fix: dialogpt (#223)

* fix context; cut unfinished responses

* fix context

* codestyle fix

services/dialogpt/server.py

@@ -2,6 +2,7 @@
 import json
 import os
 import time
+import re
 
 import sentry_sdk
 import torch
@@ -21,7 +22,8 @@
 logging.info(f"PRETRAINED_MODEL_NAME_OR_PATH = {PRETRAINED_MODEL_NAME_OR_PATH}")
 DEFAULT_CONFIDENCE = 0.9
 ZERO_CONFIDENCE = 0.0
-MAX_HISTORY_DEPTH = 3
+MAX_HISTORY_DEPTH = 2
+smiles_pattern = re.compile(r":[)(DpP3]")
 with open(CONFIG_NAME, "r") as f:
     generation_params = json.load(f)
 generation_params["num_return_sequences"] = N_HYPOTHESES_TO_GENERATE
@@ -45,7 +47,12 @@
 
 def generate_responses(context, model, tokenizer, continue_last_uttr=False):
     encoded_context = []
-    for uttr in context[-MAX_HISTORY_DEPTH:-1]:
+
+    history_depth = MAX_HISTORY_DEPTH
+    if len(context[-1].split()) > 3:
+        history_depth = MAX_HISTORY_DEPTH - 1
+
+    for uttr in context[-history_depth:-1]:
         encoded_context += [tokenizer.encode(uttr + " " + tokenizer.eos_token, return_tensors="pt")]
     if continue_last_uttr:
         encoded_context += [tokenizer.encode(context[-1] + " ", return_tensors="pt")]
@@ -64,6 +71,24 @@ def generate_responses(context, model, tokenizer, continue_last_uttr=False):
     return outputs
 
 
+def cut_response(response):
+    # if ends with a smile, it's finished
+    if smiles_pattern.match(response[-2:]):
+        return response
+
+    leftover = re.split(r"[.!?]", response)[-1]
+    if leftover:
+        # strings with no ending punctuation will be empty
+        response = response[: -len(leftover)]
+
+    # save smiles from cutting
+    smile = ""
+    if smiles_pattern.match(leftover.strip()[:2]):
+        smile = " " + leftover.strip()[:2]
+    response += smile
+    return response.strip()
+
+
 @app.route("/respond", methods=["POST"])
 def respond():
     st_time = time.time()
@@ -77,6 +102,7 @@ def respond():
             curr_confidences = []
             outputs = generate_responses(context, model, tokenizer)
             for response in outputs:
+                response = cut_response(response)
                 if len(response) > 3:
                     # drop too short responses
                     curr_responses += [response]
@@ -110,6 +136,7 @@ def continue_last_uttr():
             curr_responses = []
             outputs = generate_responses(context, model, tokenizer, continue_last_uttr=True)
             for response in outputs:
+                response = cut_response(response)
                 if len(response) > 3:
                     # drop too short responses
                     curr_responses += [response]