Update llm_utils.py

llm_utils.py

@@ -1,38 +1,36 @@
-# llm_utils.py
 import os
 from openai import OpenAI
 
-
-# Option 1 (recommended): set OPENAI_API_KEY in Hugging Face Secrets
+# You can either:
+# 1) Set OPENAI_API_KEY in Hugging Face Secrets
+# 2) Or replace "YOUR_OPENAI_API_KEY" with your key directly (less secure)
 API_KEY = os.getenv("OPENAI_API_KEY", "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")
 
-
 client = OpenAI(api_key=API_KEY)
 
 
 def explain_savings_plan(payload: dict) -> str:
     """
-    Uses a small LLM model to turn numeric savings plan outputs into a
+    Uses a small LLM model to turn the numeric savings plan into a
     short, plain-English explanation.
 
-    The LLM:
-    - MUST NOT invent new numbers
-    - Only rephrases the fields in payload
-    - Does not change the calculation, only explains it
+    Guardrails:
+    - MUST NOT invent any new numbers
+    - Only uses fields in payload
+    - Explanation only; no decision-making
     """
 
     system_prompt = """
-You are an AI assistant that explains mortgage down-payment savings plans.
-You MUST NOT invent any new numbers.
+You are an AI assistant that explains home down-payment savings plans for first-time buyers.
+You MUST NOT invent or change any numeric values.
 Use ONLY the fields provided in the JSON payload.
 Write 2–3 short sentences that:
   1) State what the user is trying to achieve (reach a down payment).
   2) Explain why this recommended monthly savings amount makes sense.
   3) Mention the timeline (years and months).
-Be neutral, clear, and non-promotional.
+Be neutral, concise, and non-promotional.
     """.strip()
 
-    # We send the raw payload as JSON text in the user message.
     user_content = f"Here is the JSON for this user's plan: {payload}"
 
     try:
@@ -42,16 +40,16 @@ Be neutral, clear, and non-promotional.
                 {"role": "system", "content": system_prompt},
                 {"role": "user", "content": user_content},
             ],
-            max_tokens=200,
+            max_tokens=220,
             temperature=0.3,
         )
         explanation = completion.choices[0].message.content.strip()
         return explanation
-    except Exception as e:
-        # Fallback if LLM fails
+    except Exception:
+        # Safe fallback explanation if LLM is unavailable
         return (
-            "We calculated your recommended monthly savings based on your home budget, "
-            "target down payment %, current savings, and chosen timeline. "
-            "The amount spreads your remaining down-payment need over the available months, "
-            "adding estimated closing costs and subtracting expected interest earnings."
+            "We calculated your recommended monthly savings using your home budget, "
+            "target down payment percentage, current savings, and chosen timeline. "
+            "The remaining amount needed is spread over the available months, "
+            "with estimated closing costs added and expected interest on savings subtracted."
         )