Update app.py

app.py

@@ -1,41 +1,29 @@
+import streamlit as st
 import pyttsx3
 import speech_recognition as sr
 from playsound import playsound
 import random
 import datetime
-hour = datetime.datetime.now().strftime('%H:%M')
-#print(hour)
-date = datetime.date.today().strftime('%d/%B/%Y')
-#print(date)
-date = date.split('/')
-#print(date)
 import webbrowser as wb
 import tensorflow as tf
 import numpy as np
 import librosa
 import matplotlib.pyplot as plt
 import seaborn as sns
-sns.set()
 from modules import commands_answers, load_agenda
+
+# Initial settings
+sns.set()
 commands = commands_answers.commands
 answers = commands_answers.answers
-#print(commands)
-#print(answers)
-
 my_name = 'Bob'
 
-# MacOS
-chrome_path = 'open -a /Applications/Google\ Chrome.app %s'
-# Windows
-#chrome_path = 'C:/Program Files/Google/Chrome/Application/chrome.exe %s'
-# Linux
-# chrome_path = '/usr/bin/google-chrome %s'
-
-def search(sentence):
-    wb.get(chrome_path).open('https://www.google.com/search?q=' + sentence)
-
-#search('python programming language')
+# Paths for browser
+chrome_path = 'open -a /Applications/Google\ Chrome.app %s'  # MacOS
+# chrome_path = 'C:/Program Files/Google/Chrome/Application/chrome.exe %s'  # Windows
+# chrome_path = '/usr/bin/google-chrome %s'  # Linux
 
+# Load model
 MODEL_TYPES = ['EMOTION']
 def load_model_by_name(model_type):
     if model_type == MODEL_TYPES[0]:
@@ -44,65 +32,36 @@ def load_model_by_name(model_type):
         SAMPLE_RATE = 48000
     return model, model_dict, SAMPLE_RATE
 
-#print(load_model_by_name('EMOTION'))
-#print(load_model_by_name('EMOTION')[0].summary())
+loaded_model = load_model_by_name('EMOTION')
 
-model_type = 'EMOTION'
-loaded_model = load_model_by_name(model_type)
+# Functions
+def search(sentence):
+    wb.get(chrome_path).open('https://www.google.com/search?q=' + sentence)
 
-def predict_sound(AUDIO, SAMPLE_RATE, plot = True):
+def predict_sound(AUDIO, SAMPLE_RATE, plot=True):
     results = []
-    wav_data, sample_rate = librosa.load(AUDIO, sr = SAMPLE_RATE)
-    #print(wav_data.shape)
-    #print(sample_rate)
-    #print(wav_data)
-    # ' librosa ' -> 'librosa'
-    # https://librosa.org/doc/main/generated/librosa.effects.trim.html
+    wav_data, sample_rate = librosa.load(AUDIO, sr=SAMPLE_RATE)
     clip, index = librosa.effects.trim(wav_data, top_db=60, frame_length=512, hop_length=64)
-    splitted_audio_data = tf.signal.frame(clip, sample_rate, sample_rate, pad_end = True, pad_value = 0)
+    splitted_audio_data = tf.signal.frame(clip, sample_rate, sample_rate, pad_end=True, pad_value=0)
     for i, data in enumerate(splitted_audio_data.numpy()):
-        #print('Audio split: ', i)
-        #print(data.shape)
-        #print(data)
-        # Mel frequency: https://en.wikipedia.org/wiki/Mel-frequency_cepstrum
-        # PCA
-        mfccs_features = librosa.feature.mfcc(y = data, sr = sample_rate, n_mfcc=40)
-        #print(mfccs_features.shape)
-        #print(mfccs_features)
-        mfccs_scaled_features = np.mean(mfccs_features.T, axis = 0)
-        mfccs_scaled_features = mfccs_scaled_features.reshape(1, -1)
-        #print(mfccs_scaled_features.shape)
-        mfccs_scaled_features = mfccs_scaled_features[:, :, np.newaxis]
-        # batch
-        #print(mfccs_scaled_features.shape)
+        mfccs_features = librosa.feature.mfcc(y=data, sr=sample_rate, n_mfcc=40)
+        mfccs_scaled_features = np.mean(mfccs_features.T, axis=0)
+        mfccs_scaled_features = mfccs_scaled_features.reshape(1, -1)[:, :, np.newaxis]
         predictions = loaded_model[0].predict(mfccs_scaled_features)
-        #print(predictions)
-        #print(predictions.sum())
         if plot:
             plt.figure(figsize=(len(splitted_audio_data), 5))
             plt.barh(loaded_model[1], predictions[0])
             plt.tight_layout()
-            plt.show()
+            st.pyplot(plt)
 
-        predictions = predictions.argmax(axis = 1)
-        #print(predictions)
+        predictions = predictions.argmax(axis=1)
         predictions = predictions.astype(int).flatten()
         predictions = loaded_model[1][predictions[0]]
         results.append(predictions)
-        #print(results)
-
-        result_str = 'PART ' + str(i) + ': ' + str(predictions).upper()
-        #print(result_str)
 
     count_results = [[results.count(x), x] for x in set(results)]
-    #print(count_results)
-
-    #print(max(count_results))
     return max(count_results)
 
-#playsound('sad.wav')
-#predict_sound('sad.wav', loaded_model[2], plot=False)
-
 def play_music_youtube(emotion):
     play = False
     if emotion == 'sad' or emotion == 'fear':
@@ -113,82 +72,62 @@ def play_music_youtube(emotion):
         play = True
     return play
 
