fix: upload actual compare.py content with all v4.1 fixes

compare.py

@@ -1 +1,333 @@
-file:/app/compare.py
+"""
+ClauseGuard — Contract Comparison Engine v3.1
+═════════════════════════════════════════════
+FIXED in v3.1:
+  • PERF: Pre-compute all embeddings once, use matrix multiplication (was O(n²) per-pair encoding)
+  • FIX: Shared SentenceTransformer singleton (no duplicate model loading)
+  • FIX: Raised similarity thresholds to reduce false matches
+"""
+
+import re
+from difflib import SequenceMatcher
+from collections import defaultdict
+import numpy as np
+
+# Try to load sentence-transformers for semantic comparison
+_HAS_EMBEDDINGS = False
+_embedder = None
+
+try:
+    from sentence_transformers import SentenceTransformer
+    _HAS_EMBEDDINGS = True
+except ImportError:
+    pass
+
+
+def _load_embedder():
+    """Load shared SentenceTransformer singleton."""
+    global _embedder
+    if _HAS_EMBEDDINGS and _embedder is None:
+        try:
+            _embedder = SentenceTransformer("all-MiniLM-L6-v2")
+            print("[ClauseGuard] Sentence embeddings loaded for comparison")
+        except Exception as e:
+            print(f"[ClauseGuard] Embeddings not available: {e}")
+
+
+def _normalize_clause(text):
+    """Normalize clause text for comparison."""
+    text = text.lower()
+    text = re.sub(r'[^a-z0-9\s]', ' ', text)
+    text = re.sub(r'\s+', ' ', text).strip()
+    return text
+
+
+def _compute_similarity_matrix(clauses_a, clauses_b):
+    """
+    FIX v3.1: Compute similarity matrix using pre-computed embeddings + matrix multiply.
+    Was: O(n²) individual encode() calls per pair.
+    Now: O(n+m) encode calls + O(n*m) dot product (fast numpy).
+    """
+    if _embedder is not None:
+        try:
+            # Encode all clauses at once (batched)
+            texts_a = [c[:512] for c in clauses_a]
+            texts_b = [c[:512] for c in clauses_b]
+            emb_a = _embedder.encode(texts_a, normalize_embeddings=True, batch_size=32, show_progress_bar=False)
+            emb_b = _embedder.encode(texts_b, normalize_embeddings=True, batch_size=32, show_progress_bar=False)
+            # Cosine similarity via dot product (embeddings are L2-normalized)
+            sim_matrix = np.dot(emb_a, emb_b.T)
+            return sim_matrix, "semantic"
+        except Exception:
+            pass
+
+    # Fallback: string matching (still compute matrix)
+    n, m = len(clauses_a), len(clauses_b)
+    sim_matrix = np.zeros((n, m))
+    for i in range(n):
+        norm_a = _normalize_clause(clauses_a[i])
+        for j in range(m):
+            norm_b = _normalize_clause(clauses_b[j])
+            sim_matrix[i, j] = SequenceMatcher(None, norm_a, norm_b).ratio()
+    return sim_matrix, "lexical"
+
+
+def _extract_clause_type(clause_text):
+    """Clause type detection with legal taxonomy."""
+    text_lower = clause_text.lower()
+    type_keywords = {
+        "governing law": ["govern", "law of", "jurisdiction of", "applicable law"],
+        "termination": ["terminat", "cancel", "expir"],
+        "indemnification": ["indemnif", "hold harmless", "defend and indemnify"],
+        "confidentiality": ["confidential", "non-disclosure", "nda", "proprietary"],
+        "liability": ["liability", "liable", "damages", "limitation of"],
+        "payment": ["payment", "fee", "price", "compensat", "invoice", "remit"],
+        "intellectual property": ["intellectual property", "ip rights", "copyright", "patent", "trademark"],
+        "warranty": ["warrant", "guarantee", "representation"],
+        "force majeure": ["force majeure", "act of god", "beyond control"],
+        "arbitration": ["arbitrat", "mediation", "dispute resolution"],
+        "assignment": ["assign", "transfer of rights"],
+        "non-compete": ["non-compete", "not compete", "competition"],
+        "renewal": ["renew", "extend", "automatic renewal"],
+        "effective date": ["effective date", "commencement"],
+        "insurance": ["insurance", "coverage", "policy of insurance"],
+        "audit": ["audit", "inspection", "examination of records"],
+        "data protection": ["data protection", "privacy", "personal data", "gdpr", "ccpa"],
+        "notice": ["notice", "notification", "written notice"],
+    }
+    for ctype, keywords in type_keywords.items():
+        if any(kw in text_lower for kw in keywords):
+            return ctype
+    return "general"
+
+
+def compare_contracts(text_a, text_b, clauses_a=None, clauses_b=None):
+    """Compare two contracts with semantic similarity."""
