products

app.py

@@ -13,37 +13,6 @@ from peft import PeftModel
 from qwen_vl_utils import process_vision_info  # pip install qwen-vl-utils
 from tools.retriever import search as product_search
 
-# ---- Product JSON block markers ----
-PRODUCTS_START = "<DERMACARE_PRODUCTS_JSON>"
-PRODUCTS_END   = "</DERMACARE_PRODUCTS_JSON>"
-
-def append_products_json_to_text(text_answer: str, products: list[dict]) -> str:
-    """
-    Append a machine-readable JSON block to the end of the text.
-    The Flutter client will read between the PRODUCTS_START/END tags.
-    """
-    payload = {
-        "version": 1,
-        "products": products or [],
-    }
-    block = PRODUCTS_START + json.dumps(payload, ensure_ascii=False) + PRODUCTS_END
-    return text_answer.rstrip() + "\n\n" + block
-
-def to_product_card(meta):
-    return {
-        "id": meta.get("id"),
-        "brand": meta.get("brand_en") or meta.get("brand_zh"),
-        "name": meta.get("product_name_en") or meta.get("product_name_zh"),
-        "category": meta.get("category_en") or meta.get("category_zh"),
-        "price_value": meta.get("price_value"),
-        "price_currency": meta.get("price_currency"),
-        "why": "Matches your query by skin concern/ingredient.",
-        "how": "Use as directed on label; patch-test first.",
-        "url": meta.get("source_url"),
-        "image_url": meta.get("image_url"),
-        "score": round(float(meta.get("_score", 0.0)), 3),
-    }
-
 print("VERSIONS:", transformers.__version__, accelerate.__version__, torch.__version__, np.__version__)
 os.environ["ACCELERATE_USE_SLOW_RETRIEVAL"] = "true"
 
@@ -123,6 +92,40 @@ def recommend_products(query_text: str, budget_twd: int | None = None, k: int =
 
     return answer
 
+
+def _count_products_from_tagged_json(text: str) -> int | None:
+    start_tag = "<DERMACARE_PRODUCTS_JSON>"
+    end_tag = "</DERMACARE_PRODUCTS_JSON>"
+    start = text.find(start_tag)
+    end = text.find(end_tag)
+    if start == -1 or end == -1 or end <= start:
+        return None
+    json_str = text[start + len(start_tag):end]
+    try:
+        payload = json.loads(json_str)
+        products = payload.get("products", [])
+        if isinstance(products, list):
+            return len(products)
+    except Exception:
+        return None
+    return None
+
+
+def _has_valid_suggestions(raw_suggestions: str) -> bool:
+    cleaned = raw_suggestions.strip()
+    if not cleaned:
+        return False
+
+    lower = cleaned.lower()
+    if "no relevant products" in lower:
+        return False
+
+    count = _count_products_from_tagged_json(cleaned)
+    if count is not None:
+        return count > 0
+
+    return True
+
 # ---- Inference on GPU (ZeroGPU pattern) ----
 @spaces.GPU(duration=120)
 def generate_answer(image, question, temperature=0.7, top_p=0.95, max_tokens=256):
@@ -231,26 +234,15 @@ def pet_answer_with_recs(image, question, temperature=0.7, top_p=0.95, max_token
         "Safety notes: For broken/infected skin, pregnancy/lactation, infants, "
         "or if symptoms worsen—seek a qualified dermatologist. Patch-test first."
     )
-    # Final combined message (human-readable)
-    if suggestions.strip() != '':
-        product_section = f"Suggested products:\n{suggestions}"
-    else:
-        product_section = "No relevant products."
-
-    human_text = f"{base}\n\n{product_section}\n\n{safety}"
-
-    # Build deterministic product cards (top-3) for client rendering.
-    # If retrieval/index fails, fall back to empty list so the client gracefully
-    # renders only the text.
-    products_json = []
-    try:
-        top3 = [to_product_card(c) for c in cands[:3]]
-        # Filter out items missing essential fields
-        products_json = [p for p in top3 if p.get("id") and p.get("brand") and p.get("name")]
-    except Exception:
-        products_json = []
+    suggestions = suggestions.strip()
+    include_products = _has_valid_suggestions(suggestions)
+
+    sections = [base.strip()]
+    if include_products:
+        sections.append(f"Suggested products:\n{suggestions}")
+        sections.append(safety)
 
-    return append_products_json_to_text(human_text, products_json)
+    return "\n\n".join([s for s in sections if s])
 
 # ---- UI ----
 with gr.Blocks(title="DermalCare - Pet & Skincare Assistant") as demo:
@@ -276,4 +268,4 @@ with gr.Blocks(title="DermalCare - Pet & Skincare Assistant") as demo:
                 outputs=answer
             )
 
-demo.queue().launch(show_api=False, share=True)
+demo.queue().launch(show_api=False, share=True)