Upload Code_files/uslap.py with huggingface_hub

Code_files/uslap.py

@@ -171,8 +171,7 @@ def query(text):
     Fixed 2026-04-02: tasrif now routes through dhakaa pipeline
     (perceive → reason → articulate) instead of bypass.
 
-    Fixed 2026-04-15: DB hits no longer trigger hypothesis append.
-    Transliterated AA queries route to root lookup, not hypothesis.
+    Fixed 2026-04-15: DB hits no longer trigger hypothesis append after result.
     """
     from uslap_handler import search
 
@@ -204,49 +203,13 @@ def query(text):
         lines.append("╚" + "═" * 58 + "╝")
         return '\n'.join(lines)
 
-    # Step 2: check if the intent is a structural query (tasrif, intel, explain,
-    # compare, state, quf) — these route through think() regardless of DB hit.
-    stripped = text.strip().lower()
-    structural_prefixes = (
-        'tasrif', 'intel ', 'explain ', 'compare ', '--state',
-        '--quf', '--batch', 'bitig tasrif',
-    )
-    is_structural = any(stripped.startswith(p) for p in structural_prefixes)
-
-    if is_structural:
-        from amr_dhakaa import think
-        result = think(text)
-        return result.get('output', '')
-
-    # Step 3: plain word lookup — check entries table directly before running
-    # the full hypothesis engine. If the word has no confirmed entry, stop here.
-    # Hypothesis mode is for local development only, not public output.
-    import sqlite3
-    db_path = os.path.join(os.path.dirname(__file__), 'uslap_database_v3.db')
-    word = stripped.replace('trace ', '').strip()
-    try:
-        conn = sqlite3.connect(db_path)
-        hit = conn.execute(
-            "SELECT entry_id FROM entries WHERE LOWER(en_term) = ? LIMIT 1",
-            (word,)
-        ).fetchone()
-        conn.close()
-        if not hit:
-            return f"Not found in DB: '{word}'"
-    except Exception:
-        pass
-
+    # Not found in DB index — run full AMR pipeline (hypothesis/candidate engine).
+    # amr_dhakaa.think() handles all intent routing: trace_word → hypothesise()
+    # finds phonetic candidate roots, scores them, applies QUF gate.
+    # This IS the core feature for unconfirmed downstream words.
     from amr_dhakaa import think
     result = think(text)
-    output = result.get('output', '')
-
-    # Final safety: block any hypothesis that slipped through.
-    output_lines = output.splitlines()
-    for line in output_lines[:4]:
-        if 'HYPOTHESIS:' in line:
-            return f"Not found in DB: '{word}'"
-
-    return output
+    return result.get('output', '')
 
 
 def full_report(text):