Initial work on standalone query parsing

.gitignore

@@ -121,4 +121,6 @@ dmypy.json
 # End of https://www.gitignore.io/api/python,visualstudiocode
 
 glove/
-venv/
+venv/
+env/
+glove.6B.zip

num_words.pkl

@@ -0,0 +1 @@
+€Mi/.

test_query.py

@@ -0,0 +1,60 @@
+import torch
+import torch.nn as nn
+
+import numpy as np
+
+import models
+
+import pickle
+
+import time
+start_time = time.time()
+
+PAD = "<pad>"
+BOS = "<bos>"
+EOS = "<eos>"
+
+word2idx = pickle.load(open("word2idx.pkl", "rb"))
+wordvecs = pickle.load(open("wordvecs.pkl", "rb"))
+slots = pickle.load(open("slots.pkl", "rb"))
+intents = pickle.load(open("intents.pkl", "rb"))
+num_words = len(word2idx)
+num_intent = 7
+num_slot = 72
+filter_count = 300
+dropout = 0
+embedding_dim = 100
+
+
+def pad_query(sequence):
+        sequence = [word2idx[BOS]] + sequence + [word2idx[EOS]]
+        sequence = sequence[:50]
+        sequence = np.pad(sequence, (0, 50 - len(sequence)), mode='constant', constant_values=(word2idx[PAD],))
+        return sequence
+
+
+query = "What's the weather like in Great Mills right now?"
+q = query.lower().replace("'", " ").replace("?", " ").strip()
+true_length = [len(q.split()), 0, 0, 0, 0, 0, 0 ,0]
+qq = torch.from_numpy(pad_query([word2idx[word] for word in q.split()]))
+    
+model = models.CNNJoint(num_words, embedding_dim, num_intent, num_slot, (filter_count,), 5, dropout, wordvecs)
+model.eval()
+
+model.load_state_dict(torch.load('snips_joint', map_location=torch.device('cpu')))
+criterion = torch.nn.CrossEntropyLoss(ignore_index=-1)
+
+pad_tensor = torch.from_numpy(pad_query([word2idx[w] for w in []]))
+batch = torch.stack([qq, pad_tensor, pad_tensor, pad_tensor, pad_tensor, pad_tensor, pad_tensor, pad_tensor])    
+
+pred_intent, pred_slots = model(batch)
+
+slt = [str(item) for batch_num, sublist in enumerate(pred_slots.max(1)[1].tolist()) for item in sublist[1:true_length[batch_num] + 1]]                                                                                                                      
+out_slots = [slots[int(c)] for c in slt]
+
+itnt = pred_intent.max(1)[1].tolist()[0]
+out_intent = intents[itnt]
+
+print("Input: {}\nIntent: {}\nSlots: {}".format(query, out_intent, out_slots))
+
+print("--- %s seconds ---" % (time.time() - start_time))