+    if not text_a or not text_b:
+        return {"error": "Both contracts required"}
+
+    _load_embedder()
+
+    if clauses_a is None:
+        clauses_a = _split_clauses(text_a)
+    if clauses_b is None:
+        clauses_b = _split_clauses(text_b)
+
+    # Detect contract types and flag cross-domain comparisons
+    _CONTRACT_TYPE_KEYWORDS = {
+        "employment": ["employee", "employer", "salary", "compensation", "benefits", "vacation", "severance", "at-will"],
+        "lease": ["landlord", "tenant", "rent", "premises", "lease", "occupancy", "security deposit", "eviction"],
+        "service": ["service provider", "customer", "SLA", "deliverables", "statement of work", "SOW"],
+        "nda": ["confidential", "non-disclosure", "disclosing party", "receiving party"],
+        "saas": ["subscription", "SaaS", "cloud", "uptime", "API", "data processing"],
+        "purchase": ["buyer", "seller", "purchase order", "goods", "shipment", "delivery"],
+    }
+
+    def _detect_contract_type(text):
+        text_lower = text.lower()
+        scores = {}
+        for ctype, keywords in _CONTRACT_TYPE_KEYWORDS.items():
+            scores[ctype] = sum(1 for kw in keywords if kw.lower() in text_lower)
+        best = max(scores, key=scores.get)
+        return best if scores[best] >= 2 else "general"
+
+    type_a = _detect_contract_type(text_a)
+    type_b = _detect_contract_type(text_b)
+    is_cross_domain = type_a != type_b and type_a != "general" and type_b != "general"
+
+    # Build clause type maps
+    type_map_a = defaultdict(list)
+    type_map_b = defaultdict(list)
+    for c in clauses_a:
+        type_map_a[_extract_clause_type(c)].append(c)
+    for c in clauses_b:
+        type_map_b[_extract_clause_type(c)].append(c)
+
+    # FIX v3.1: Compute similarity matrix once (O(n+m) encoding + O(n*m) dot product)
+    if clauses_a and clauses_b:
+        sim_matrix, method_type = _compute_similarity_matrix(clauses_a, clauses_b)
+    else:
+        sim_matrix = np.zeros((0, 0))
+        method_type = "none"
+
+    # Find matches using the pre-computed matrix
+    matched_a = set()
+    matched_b = set()
+    modified = []
+
+    SIMILARITY_THRESHOLD = 0.75
+    MODIFIED_THRESHOLD = 0.55
+
+    for i in range(len(clauses_a)):
+        if len(clauses_b) == 0:
+            break
+        # Find best match for clause i in A
+        row = sim_matrix[i]
+        # Mask already-matched B clauses
+        available = np.ones(len(clauses_b), dtype=bool)
+        for j in matched_b:
+            available[j] = False
+        if not available.any():
+            break
+        masked_row = np.where(available, row, -1.0)
+        best_j = int(np.argmax(masked_row))
+        best_sim = masked_row[best_j]
+
+        if best_sim >= SIMILARITY_THRESHOLD:
+            matched_a.add(i)
+            matched_b.add(best_j)
+            if best_sim < 0.95:
+                modified.append({
+                    "type": "modified",
+                    "similarity": round(float(best_sim), 3),
+                    "clause_a": clauses_a[i][:200],
+                    "clause_b": clauses_b[best_j][:200],
+                    "clause_type": _extract_clause_type(clauses_a[i]),
+                })
+        elif best_sim >= MODIFIED_THRESHOLD:
+            matched_a.add(i)
