delete llm_utils.py

llm_utils.py

@@ -1,53 +0,0 @@
-# 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."
-        )