Pick the reply with the median length + switch from shelve to SqliteDict.

bot.py

@@ -7,7 +7,7 @@ import model
 import utils
 from client import DramaClient
 
-from config import config
+from data import config, db
 
 traceback.install()
 
@@ -24,7 +24,7 @@ class Bot:
 
         comments = [
             c
-            for c in self.client.fetch_new_comments(limit=50)
+            for c in self.client.fetch_new_comments()
             if "author_name" in c
             and not c["is_bot"]
             and c["author_name"] != config["username"]
@@ -71,13 +71,13 @@ class Bot:
 
         if not post or not thread_comments:
             print("Could not fetch context!")
+            return
 
         prompt = utils.build_prompt(post, thread_comments)
-        utils.log_prompt(prompt)
 
         candidates = []
-        num_candidates = config["num_candidates"] if random.random() < 0.6 else 1
+        rejects = []
-        while len(candidates) < num_candidates:
+        while len(candidates) < config["num_candidates"]:
             gen_text = self.model.generate(prompt)
             reply = utils.extract_reply(gen_text)
             print(f"Generated text: {gen_text}\nReply:\n{reply}")
@@ -85,14 +85,25 @@ class Bot:
 
             if len(reply) == 0:
                 print("Retrying: reply empty after processing.")
+                rejects.append(reply)
             elif utils.is_low_quality(reply, post, thread_comments):
                 print("Retrying: low quality reply.")
+                rejects
             else:
                 candidates.append(reply)
                 print("Accepting reply.")
 
-        # Get the longest reply, but cap the considered length at 500 chars.
+        reply = utils.median_by_key(candidates, key=utils.reply_length)
-        reply = max(candidates, key=lambda r: min(utils.reply_length(r), 500))
+
+        db["prompts"].append(
+            {
+                "prompt": prompt,
+                "candidates": candidates,
+                "rejects": rejects,
+                "selected": reply,
+            }
+        )
+
         self.client.reply(reply, comment)
 
 

client.py

@@ -7,16 +7,13 @@ import shelve
 
 from requests.adapters import HTTPAdapter, Retry
 
-from config import config
+from data import config, db
 
 
 class DramaClient:
     BASE_URL = "https://rdrama.net"
 
     def __init__(self):
-        self.db = shelve.open(f"{config['data_dir']}/client_state.p", writeback=True)
-        self.db.setdefault("processed_replies", set())
-
         self.session = requests.Session()
         retries = Retry(
             total=5, backoff_factor=5, status_forcelist=[500, 502, 503, 504, 521]
@@ -77,22 +74,21 @@ class DramaClient:
 
         return r.json()
 
-    def fetch_new_comments(self, limit=0):
+    def fetch_new_comments(self, limit=config["num_replies"] * 25):
         comments = []
 
-        last_processed_id = self.db.get("last_processed_id", -1)
         earliest_id = math.inf
         page = 1
 
         # Fetch comments until we find the last one processed.
-        while earliest_id > last_processed_id:
+        while earliest_id > db["last_processed_id"]:
             page_comments = self.fetch_page(page)
 
             if len(page_comments) == 0:
                 break
 
             earliest_id = min([c["id"] for c in page_comments])
-            comments += [c for c in page_comments if c["id"] > last_processed_id]
+            comments += [c for c in page_comments if c["id"] > db["last_processed_id"]]
 
             if limit > 0 and len(comments) >= limit:
                 break
@@ -102,8 +98,7 @@ class DramaClient:
         if not comments:
             return []
 
-        self.db["last_processed_id"] = max(c["id"] for c in comments)
+        db["last_processed_id"] = max(c["id"] for c in comments)
-        self.db.sync()
 
         # New comments may have pushed others to page n+1 while fetching.
         deduped_comments = {c["id"]: c for c in comments}.values()

config.py

@@ -1,8 +0,0 @@
-import yaml
-import os
-
-current_dir = os.path.dirname(os.path.realpath(__file__))
-config_path = os.path.join(current_dir, "config.yaml")
-
-with open(config_path, "r") as f:
-    config = yaml.safe_load(f)

model.py

@@ -7,7 +7,7 @@ from transformers import (
     AutoModelForCausalLM,
     LogitsProcessorList,
 )
-from config import config
+from data import config
 
 
 class StopAfterPlusIsGenerated(LogitsProcessor):

requirements.txt

@@ -2,5 +2,6 @@ fuzzywuzzy==0.18.0
 PyYAML==6.0
 Requests==2.31.0
 rich==13.4.2
+sqlitedict==2.1.0
 torch==2.0.1
 transformers==4.31.0

utils.py

@@ -4,7 +4,7 @@ import re
 from fuzzywuzzy import fuzz
 from transformers import GPTNeoXTokenizerFast
 
-from config import config
+from data import config
 from maxsubstring import longest_common_substring
 
 URL_REGEX = (
@@ -131,6 +131,7 @@ def build_prompt(post, comments):
         prompt += comment_str
 
     prompt = prompt.replace(config["username"], random.choice(config["fake_usernames"]))
+    prompt = prompt.replace("👻", "Ghost")
     prompt = prompt.strip() + "\n"
 
     # Truncate the prompt to leave room for generation.
@@ -142,11 +143,6 @@ def build_prompt(post, comments):
     return prompt
 
 
-def log_prompt(prompt):
-    with open(f"{config['data_dir']}/prompts.txt", "a") as f:
-        f.write(f"{prompt}\n==========\n")
-
-
 def reply_length(reply):
     """Return the length of the reply, without Markdown images, URLs, or quoted text."""
     # Remove Markdown images and URLs.
@@ -160,6 +156,17 @@ def reply_length(reply):
     return len(reply)
 
 
+def median_by_key(lst, key):
+    lst = sorted(lst, key=key)
+    mid_index = len(lst) // 2
+
+    # For lists of even length, pick either option as the median.
+    if len(lst) % 2 == 0:
+        return random.choice([lst[mid_index - 1], lst[mid_index]])
+    else:
+        return lst[mid_index]
+
+
 def count_tokens(text):
     return len(tokenizer(text).input_ids)