+            matched_b.add(best_j)
+            modified.append({
+                "type": "partial",
+                "similarity": round(float(best_sim), 3),
+                "clause_a": clauses_a[i][:200],
+                "clause_b": clauses_b[best_j][:200],
+                "clause_type": _extract_clause_type(clauses_a[i]),
+            })
+
+    removed = [clauses_a[i] for i in range(len(clauses_a)) if i not in matched_a]
+    added = [clauses_b[j] for j in range(len(clauses_b)) if j not in matched_b]
+
+    # Compute alignment score
+    total_pairs = max(len(clauses_a), len(clauses_b))
+    alignment = len(matched_a) / total_pairs if total_pairs > 0 else 0.0
+
+    # Risk delta
+    risk_keywords = ["unlimited", "unilateral", "waive", "arbitration", "indemnif",
+                     "not liable", "no warranty", "sole discretion", "terminate",
+                     "non-compete", "liquidated damages", "uncapped"]
+    risk_a = sum(1 for kw in risk_keywords if kw in text_a.lower())
+    risk_b = sum(1 for kw in risk_keywords if kw in text_b.lower())
+
+    if risk_a > risk_b + 2:
+        risk_delta = "Contract A is significantly riskier"
+        risk_winner = "B"
+    elif risk_b > risk_a + 2:
+        risk_delta = "Contract B is significantly riskier"
+        risk_winner = "A"
+    elif risk_a > risk_b:
+        risk_delta = "Contract A is slightly riskier"
+        risk_winner = "B"
+    elif risk_b > risk_a:
+        risk_delta = "Contract B is slightly riskier"
+        risk_winner = "A"
+    else:
+        risk_delta = "Similar risk profiles"
+        risk_winner = "tie"
+
+    if is_cross_domain:
+        risk_delta = f"Cross-domain comparison ({type_a} vs {type_b}) — risk delta not meaningful across different contract types"
+        risk_winner = "cross-domain"
+
+    comparison_method = f"semantic (sentence embeddings)" if method_type == "semantic" else "lexical (string matching)"
+
+    return {
+        "alignment_score": round(alignment, 3),
+        "contract_a_clauses": len(clauses_a),
+        "contract_b_clauses": len(clauses_b),
+        "contract_a_type": type_a,
+        "contract_b_type": type_b,
+        "is_cross_domain": is_cross_domain,
+        "added_clauses": [{"text": c[:200], "type": _extract_clause_type(c)} for c in added[:50]],
+        "removed_clauses": [{"text": c[:200], "type": _extract_clause_type(c)} for c in removed[:50]],
+        "modified_clauses": modified[:50],
+        "risk_delta": risk_delta,
+        "risk_winner": risk_winner,
+        "comparison_method": comparison_method,
+        "type_map_a": {k: len(v) for k, v in type_map_a.items()},
+        "type_map_b": {k: len(v) for k, v in type_map_b.items()},
+    }
+
+
+def _split_clauses(text):
+    """Split text into clauses."""
+    text = re.sub(r'\n{3,}', '\n\n', text.strip())
+    section_splits = re.split(
+        r'(?:\n\n)(?=\d+[.)]\s|\([a-z]\)\s|(?:Section|Article|Clause)\s+\d+)',
+        text
+    )
+    if len(section_splits) >= 3:
+        return [p.strip() for p in section_splits if len(p.strip()) > 30]
+    parts = re.split(
+        r'(?<=[.!?])\s+(?=[A-Z0-9(])|(?:\n\n)',
+        text
+    )
+    return [p.strip() for p in parts if len(p.strip()) > 30]
+
+
+def render_comparison_html(result):
+    """Render comparison results as HTML for Gradio."""
