Update app.py

app.py

@@ -3,11 +3,10 @@ import os, datetime, tempfile, traceback
 import gradio as gr
 import pandas as pd
 from fpdf import FPDF
-from openai import OpenAI
+import openai  # legacy-compatible import
 
-# Use environment variable for security:
-#   export OPENAI_API_KEY="sk-..."
-client = OpenAI()  # picks up OPENAI_API_KEY from env (recommended)  # docs: platform.openai.com
+# Read API key from env (legacy SDK uses this attribute)
+openai.api_key = os.getenv("OPENAI_API_KEY", "")
 
 QUESTIONS = [
     "Governance framework is documented and communicated across the organisation.",
@@ -57,30 +56,29 @@ def latin1(t: str) -> str:
 
 def llm_remediation(product: str, b_avgs: dict, overall_tier: str) -> str:
     """
-    Calls OpenAI Chat Completions with gpt-4o-mini to generate the summary.
-    Uses correct param names for the current SDK: client.chat.completions.create(..., max_tokens=...).
+    Calls OpenAI Chat Completions with gpt-4o-mini (legacy-compatible).
+    Uses `max_tokens` (correct for Chat Completions).
     """
     bucket_lines = "\n".join(f"{b}: {v:.2f}" for b, v in b_avgs.items())
     prompt = (
         f"Summarise the governance maturity for the product '{product}' at tier '{overall_tier}' "
-        f"based on these bucket scores:\n"
-        f"{bucket_lines}\n\n"
+        f"based on these bucket scores:\n{bucket_lines}\n\n"
         "First, write a one-sentence overall assessment. Then, provide 3-5 markdown bullets "
         "suggesting next actions for improvement, mentioning bucket names."
     )
 
     try:
         print("[OpenAI] Prompt:", prompt)
-        completion = client.chat.completions.create(
-            model="gpt-4o-mini",  # OpenAI's cost-efficient multimodal/chat model
+        response = openai.ChatCompletion.create(
+            model="gpt-4o-mini",
             messages=[
                 {"role": "system", "content": "You are an expert in AI governance maturity."},
                 {"role": "user", "content": prompt},
             ],
-            max_tokens=300,       # correct parameter name for chat completions
-            temperature=0.7,      # allowed for this model; adjust to taste
+            max_tokens=300,    # correct param name for this endpoint
+            temperature=0.7,   # allowed on chat models
         )
-        out = (completion.choices[0].message.content or "").strip()
+        out = (response["choices"][0]["message"]["content"] or "").strip()
         print("[OpenAI] Raw output:", out)
         if len(out) > 20:
             return out
@@ -154,5 +152,5 @@ with gr.Blocks(title="Governance-GPT Quiz") as demo:
     btn.click(generate_report, [pname] + sliders, [md_out, file_out])
 
 if __name__ == "__main__":
-    # You can pass share=True to get a public link if you like.
+    # Set share=True if you want a public link
     demo.launch()