Update game build from GitLab CI

server/ActionProcessor.py

@@ -1,6 +1,6 @@
 from threading import Thread
 from multiprocessing import Queue
-from typing import Dict, Any
+from typing import Dict, Any, List
 import json
 import re
 import logging
@@ -27,56 +27,64 @@ class ActionProcessor(Thread):
         super().__init__()
         self.text_queue = text_queue
         self.action_queue = action_queue
+        self.text_buffers: List[str] = []
         self.mistral_client = Mistral(api_key=mistral_api_key)
         self.daemon = True  # Thread will exit when main program exits
 
-    def get_sentiment(self, input_text: str) -> str:
+    def get_sentiment_and_action(self, input_text: str) -> str:
         """Get sentiment analysis for input text."""
         messages = [
             {
                 "role": "user",
-                "content": f"""You are a sentiment classifier of positive or negative parenting.
-                    Classify the following sentence, output "negative" or "positive", do not justify:
-                    "{input_text}"
-                    """,
-            },
+                "content": f"""
+            The following transcription is a broken transmission overheard by a baby through a baby walkie-talkie. The transmission contains **fragments** of negative parenting commands, and your task is to reconstruct the most likely intended message.
+
+            Analyze the provided transcription and follow these steps:
+
+            1. **Reconstruct the most logical full command** by analyzing the given fragments and arranging them into a coherent order.
+            2. **Select the closest matching three-word action** from the following predefined options:
+                ["drop it", "don't drink", "None", "stay back", "stop touching", "move away"].
+                Always choose the most contextually relevant option. If no match is appropriate, select "None."
+            3. **Determine the parenting sentiment**, which should always be classified as "negative."
+
+            Your response should strictly follow this JSON format:
+
+            {{
+                "RECONSTRUCTED_ORDER": "[reconstructed text]",
+                "ACTION": "[chosen action]",
+                "SENTIMENT": "negative"
+            }}
+
+            Transcription fragments: "{input_text}"
+            """,
+            }
         ]
+
         response = self.mistral_client.chat.complete(
             model="mistral-large-latest",
             messages=messages,
         )
 
-        result = response.choices[0].message.content
+        result: str = response.choices[0].message.content
 
         return result.strip()
 
     def process_text(self, text: str) -> Dict[str, Any] | None:
         """Convert text into an action if a complete command is detected."""
         # Get sentiment first
-        sentiment = self.get_sentiment(text)
-
-        # Define command patterns
-        command_patterns = {
-            r"(?i)\b(stop|now)\b": "stop",
-            r"(?i)\b(come back|get back)\b": "return",
-            r"(?i)\b(easy)\b": "slow",
-            r"(?i)\b(stop drinking)\b": "pause_liquid",
-            r"(?i)\b(stop eating)\b": "pause_solid",
-            r"(?i)\b(look at me)\b": "look_at_me",
-            r"(?i)\b(look away)\b": "look_away",
-            r"(?i)\b(don't do that)\b": "stop",
-        }
-
-        # TODO: Remove this test thing
-        if len(text) <= 3:
+        self.text_buffers.append(text)
+
+        if len(self.text_buffers) < 3:
             return None
-        return {"type": text, "sentiment": sentiment}
 
-        # Check each pattern
-        for pattern, action_type in command_patterns.items():
-            match = re.search(pattern, text.lower())
-            if match:
-                return {"type": action_type, "sentiment": sentiment}
+        if len(self.text_buffers) >= 3:
+            _ = self.text_buffers.pop(0)
+
+        candidate = self.text_buffers[-2]
+
+        if len(self.text_buffers[0]) <= len(candidate) <= len(self.text_buffers[-1]):
+            sentiment_and_action = self.get_sentiment_and_action(candidate)
+            return {"type": "action", "sentiment": sentiment_and_action}
 
         return None