done

app.py

@@ -93,6 +93,7 @@ def analyze_code(code: str, language: str):
     clean_conf = round(probs[0] * 100, 1)
     vuln_conf  = round(probs[1] * 100, 1)
 
+    # ── Static rule scan ───────────────────────────────────
     findings, matched_categories = [], set()
     for pattern, message in VULN_RULES.get(language, []):
         if re.search(pattern, code, re.IGNORECASE):
@@ -111,26 +112,46 @@ def analyze_code(code: str, language: str):
                 elif "shell" in message.lower() or "subprocess" in message.lower():
                     matched_categories.add("command_injection")
 
+    # ── Code quality score (10 = perfect, 0 = critical) ───
+    # Start at 10, deduct for each issue found
     model_uncertain = abs(vuln_conf - clean_conf) < 15.0
-    if model_uncertain and len(findings) == 0:
-        score = 0.0
+
+    # Deductions
+    model_penalty    = (vuln_conf / 100) * 4.0 if not model_uncertain else 0.0
+    findings_penalty = min(len(findings) * 1.2, 6.0)
+    total_deduction  = round(min(model_penalty + findings_penalty, 10.0), 1)
+
+    quality_score = round(max(10.0 - total_deduction, 0.0), 1)
+
+    # ── Grade label ────────────────────────────────────────
+    if quality_score >= 9.0:
+        grade, verdict = "A",  "EXCELLENT"
+    elif quality_score >= 7.5:
+        grade, verdict = "B",  "GOOD"
+    elif quality_score >= 6.0:
+        grade, verdict = "C",  "FAIR"
+    elif quality_score >= 4.0:
+        grade, verdict = "D",  "POOR"
+    elif quality_score >= 2.0:
+        grade, verdict = "F",  "VULNERABLE"
     else:
-        finding_penalty = min(len(findings) * 0.8, 4.0)
-        base_score      = (vuln_conf / 100) * 6.0 if not model_uncertain else 0.0
-        score           = round(min(base_score + finding_penalty, 10.0), 1)
+        grade, verdict = "F-", "CRITICAL"
 
-    if score >= 7.0:   risk, verdict = "critical", "VULNERABLE"
-    elif score >= 4.5: risk, verdict = "high",     "VULNERABLE"
-    elif score >= 2.5: risk, verdict = "medium",   "REVIEW NEEDED"
-    elif score >= 1.0: risk, verdict = "low",      "MOSTLY SAFE"
-    else:              risk, verdict = "safe",      "CLEAN"
+    # ── Risk label (kept for API compatibility) ────────────
+    if quality_score <= 3.0:   risk = "critical"
+    elif quality_score <= 5.0: risk = "high"
+    elif quality_score <= 6.5: risk = "medium"
+    elif quality_score <= 8.5: risk = "low"
+    else:                      risk = "safe"
 
+    # ── Smart tips ─────────────────────────────────────────
     tips    = [SMART_TIPS[c] for c in matched_categories if c in SMART_TIPS]
     general = CODE_TIPS.get(language, ["Follow security best practices."])
     tips   += random.sample(general, min(max(0, 3 - len(tips)), len(general)))
 
     return {
-        "score":            score,
+        "score":            quality_score,   # now 10 = best, 0 = worst
+        "grade":            grade,
         "risk":             risk,
         "verdict":          verdict,
         "clean_confidence": clean_conf,