Upload community_contributions/damola-adewunmi/agents/valuer.py with huggingface_hub

community_contributions/damola-adewunmi/agents/valuer.py

@@ -0,0 +1,53 @@
+"""
+Valuer Agent: GPT-4o with specialist system prompt (simulates fine-tuned pricing expertise).
+Rule: PlayStation UK cards usually 5–10% discount; >12% = Strong Buy.
+"""
+
+import json
+import re
+from typing import Any
+
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_openai import ChatOpenAI
+
+# Import from project root (damola-adewunmi)
+import sys
+from pathlib import Path
+_root = Path(__file__).resolve().parent.parent
+if str(_root) not in sys.path:
+    sys.path.insert(0, str(_root))
+from prompts import VALUER_SYSTEM_PROMPT, VALUER_USER_TEMPLATE
+
+
+def _parse_valuer_response(text: str) -> list[dict]:
+    """Extract JSON from model output; return list of deals or empty list."""
+    text = (text or "").strip()
+    # Try to find JSON block
+    match = re.search(r"\{[\s\S]*\}", text)
+    if not match:
+        return []
+    try:
+        data = json.loads(match.group())
+        return data.get("deals", []) if isinstance(data, dict) else []
+    except json.JSONDecodeError:
+        return []
+
+
+def valuer_node(state: dict[str, Any]) -> dict[str, Any]:
+    """
+    Node: run GPT-4o with specialist prompt on raw_search_results.
+    Outputs structured valued_deals (retailer, denomination, price, discount_pct, verdict, url).
+    """
+    raw = state.get("raw_search_results") or ""
+    if not raw.strip():
+        return {"valued_deals": []}
+
+    llm = ChatOpenAI(model="gpt-4o", temperature=0)
+    messages = [
+        SystemMessage(content=VALUER_SYSTEM_PROMPT),
+        HumanMessage(content=VALUER_USER_TEMPLATE.format(search_results=raw)),
+    ]
+    response = llm.invoke(messages)
+    content = response.content if hasattr(response, "content") else str(response)
+    valued_deals = _parse_valuer_response(content)
+    return {"valued_deals": valued_deals}