Merge pull request #14 from karthagokul/beta

code cleanup

README.md

@@ -1,7 +1,23 @@
 
 # sentimeter
 
-Reading the Emotions From Text / Youtube video or live speech
+Sentimeter is an app written in python to understand the emotions in audio / text. Currently it has the below modules
+* Audio Module
+ Process the Emotions in a given audio file. Currently it supports only wav file . In future we will add more formats
+
+* Live Speech
+Process the Emotions in a live speech
+
+* Telegram Mode
+Sentimeter has a Telegram Bot, it can process the emotions in a Telegram chat
+
+* Text File
+Process a text file and identifies the emotions
+
+* Youtube Video
+Under Development
+
+
 
 
 ## Usage

__init__.py

@@ -0,0 +1,6 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Application Main
+"""

core/__init__.py

@@ -1,3 +1,9 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Application Main
+"""
 from core.emotions_engine import EmotionsEngine
 from core.sttengine import STTEngine
-from core.user_interfaces import SentimeterSimpleUI
+from core.user_interfaces import SentimeterSimpleUI

core/emotions_engine.py

@@ -1,5 +1,8 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
 """
-Class Description
+The Core Engine
 """
 
 import logging
@@ -9,14 +12,14 @@
 
 class SentiMeterModule:
     def __init__(self) -> None:
-        self.active=False
+        self.active = False
 
     def start(self):
-        self.active=True
+        self.active = True
         return self.active
 
     def stop(self):
-        self.active=False
+        self.active = False
         return self.active
 
     def is_active(self):
@@ -25,45 +28,45 @@ def is_active(self):
 
 class EngineObserver:
     def __init__(self, name) -> None:
-        '''
+        """
         A Name is given for observer
-        '''
+        """
         self.name = name
 
     def on_event(self, result, emotions):
-        '''
+        """
         Observer Abstract Method
-        '''
+        """
         pass
 
 
-class EmotionsEngine():
-    '''
+class EmotionsEngine:
+    """
     The class responsible to identify the emotions of a given text
-    '''
+    """
 
     def __init__(self) -> None:
-        '''
+        """
         constructor , the object acts as singleton
-        '''
+        """
         self._listeners = {}
 
     def add_observer(self, observer):
-        '''
+        """
         The Engine events are propogated via an observer mechanism, Objects can register via this method
-        '''
+        """
         self._listeners[observer.name] = observer
 
     def remove_observer(self, observer):
-        '''
+        """
         Remove an observer ,Todo : Check if observer is not registered to avoid exceptions
-        '''
+        """
         self._listeners.pop(observer.name)
 
     def process_text(self, result):
-        '''
+        """
         The Functions to start the processing
-        '''
+        """
         emotions = te.get_emotion(result)
         # Lets inform all the listerners
         for obj in self._listeners:

core/sttengine.py

@@ -1,31 +1,36 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
 
+"""
+Speech to Text Module, Currently uses Google STT, Future we will add more offline Engines
+"""
 import logging
 import speech_recognition as sr
 
+
 class STTEngine:
-    '''
+    """
     Speech to Text Engine, Currently using Google Service, Options to be added for Sphinix
-    '''
+    """
 
     def __init__(self, recognizer) -> None:
-        '''
+        """
         constructor
-        '''
+        """
         self.recognizer = recognizer
         self.lang = "en-IN"
         pass
 
     def speech_to_text(self, audiodata):
-        '''
+        """
         function to get the text for inputed audio data
-        '''
+        """
         try:
             actual_result = self.recognizer.recognize_google(
                 audiodata, language=self.lang, show_all=True
             )
         except sr.UnknownValueError:
-            logging.fatal(
-                "Google Speech Recognition could not understand audio")
+            logging.fatal("Google Speech Recognition could not understand audio")
             exit(-1)
         except sr.RequestError as e:
             logging.fatal("Error service; {0}".format(e))

core/user_interfaces.py

@@ -1,6 +1,13 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+File which can handle various user interfaces , Currently only basic commandlines
+"""
 import core.emotions_engine as emotions_engine
 from core.emotions_engine import EngineObserver
 