-#play_music_youtube('sad')
-#play_music_youtube('surprise')
-#emotion = predict_sound('sad.wav', loaded_model[2], plot=False)
-#print(emotion)
-#play_music_youtube(emotion[1])
-
 def speak(text):
     engine = pyttsx3.init()
-    engine.setProperty('rate', 90) # number of words per second
-    engine.setProperty('volume', 1) # min: 0, max: 1
+    engine.setProperty('rate', 90)  # number of words per second
+    engine.setProperty('volume', 1)  # min: 0, max: 1
     engine.say(text)
     engine.runAndWait()
 
-#speak("Testing the Assistant's Speech Synthesizer")
-
 def listen_microphone():
     microphone = sr.Recognizer()
     with sr.Microphone() as source:
         microphone.adjust_for_ambient_noise(source, duration=0.8)
-        print('Listening: ')
+        st.write('Listening...')
         audio = microphone.listen(source)
         with open('recordings/speech.wav', 'wb') as f:
             f.write(audio.get_wav_data())
     try:
-        # https://pypi.org/project/SpeechRecognition/
         sentence = microphone.recognize_google(audio, language='en-US')
-        print('You said: ' + sentence)
+        st.write('You said: ' + sentence)
     except sr.UnknownValueError:
         sentence = ''
-        print('Not understood')
+        st.write('Not understood')
     return sentence
 
-#playsound('recordings/speech.wav')
-#listen_microphone()
-
 def test_models():
-    audio_source = '/Users/jonesgranatyr/Documents/Ensino/IA Expert/Cursos/Virtual assistent/virtual_assistant/recordings/speech.wav'
-    prediction = predict_sound(audio_source, loaded_model[2], plot = False)
+    audio_source = 'recordings/speech.wav'
+    prediction = predict_sound(audio_source, loaded_model[2], plot=False)
     return prediction
 
-#print(test_models())
-
-playing = False
-mode_control = False
-print('[INFO] Ready to start!')
-playsound('n1.mp3')
+# Streamlit UI
+st.title("Virtual Assistant")
+st.write("This assistant can perform tasks based on your voice commands.")
 
-while (1):
+if st.button("Activate Assistant"):
     result = listen_microphone()
 
-    if my_name in result:
+    if my_name.lower() in result.lower():
         result = str(result.split(my_name + ' ')[1])
         result = result.lower()
-        #print('The assistant has been activacted!')
-        #print('After processing: ', result)
 
         if result in commands[0]:
-            playsound('n2.mp3')
             speak('I will read my list of functionalities: ' + answers[0])
 
-        if result in commands[3]:
-            playsound('n2.mp3')
+        elif result in commands[3]:
             speak('It is now ' + datetime.datetime.now().strftime('%H:%M'))
 
-        if result in commands[4]:
-            playsound('n2.mp3')
+        elif result in commands[4]:
+            date = datetime.date.today().strftime('%d/%B/%Y').split('/')
             speak('Today is ' + date[0] + ' of ' + date[1])
 
-        if result in commands[1]:
-            playsound('n2.mp3')
+        elif result in commands[1]:
             speak('Please, tell me the activity!')
             result = listen_microphone()
             annotation = open('annotation.txt', mode='a+', encoding='utf-8')
             annotation.write(result + '\n')
             annotation.close()
-            speak(''.join(random.sample(answers[1], k = 1)))
+            speak(''.join(random.sample(answers[1], k=1)))
             speak('Want me to read the notes?')
             result = listen_microphone()
             if result == 'yes' or result == 'sure':
@@ -199,54 +138,25 @@ while (1):
             else:
                 speak('Ok!')
 
-        if result in commands[2]:
-            playsound('n2.mp3')
-            speak(''.join(random.sample(answers[2], k = 1)))
+        elif result in commands[2]:
+            speak(''.join(random.sample(answers[2], k=1)))
             result = listen_microphone()
             search(result)
 
-        if result in commands[6]:
-            playsound('n2.mp3')
+        elif result in commands[6]:
             if load_agenda.load_agenda():
                 speak('These are the events for today:')
                 for i in range(len(load_agenda.load_agenda()[1])):
                     speak(load_agenda.load_agenda()[1][i] + ' ' + load_agenda.load_agenda()[0][i] + ' schedule for ' + str(load_agenda.load_agenda()[2][i]))
             else:
-                speak('There are not events for today considering the current time!')
+                speak('There are no events for today considering the current time!')
 
-        if result in commands[5]:
-            mode_control = True
-            playsound('n1.mp3')
-            speak('Emotion analysis mode has been activacted!')
-
-        if mode_control:
+        elif result in commands[5]:
+            st.write('Emotion analysis mode activated!')
             analyse = test_models()
-            print(f'I heard {analyse} in your voice!')
-            if not playing:
-                playing = play_music_youtube(analyse[1])
-
-        if result == 'turn off':
-            playsound('n2.mp3')
-            speak(''.join(random.sample(answers[4], k = 1)))
-            break
-    else:
-        playsound('n3.mp3')
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+            st.write(f'I heard {analyse} in your voice!')
+            play_music_youtube(analyse[1])
 
+        elif result == 'turn off':
+            speak(''.join(random.sample(answers[4], k=1)))
+            st.write("Assistant turned off.")