Update app.py

app.py

@@ -1,19 +1,21 @@
 """
-Governance-GPT Quiz · ASCII-safe PDF with free HF summariser
+Governance-GPT Quiz · ASCII-safe PDF with logged summariser
 """
 
-import datetime, tempfile, re
+import datetime, tempfile, re, traceback
 import gradio as gr
 import pandas as pd
 from fpdf import FPDF
 from transformers import pipeline
 
-# lightweight, free CPU summariser
+# ---------- model ---------- #
+print("[INIT] loading summariser …")
 summariser = pipeline(
     "summarization",
-    model="sshleifer/distilbart-cnn-12-6",
+    model="sshleifer/distilbart-cnn-12-6",      # same model most HF demo Spaces use
     tokenizer="sshleifer/distilbart-cnn-12-6",
 )
+print("[INIT] summariser ready")
 
 QUESTIONS = [
     "Governance framework is documented and communicated across the organisation.",
@@ -51,25 +53,43 @@ 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):
-    return t.replace("–","-").replace("—","-").replace("•","-")\
-            .encode("latin-1","replace").decode("latin-1")
+    return (t.replace("–","-").replace("—","-").replace("•","-")
+            .encode("latin-1","replace").decode("latin-1"))
 
+# ---------- LLM helper with logging ---------- #
 def llm_remediation(product,b_avgs,overall_tier):
-    lines="\n".join(f"- {b}: {v:.2f}" for b,v in b_avgs.items())
+    bucket_lines="\n".join(f"- {b}: {v:.2f}" for b,v in b_avgs.items())
     prompt=(f"Product: {product}\nOverall tier: {overall_tier}\n"
-            f"Bucket scores:\n{lines}\n\n"
+            f"Bucket scores:\n{bucket_lines}\n\n"
             "Write one assessment sentence and 3-5 bullet remediation actions "
             "referencing bucket names. Return only the summary.")
-    out=summariser(prompt,max_length=120,min_length=40,do_sample=False)[0]["summary_text"]
-    clean="\n".join(ln for ln in out.splitlines()
-                    if not re.search(r"assessment sentence|bullet remediation",ln,flags=re.I)).strip()
-    return clean.replace("•","- ") or "LLM summary unavailable."
+    try:
+        print("[LLM] prompt:\n", prompt)
+        result = summariser(prompt,
+                            max_length=80,   # shorter than default to avoid warnings
+                            min_length=20,
+                            do_sample=False)[0]["summary_text"]
+        print("[LLM] raw output:\n", result)
+    except Exception as e:
+        print("[LLM] ERROR:", e)
+        traceback.print_exc()
+        return "LLM summary unavailable."
+
+    clean = "\n".join(
+        ln for ln in result.splitlines()
+        if not re.search(r"assessment sentence|bullet remediation", ln, re.I)
+    ).replace("•","- ").strip()
+
+    print("[LLM] cleaned summary:\n", clean)
+    return clean or "LLM summary unavailable."
 
 def build_pdf(product,df,avg,tier,path,summary):
+    print("[PDF] building report …")
     pdf=FPDF(); pdf.set_auto_page_break(auto=True,margin=15); pdf.add_page()
     pdf.set_font("Helvetica","B",16)
     pdf.cell(0,10,latin1(f"AI Governance Maturity Report - {product}"),ln=1,align="C")
@@ -90,11 +110,14 @@ def build_pdf(product,df,avg,tier,path,summary):
     pdf.cell(35,8,f"{avg:.2f}",1)
     pdf.cell(35,8,tier,1,ln=1)
     pdf.output(path)
+    print("[PDF] saved at", path)
 
 def generate_report(name,*scores):
-    product=name.strip() or "your product"; scores=list(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); tier=score_to_tier(avg)
+    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()]})
@@ -111,4 +134,5 @@ with gr.Blocks(title="Governance-GPT Quiz") as demo:
     out_md=gr.Markdown(); out_pdf=gr.File(label="⬇️ Download PDF")
     btn.click(generate_report,[name]+sliders,[out_md,out_pdf])
 
-demo.launch()
+# set share=True for public URL
+demo.launch(share=True)