Create llm_utils.py

src/llm_utils.py

@@ -0,0 +1,53 @@
+# src/llm_utils.py
+import os
+from openai import OpenAI
+
+# You can either:
+# - Set OPENAI_API_KEY as a secret in Hugging Face, OR
+# - Replace "YOUR_OPENAI_API_KEY" directly (less secure)
+API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_OPENAI_API_KEY")
+
+client = OpenAI(api_key=API_KEY)
+
+
+def explain_savings_plan(payload: dict) -> str:
+    """
+    Calls a small LLM model to explain the numeric savings plan.
+    Guardrails:
+    - Do NOT invent new numbers
+    - Use ONLY fields from payload
+    - Explanation-only; no decisions
+    """
+    system_prompt = """
+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, concise, and non-promotional.
+    """.strip()
+
+    user_content = f"Here is the JSON for this user's plan: {payload}"
+
+    try:
+        completion = client.chat.completions.create(
+            model="gpt-4.1-mini",
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_content},
+            ],
+            max_tokens=220,
+            temperature=0.3,
+        )
+        explanation = completion.choices[0].message.content.strip()
+        return explanation
+    except Exception:
+        # Safe fallback if LLM fails
+        return (
+            "We calculated your recommended monthly savings using your home budget, "
+            "target down payment percentage, current savings, and chosen timeline. "
+            "The remaining amount is spread over the available months, with estimated "
+            "closing costs added and expected interest on savings subtracted."
+        )