+
 class SentimeterSimpleUI(EngineObserver):
     def __init__(self, name) -> None:
         super().__init__(name)

modules/__init__.py

@@ -1,3 +1,10 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Application Main
+"""
+
 from modules.livespeech_module import LiveSpeechModule
 from modules.telegram_module import TelegramModule
-from modules.audio_module import AudioModule
+from modules.audio_module import AudioModule

modules/audio_module.py

@@ -1,3 +1,9 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Module for processing offline Audiofile
+"""
 import core.emotions_engine as emotions_engine
 from core.emotions_engine import SentiMeterModule
 from pydub import AudioSegment
@@ -12,21 +18,22 @@
 import speech_recognition as sr
 from core.user_interfaces import SentimeterSimpleUI
 
-class AudioModule (SentiMeterModule):
-    '''
+
+class AudioModule(SentiMeterModule):
+    """
     Classs Abstracts Audio Related features
-    '''
+    """
 
-    def __init__(self,path) -> None:
-        '''
+    def __init__(self, path) -> None:
+        """
         Constructor
         path : location to a .wav file , in future we can add support for mp3 etc
-        '''
+        """
         super().__init__()
         self.recognizer = sr.Recognizer()
         self.backend = STTEngine(self.recognizer)
-        self.path=path
-        self.ui=SentimeterSimpleUI("Audio UI")
+        self.path = path
+        self.ui = SentimeterSimpleUI("Audio UI")
 
     def start(self):
         super().start()
@@ -35,14 +42,15 @@ def start(self):
         # open the audio file using pydub
         sound = AudioSegment.from_wav(self.path)
         # split audio sound where silence is 1000 miliseconds or more and get chunks
-        chunks = split_on_silence(sound,
-                                  # experiment with this value for your target audio file
-                                  min_silence_len=1000,
-                                  # adjust this per requirement
-                                  silence_thresh=sound.dBFS-14,
-                                  # keep the silence for 1 second, adjustable as well
-                                  keep_silence=1000,
-                                  )
+        chunks = split_on_silence(
+            sound,
+            # experiment with this value for your target audio file
+            min_silence_len=1000,
+            # adjust this per requirement
+            silence_thresh=sound.dBFS - 14,
+            # keep the silence for 1 second, adjustable as well
+            keep_silence=1000,
+        )
         folder_name = "audio-chunks"
         # create a directory to store the audio chunks
         if not os.path.isdir(folder_name):
@@ -61,9 +69,9 @@ def start(self):
         return True
 
     def stop(self):
-        '''
+        """
         Routines to stop the Engine
-        '''
+        """
         super().stop()
         logging.debug("Stopping IntelliAudio")
         if not self.sttrunner.is_alive:

modules/livespeech_module.py

@@ -1,3 +1,6 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
 """
 IntelliAudio Class who proces live speech Audio Data file , Todo : May be split the class?
 """
@@ -19,56 +22,55 @@
 
 
 class STTLiveTranscoder(Thread):
-    '''
+    """
     Threaded implementation to handle live audio with a processing FIFO
-    '''
+    """
 
     def __init__(self, backend):
-        '''
+        """
         Constructor
-        '''
+        """
         Thread.__init__(self)
         self.backend = backend
         self.queue = Queue()
-        self.ui=SentimeterSimpleUI("Live UI")
+        self.ui = SentimeterSimpleUI("Live UI")
 
     def put_data(self, audio_data):
-        '''
+        """
         Adds the audio data to processing queue
-        '''
+        """
         self.queue.put(audio_data)
 
     def run(self):
-        '''
+        """
         Runner
-        '''
+        """
         while True:
             audiodata = self.queue.get()
             result = self.backend.speech_to_text(audiodata)
             emotions_engine.engine.process_text(result)
         return True
 
 
-class LiveSpeechModule (SentiMeterModule):
-    '''
+class LiveSpeechModule(SentiMeterModule):
+    """
     Classs Abstracts Audio Related features
-    '''
+    """
 
     def __init__(self) -> None:
-        '''
+        """
         Constructor
-        '''
+        """
         super().__init__()
         self.recognizer = sr.Recognizer()
         self.entries = []
         self.backend = STTEngine(self.recognizer)
         self.sttrunner = STTLiveTranscoder(self.backend)
-
 
     def start(self):
-        '''
+        """
         Process Microphone
-        '''
+        """
         super().start()
         self.sttrunner.start()
         while True:
@@ -77,11 +79,10 @@ def start(self):
                 audio_data = self.recognizer.record(source, duration=5)
                 self.sttrunner.put_data(audio_data)
 
-
     def stop(self):
-        '''
+        """
         Routines to stop the Engine
-        '''
+        """
         super().stop()
         logging.debug("Stopping IntelliAudio")
         if not self.sttrunner.is_alive: