feat : cleaner import arrangement

prompts/base/planner_agent.txt

@@ -1,4 +1,4 @@
-You are a planner agent.
+You are a project manager.
 Your goal is to divide and conquer the task using the following agents:
 - Coder: An expert coder agent.
 - File: An expert agent for finding files.

sources/interaction.py

@@ -1,3 +1,4 @@
+from typing import List, Tuple, Type, Dict, Tuple
 
 from sources.text_to_speech import Speech
 from sources.utility import pretty_print, animate_thinking
@@ -11,7 +12,8 @@ class Interaction:
     def __init__(self, agents,
                  tts_enabled: bool = True,
                  stt_enabled: bool = True,
-                 recover_last_session: bool = False):
+                 recover_last_session: bool = False,
+                ):
         self.is_active = True
         self.current_agent = None
         self.last_query = None

sources/language.py

@@ -1,3 +1,4 @@
+from typing import List, Tuple, Type, Dict, Tuple
 import langid
 import re
 import nltk

sources/llm_provider.py

@@ -1,16 +1,17 @@
 
+import os
 import time
 import ollama
 from ollama import chat
 import requests
 import subprocess
 import ipaddress
+import httpx
 import platform
 from dotenv import load_dotenv, set_key
 from openai import OpenAI
 from huggingface_hub import InferenceClient
-import os
+from typing import List, Tuple, Type, Dict, Tuple
-import httpx
 
 from sources.utility import pretty_print, animate_thinking
 

sources/memory.py

@@ -1,11 +1,12 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import time
 import datetime
 import uuid
 import os
 import sys
 import json
+from typing import List, Tuple, Type, Dict, Tuple
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 

sources/router.py

@@ -1,6 +1,8 @@
 import os
 import sys
 import torch
+from typing import List, Tuple, Type, Dict, Tuple
+
 from transformers import pipeline
 from adaptive_classifier import AdaptiveClassifier
 

sources/speech_to_text.py

@@ -1,4 +1,5 @@
 from colorama import Fore
+from typing import List, Tuple, Type, Dict, Tuple
 import queue
 import threading
 import numpy as np

sources/text_to_speech.py

@@ -1,30 +1,33 @@
+import re
+import platform
+import subprocess
+from sys import modules
+from typing import List, Tuple, Type, Dict, Tuple
+
 from kokoro import KPipeline
 from IPython.display import display, Audio
 import soundfile as sf
-import subprocess
-import re
-import platform
-from sys import modules
 
 class Speech():
     """
     Speech is a class for generating speech from text.
     """
-    def __init__(self, enable: bool = True, language: str = "english") -> None:
+    def __init__(self, enable: bool = True, language: str = "en", voice_idx: int = 0) -> None:
         self.lang_map = {
-            "english": 'a',
+            "en": 'a',
-            "chinese": 'z',
+            "zh": 'z',
-            "french": 'f'
+            "fr": 'f'
         }
         self.voice_map = {
-            "english": ['af_alloy', 'af_bella', 'af_kore', 'af_nicole', 'af_nova', 'af_sky', 'am_echo', 'am_michael', 'am_puck'],
+            "en": ['af_kore', 'af_bella', 'af_alloy', 'af_nicole', 'af_nova', 'af_sky', 'am_echo', 'am_michael', 'am_puck'],
-            "chinese": ['zf_xiaobei', 'zf_xiaoni', 'zf_xiaoxiao', 'zf_xiaoyi', 'zm_yunjian', 'zm_yunxi', 'zm_yunxia', 'zm_yunyang'],
+            "zh": ['zf_xiaobei', 'zf_xiaoni', 'zf_xiaoxiao', 'zf_xiaoyi', 'zm_yunjian', 'zm_yunxi', 'zm_yunxia', 'zm_yunyang'],
-            "french": ['ff_siwis']
+            "fr": ['ff_siwis']
         }
         self.pipeline = None
+        self.language = language
         if enable:
             self.pipeline = KPipeline(lang_code=self.lang_map[language])
-        self.voice = self.voice_map[language][2]
+        self.voice = self.voice_map[language][voice_idx]
         self.speed = 1.2
 
     def speak(self, sentence: str, voice_number: int = 1 , audio_file: str = 'sample.wav'):
@@ -38,7 +41,7 @@ class Speech():
         if not self.pipeline:
             return
         sentence = self.clean_sentence(sentence)
-        self.voice = self.voice_map["english"][voice_number]
+        self.voice = self.voice_map[self.language][voice_number]
         generator = self.pipeline(
             sentence, voice=self.voice,
             speed=self.speed, split_pattern=r'\n+'