Update app.py

app.py

@@ -2,10 +2,9 @@ import datetime, tempfile, traceback
 import gradio as gr
 import pandas as pd
 from fpdf import FPDF
-from openai import OpenAI
+import openai
 
-# --------------- Setup OpenAI Client ---------------
-client = OpenAI(api_key="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")
+openai.api_key = "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"
 
 QUESTIONS = [
     "Governance framework is documented and communicated across the organisation.",
@@ -59,17 +58,22 @@ def llm_remediation(product, b_avgs, overall_tier):
         "First, write a one-sentence overall assessment. Then, provide 3-5 markdown bullets suggesting next actions for improvement, mentioning bucket names."
     )
     try:
-        print("[GPT-5-nano] Prompt:", prompt)
-        response = client.responses.create(
-            model="gpt-5-nano",
-            input=prompt
+        print("[OpenAI] Prompt:", prompt)
+        response = openai.ChatCompletion.create(
+            model="gpt-4o",    # "gpt-5-nano" when available
+            messages=[
+                {"role": "system", "content": "You are an expert in AI governance maturity."},
+                {"role": "user", "content": prompt}
+            ],
+            max_tokens=200,
+            temperature=0.3,
         )
-        out = response.output_text.strip()
-        print("[GPT-5-nano] Raw output:", out)
+        out = response["choices"][0]["message"]["content"].strip()
+        print("[OpenAI] Raw output:", out)
         if len(out) > 20:
             return out
     except Exception as e:
-        print("[GPT-5-nano] ERROR:", e)
+        print("[OpenAI] ERROR:", e)
         traceback.print_exc()
 
     # Rule-based fallback if OpenAI call fails