+    if "error" in result:
+        return f'<p style="color:#dc2626;">{result["error"]}</p>'
+
+    method = result.get("comparison_method", "unknown")
+    method_badge = f'<div style="font-size:10px;color:#6b7280;text-align:center;margin-bottom:12px;">Comparison method: {method}</div>'
+
+    html = f'''
+    <div style="font-family:system-ui,sans-serif;">
+      {method_badge}
+      <div style="display:grid;grid-template-columns:1fr 1fr;gap:12px;margin-bottom:16px;">
+        <div style="padding:12px;border-radius:8px;background:#eff6ff;border:1px solid #bfdbfe;text-align:center;">
+          <div style="font-size:24px;font-weight:700;color:#1d4ed8;">{result["contract_a_clauses"]}</div>
+          <div style="font-size:12px;color:#3b82f6;">Clauses in Contract A</div>
+        </div>
+        <div style="padding:12px;border-radius:8px;background:#fefce8;border:1px solid #fde68a;text-align:center;">
+          <div style="font-size:24px;font-weight:700;color:#a16207;">{result["contract_b_clauses"]}</div>
+          <div style="font-size:12px;color:#ca8a04;">Clauses in Contract B</div>
+        </div>
+      </div>
+
+      <div style="padding:12px;border-radius:8px;background:#f9fafb;border:1px solid #e5e7eb;margin-bottom:16px;text-align:center;">
+        <div style="font-size:28px;font-weight:700;color:#374151;">{result["alignment_score"]*100:.1f}%</div>
+        <div style="font-size:12px;color:#6b7280;">Alignment Score</div>
+      </div>
+
+      <div style="padding:12px;border-radius:8px;background:{
+          "#fef2f2" if result["risk_winner"] != "tie" else "#f0fdf4"
+      };border:1px solid {
+          "#fecaca" if result["risk_winner"] != "tie" else "#bbf7d0"
+      };margin-bottom:16px;text-align:center;">
+        <span style="font-size:14px;font-weight:600;color:{
+            "#dc2626" if result["risk_winner"] != "tie" else "#16a34a"
+        };">⚖️ {result["risk_delta"]}</span>
+      </div>
+    '''
+
+    if result["modified_clauses"]:
+        html += '<div style="margin-bottom:16px;"><h3 style="font-size:14px;color:#374151;margin-bottom:8px;">📝 Modified Clauses</h3>'
+        for m in result["modified_clauses"][:20]:
+            html += f'''
+            <div style="border:1px solid #e5e7eb;border-radius:6px;padding:10px;margin-bottom:8px;">
+              <div style="font-size:11px;color:#6b7280;margin-bottom:4px;">{m["clause_type"].upper()} · Similarity: {m["similarity"]*100:.0f}%</div>
+              <div style="display:grid;grid-template-columns:1fr 1fr;gap:8px;">
+                <div style="background:#fef2f2;padding:6px;border-radius:4px;font-size:12px;color:#991b1b;">{m["clause_a"][:150]}...</div>
+                <div style="background:#f0fdf4;padding:6px;border-radius:4px;font-size:12px;color:#166534;">{m["clause_b"][:150]}...</div>
+              </div>
+            </div>
+            '''
+        html += '</div>'
+
+    if result["added_clauses"]:
+        html += '<div style="margin-bottom:16px;"><h3 style="font-size:14px;color:#374151;margin-bottom:8px;">➕ Added in Contract B</h3>'
+        for a in result["added_clauses"][:15]:
+            html += f'<div style="background:#f0fdf4;padding:8px;border-radius:4px;font-size:12px;color:#166534;margin-bottom:4px;border-left:3px solid #22c55e;"><b>{a["type"].upper()}</b> · {a["text"][:150]}...</div>'
+        html += '</div>'
+
+    if result["removed_clauses"]:
+        html += '<div style="margin-bottom:16px;"><h3 style="font-size:14px;color:#374151;margin-bottom:8px;">➖ Removed from Contract A</h3>'
+        for r in result["removed_clauses"][:15]:
+            html += f'<div style="background:#fef2f2;padding:8px;border-radius:4px;font-size:12px;color:#991b1b;margin-bottom:4px;border-left:3px solid #ef4444;"><b>{r["type"].upper()}</b> · {r["text"][:150]}...</div>'
+        html += '</div>'
+
+    html += '</div>'
+    return html