Update app.py

app.py

@@ -1,17 +1,8 @@
-import datetime, tempfile, re, traceback
+import datetime, tempfile, os, traceback
 import gradio as gr
 import pandas as pd
 from fpdf import FPDF
-from transformers import pipeline
-
-print("[INIT] loading FLAN-T5-base ...")
-summariser = pipeline(
-    "text2text-generation",
-    model="google/flan-t5-base",
-    tokenizer="google/flan-t5-base",
-    max_new_tokens=180,
-)
-print("[INIT] FLAN ready.")
+import requests
 
 QUESTIONS = [
     "Governance framework is documented and communicated across the organisation.",
@@ -49,7 +40,8 @@ TIERS = {
 
 def score_to_tier(x):
     for t,(lo,hi) in TIERS.items():
-        if lo<=x<=hi: return t
+        if lo<=x<=hi:
+            return t
     return "Unclassified"
 
 def latin1(t):
@@ -57,32 +49,55 @@ def latin1(t):
             .encode("latin-1","replace").decode("latin-1"))
 
 def llm_remediation(product, b_avgs, overall_tier):
-    bucket_lines = "\n".join(f"{b}: {v:.2f}" for b,v in b_avgs.items())
+    api_key = "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"
+    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}' based on these bucket scores:\n"
+        f"Summarise the AI governance maturity for the product '{product}' at tier '{overall_tier}' based on these bucket scores:\n"
         f"{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("[LLM] prompt >>>\n", prompt)
-        raw = summariser(prompt, temperature=0.0, do_sample=False)[0]["generated_text"]
-        print("[LLM] raw >>>\n", raw)
-    except Exception as e:
-        print("[LLM] ERROR", e)
-        traceback.print_exc()
-        return "LLM summary unavailable."
 
-    # extract bullets if present, otherwise take everything
-    match = re.search(r"(?s)(.+?)(?:\n|$)(- .+)", raw)
-    if match:
-        summary = match.group(1).strip() + "\n" + match.group(2).strip()
-    else:
-        summary = raw.strip()
-    # ensure not too short
-    if len(summary) < 20:
-        summary = (f"{product} is at '{overall_tier}' level. "
-                   "Consider improving governance, privacy, risk and security processes in relevant buckets.")
-    print("[LLM] cleaned >>>\n", summary)
+    if api_key:
+        try:
+            print("[GPT] Sending request to OpenAI API ...")
+            response = requests.post(
+                "https://api.openai.com/v1/chat/completions",
+                headers={
+                    "Authorization": f"Bearer {api_key}",
+                    "Content-Type": "application/json"
+                },
+                json={
+                    "model": "gpt-4o",    # swap for 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,
+                }
+            )
+            data = response.json()
+            print("[GPT] raw response:", data)
+            out = data["choices"][0]["message"]["content"].strip()
+            if len(out) > 30:
+                return out
+        except Exception as e:
+            print("[GPT] ERROR", e)
+            traceback.print_exc()
+
+    bullets = []
+    for bucket, avg in b_avgs.items():
+        tier = score_to_tier(avg)
+        if tier in ["Initial", "Repeatable", "Defined"]:
+            bullets.append(f"- {bucket}: Formalize policies, clarify owners, and address compliance gaps.")
+        elif tier == "Managed":
+            bullets.append(f"- {bucket}: Benchmark against peers and automate monitoring.")
+        else:
+            bullets.append(f"- {bucket}: Continue to optimize and share best practices.")
+    summary = (
+        f"{product} is at '{overall_tier}' maturity. "
+        "Focus on the following improvements:\n" + "\n".join(bullets)
+    )
     return summary
 
 def build_pdf(product,df,avg,tier,path,summary):
@@ -110,23 +125,25 @@ def build_pdf(product,df,avg,tier,path,summary):
 def generate_report(name, *scores):
     product = name.strip() or "your product"
     scores = list(scores)
-    b_avgs = {b: sum(scores[i] for i in idx)/len(idx) for b,idx in BUCKETS.items()}
-    avg = sum(scores)/len(scores)
+    b_avgs = {b: sum(scores[i] for i in idx) / len(idx) for b, idx in BUCKETS.items()}
+    avg = sum(scores) / len(scores)
     tier = score_to_tier(avg)
-    df = pd.DataFrame({"Bucket":b_avgs.keys(),
-                       "Avg":b_avgs.values(),
-                       "Tier":[score_to_tier(v) for v in b_avgs.values()]})
+    df = pd.DataFrame({
+        "Bucket": b_avgs.keys(),
+        "Avg": b_avgs.values(),
+        "Tier": [score_to_tier(v) for v in b_avgs.values()],
+    })
     summary = llm_remediation(product, b_avgs, tier)
-    tmp = tempfile.NamedTemporaryFile(delete=False,suffix=".pdf")
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
     build_pdf(product, df, avg, tier, tmp.name, summary)
     return summary, tmp.name
 
 with gr.Blocks(title="Governance-GPT Quiz") as demo:
     gr.Markdown("# Governance-GPT Quiz")
-    pname = gr.Textbox(label="Product / System Name",placeholder="e.g. AcmeAI Recommender")
-    sliders = [gr.Slider(1,5,3,1,label=q) for q in QUESTIONS]
+    pname = gr.Textbox(label="Product / System Name", placeholder="e.g. AcmeAI Recommender")
+    sliders = [gr.Slider(1, 5, 3, 1, label=q) for q in QUESTIONS]
     btn = gr.Button("Generate PDF Report")
     md_out = gr.Markdown(); file_out = gr.File(label="⬇️ Download PDF")
-    btn.click(generate_report,[pname]+sliders,[md_out,file_out])
+    btn.click(generate_report, [pname] + sliders, [md_out, file_out])
 
-demo.launch(share=True)
+demo.launch()