feat: implement nlp

Signed-off-by: Sphericalkat <me@kat.bio>

main.py

@@ -2,19 +2,21 @@ import logging
 
 from telegram import Update
 from telegram.ext import (
-    ContextTypes,
     ApplicationBuilder,
     CommandHandler,
+    ContextTypes,
     MessageHandler,
     filters,
 )
 
+from nlp import is_noun_follows_verb
 from settings import Settings
 
+# Load environment variables
 settings = Settings()
+
+# Set the log level
 log_level = logging.getLevelNamesMapping().get(settings.log_level, logging.INFO)
-
-
 logging.basicConfig(
     format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=log_level
 )
@@ -27,10 +29,23 @@ async def start(update: Update, context: ContextTypes.DEFAULT_TYPE):
 
 
 async def message_handler(update: Update, context: ContextTypes.DEFAULT_TYPE):
-    await context.bot.send_message(
-        chat_id=update.effective_chat.id,
-        text="I'm sorry, I'm a bot and I can't understand that.",
-    )
+    # Get the message text content
+    msg_content = update.effective_message.text
+
+    # Ignore messages without text
+    if not msg_content:
+        return
+    
+    # Check that the message doesn't have more than 5 words
+    if len(msg_content.split()) > 5:
+        return
+
+    # Check if a noun immediately follows a verb
+    is_follows_verb, verb = is_noun_follows_verb(msg_content)
+    if is_follows_verb:
+        await update.effective_message.reply_text(f"{verb} deez")
+
+    return
 
 
 if __name__ == "__main__":

nlp.py

@@ -0,0 +1,23 @@
+import spacy
+
+# Load the English NLP model
+nlp = spacy.load("en_core_web_sm")
+
+# Define a function to check if a noun immediately follows a verb
+def is_noun_follows_verb(text: str) -> bool:
+    doc = nlp(text)
+    for i in range(len(doc) - 1):
+        # Check if the current token is a verb and the next token is a noun
+        if doc[i].pos_ == "VERB" and doc[i + 1].pos_ in ["NOUN", "PROPN", "PRON"]:
+            return True, doc[i].lemma_
+    
+    return False, None
+
+
+if __name__ == "__main__":
+    text = "I was eating pizza"
+    is_follows_verb, verb = is_noun_follows_verb(text)
+    if is_follows_verb:
+        print(f"{verb} deez")
+    else:
+        print("No noun follows a verb in the text")

requirements.txt

@@ -0,0 +1,45 @@
+annotated-types==0.7.0
+anyio==4.3.0
+blis==0.7.11
+catalogue==2.0.10
+certifi==2024.2.2
+charset-normalizer==3.3.2
+click==8.1.7
+cloudpathlib==0.16.0
+confection==0.1.4
+cymem==2.0.8
+# en-core-web-sm==3.7.1 (from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl)
+h11==0.14.0
+httpcore==1.0.5
+httpx==0.27.0
+idna==3.7
+jinja2==3.1.4
+langcodes==3.4.0
+language-data==1.2.0
+marisa-trie==1.1.1
+markupsafe==2.1.5
+murmurhash==1.0.10
+numpy==1.26.4
+packaging==24.0
+pip==24.0
+preshed==3.0.9
+pydantic==2.7.1
+pydantic-core==2.18.2
+python-telegram-bot==21.2
+requests==2.32.2
+setuptools==70.0.0
+smart-open==6.4.0
+sniffio==1.3.1
+spacy==3.7.4
+https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl
+spacy-legacy==3.0.12
+spacy-loggers==1.0.5
+srsly==2.4.8
+thinc==8.2.3
+tqdm==4.66.4
+typer==0.9.4
+typing-extensions==4.12.0
+urllib3==2.2.1
+wasabi==1.1.2
+weasel==0.3.4
+wheel==0.43.0