senses working

src/backend_flask/app.py

@@ -244,7 +244,7 @@ def prepare_frames(ret_frames):
         # sid, tid==hw
         for sid, tid in unique_sids.items():
             # hwl = vallex.get_token(tid)["lemma"]
-            hwl = "derp"
+            hwl = frame.hw
             tmp_idx = len(frame.sentences)
             if hwl not in frame.aggr_sent:
                 frame.aggr_sent[hwl] = []
@@ -273,15 +273,14 @@ def api_get_frames():
 
     # entry = vallex.entries[hw]  # TODO  hw -> [Frame,]
     cur = valdb[corpus].find({"headwords": hw})
-    print("N_results: " + str(cur.count()))
     frames = []
     for ent in cur:
         # TODO: maybe filter by hw?
-        frames += frames_from_db_entry(ent)
+        frames += frames_from_db_entry(ent)  # pre-process this step for prod TODO
 
-    # return json.dumps([x.to_json() for x in frames])
+    frames = [x for x in frames if x.hw == hw]
 
-    ret_frames = RF(frames, None)
+    ret_frames = RF(frames, valdb[SENSEMAP_COLL])
     return prepare_frames(ret_frames)
 
 

src/frontend_vue/src/components/MainDispl.vue

@@ -169,6 +169,7 @@ export default {
             }
             this.sentences = {}
             for (var fi in this.frames) {
+                console.log(this.frames[fi].sentences)
                 for (var si in this.frames[fi].sentences) {
                     var sentence = this.frames[fi].sentences[si]
                     // get ssj_id without .t123
@@ -184,7 +185,7 @@ export default {
                     }
                     */
                     for (var i in sentence) {
-                        words.push(sentence[i][1].word)
+                        words.push(sentence[i][1].text)
                         if (sentence[i][1].lemma === tmp_hw && hw_idx == -1) {
                             hw_idx = i
                         }

src/pkg/valency/valency/Frame.py

@@ -20,7 +20,7 @@ def frames_from_db_entry(dbent):
             srldict[key] += [srl]
     for hwtid, srlarr in srldict.items():
         frames += [Frame(
-            hw_lemma=token_dict[hwtid]["text"],
+            hw_lemma=token_dict[hwtid]["lemma"],
             tids=[_full_tid(hwtid)],
             slots=[
                 Slot(

src/pkg/valency/valency/reduce_functions.py

@@ -22,7 +22,7 @@ def sorted_by_len_tids(frames):
     )
 
 
-def reduce_0(frames, vallex=None):
+def reduce_0(frames, valdb_sensemap=None):
     # new request... frames should be sorded by
     # functors list (basically reduce_1, just each
     # sentence gets its own frame)
@@ -43,7 +43,7 @@ def reduce_0(frames, vallex=None):
     return sorted_sep_frames
 
 
-def reduce_1(frames, vallex=None):
+def reduce_1(frames, valdb_sensemap=None):
     # Combine frames with the same set of functors.
     # The order of functors is not important.
     frame_sets = []  # [set of functors, list of frames]
@@ -76,12 +76,15 @@ def reduce_1(frames, vallex=None):
         slots_list = []
         for k, e in slots.items():
             slots_list.append(e)
-        rf = Frame(tids=tids, slots=slots_list, sentences=sentences)
+        # TODO does appending hw_lemma of first frame work for functor frames too?
+        rf = Frame(hw_lemma=fs[1][0].hw, tids=tids, slots=slots_list, sentences=sentences)
         rf.sort_slots()
         ret_frames.append(rf)
     return sorted_by_len_tids(ret_frames)
 
 
+# deprecated
+"""
 def reduce_3(raw_frames, vallex):
     # sskj simple lesk ids
     ssj_ids = [frame.tids[0] for frame in raw_frames]
@@ -94,8 +97,11 @@ def reduce_3(raw_frames, vallex):
             "sense_desc": entry.get("sense_desc")
         }})
     return frames_from_sense_ids(raw_frames, id_map)
+"""
 
 
+#deprecated
+"""
 def reduce_4(raw_frames, vallex):
     # kmeans ids
     ssj_ids = [frame.tids[0] for frame in raw_frames]
@@ -107,15 +113,15 @@ def reduce_4(raw_frames, vallex):
             "sense_id": entry["sense_id"]
         }})
     return frames_from_sense_ids(raw_frames, id_map)
+"""
 
 
-def reduce_5(raw_frames, vallex):
-    USER_SENSE_COLL = "v2_sense_map"
+def reduce_5(raw_frames, valdb_sensemap):
     headword = raw_frames[0].hw
     ssj_ids_full = [frame.tids[0] for frame in raw_frames]
     # v2_sense_map stores only sentence half of ssj_id
     ssj_ids = [".".join(ssj_id.split(".")[:-1]) for ssj_id in ssj_ids_full]
-    db_results = list(vallex.db[USER_SENSE_COLL].find({
+    db_results = list(valdb_sensemap.find({
         "ssj_id": {"$in": ssj_ids},
         "hw": headword,
     }))
@@ -173,8 +179,10 @@ def frames_from_sense_ids(raw_frames, id_map):
     for sense_id, frames in id_dict.items():
         tids = []
         reduced_slots = []
+        sentences = []
         for frame in frames:
-            tids.extend(frame.tids)
+            tids += frame.tids
+            sentences += frame.sentences
             for slot in frame.slots:
                 # if functor not in reduced slots,
                 # add new slot; else increase count
@@ -190,7 +198,12 @@ def frames_from_sense_ids(raw_frames, id_map):
                         tids=slot.tids,
                         count=1
                     ))
-        reduced_frame = Frame(tids, slots=reduced_slots)
+        reduced_frame = Frame(
+            hw_lemma="derp",
+            tids=tids,
+            slots=reduced_slots,
+            sentences=sentences
+        )
         id_map_entry = (
             id_map.get(tids[0]) or
             id_map.get(".".join(tids[0].split(".")[:-1]))
@@ -222,14 +235,14 @@ reduce_functions = {
         "simple_name": "združeni stavki"
     },
     "reduce_3": {
-        "f": reduce_3,
+        "f": None,  # deprecated
         "desc":
         "Združevanje stavčnih vzorcev na osnovi pomenov povedi v SSKJ. "
         "Pomeni so dodeljeni s pomočjo algoritma Simple Lesk.",
         "simple_name": "SSKJ_pomeni"
     },
     "reduce_4": {
-        "f": reduce_4,
+        "f": None,  # deprecated
         "desc":
         "Združevanje stavčnih vzorcev na osnovi pomenov povedi "
         "s pomočjo algoritma K-Means. Število predvidenih pomenov "