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README.md

@@ -1,13 +1,13 @@
----
-title: RetailProductRecommendationExplainer
-emoji: 💻
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 6.3.0
-app_file: app.py
-pinned: false
-short_description: (Before vs After RL)
----
+# Retail Recommendation Explainer — Before vs After RL (DPO)
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+This Space compares a base instruct model vs a DPO-trained LoRA adapter for
+retail product recommendations with concise, constraint-aware explanations.
+
+## Environment Variables (optional)
+- `BASE_MODEL` (default: `Qwen/Qwen2.5-0.5B-Instruct`)
+- `ADAPTER_RL_PATH` (default: `./adapter_dpo`)
+
+## How to use
+1. Train and produce `adapter_dpo/` using the scripts in `trainer/`.
+2. Copy the `adapter_dpo/` folder into the Space repo (same folder level as `app.py`).
+3. Run the Space and click **Generate (Before vs After)**.

app.py

@@ -0,0 +1,119 @@
+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()

requirements.txt

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+gradio
+torch
+transformers>=4.42.0
+peft
+accelerate