_pretrained(base, ADAPTER_RL_PATH)
return tok, base, rl
tok, base_model, rl_model = load_models()
def run_both(user_intent, cart, budget, urgency, brand_avoid, max_new_tokens, temperature):
prompt = build_prompt(user_intent, cart, budget, urgency, brand_avoid)
before = generate(base_model, tok, prompt, max_new_tokens=max_new_tokens, temperature=temperature)
if rl_model is None:
after = "RL adapter not found. Ensure adapter_dpo/ is included in the Space repo or ADAPTER_RL_PATH is correct."
else:
after = generate(rl_model, tok, prompt, max_new_tokens=max_new_tokens, temperature=temperature)
return before, after
with gr.Blocks() as demo:
gr.Markdown("# Retail Recommendation Explainer — Before vs After RL (DPO)")
user_intent = gr.Textbox(
label="User intent",
value="I’m starting to run regularly and want to avoid blisters."
)
cart = gr.Textbox(label="Cart items (comma-separated)", value="running shoes, socks")
with gr.Row():
budget = gr.Slider(10, 150, value=40, step=5, label="Budget (USD)")
urgency = gr.Dropdown(["fast", "normal"], value="fast", label="Shipping urgency")
brand_avoid = gr.Textbox(label="Brands/materials to avoid (comma-separated)", value="")
with gr.Row():
max_new_tokens = gr.Slider(80, 400, value=220, step=10, label="Max new tokens")
temperature = gr.Slider(0.0, 1.2, value=0.7, step=0.05, label="Temperature")
btn = gr.Button("Generate (Before vs After)")
with gr.Row():
out_before = gr.Textbox(label="Before (Base)", lines=18)
out_after = gr.Textbox(label="After (RL / DPO LoRA)", lines=18)
btn.click(
fn=run_both,
inputs=[user_intent, cart, budget, urgency, brand_avoid, max_new_tokens, temperature],
outputs=[out_before, out_after]
)
demo.launch()
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