Import Flask folder with python files.

IT20201364/Flask/chatbot_web.py

+from voice_assistant import ChatBot
+from flask import Flask, render_template, redirect
+# from flask import Flask, render_template, redirect
+
+# from voice_assistant import ChatBot
+
+app = Flask(__name__)
+ai = ChatBot(name="AIC")
+overall_emotion = []
+
+
+@app.route("/")
+def index():
+    return render_template("index.html", emotion_percentages={})
+
+
+
+
+#@app.route("/process")
+#def process():
+#    ai.run()
+#    global overall_emotion
+#    overall_emotion = ai.sentiment_analysis
+#    return render_template("index.html", overall_emotion=overall_emotion)
+@app.route("/process")
+def process():
+    ai.run()
+    sentiment_analysis = ai.sentiment_analysis
+    emotion_percentages = calculate_emotion_percentages(sentiment_analysis)
+    stress_level = calculate_stress_level(sentiment_analysis)
+    return render_template("index.html", emotion_percentages=emotion_percentages, stress_level=stress_level)
+
+def calculate_emotion_percentages(sentiment_analysis):
+    total_emotions = len(sentiment_analysis)
+    emotion_count = {}
+    for emotion in sentiment_analysis:
+        emotion_count[emotion] = emotion_count.get(emotion, 0) + 1
+    emotion_percentages = {}
+    for emotion, count in emotion_count.items():
+        percentage = (count / total_emotions) * 100
+        emotion_percentages[emotion] = round(percentage, 2)
+    return emotion_percentages
+
+def calculate_stress_level(sentiment_analysis):
+    # Determine stress level based on detected emotions
+    if any(emotion in sentiment_analysis for emotion in ["Angry", "Fear", "Sad", "Disgust"]):
+        return "High Stress"
+    elif any(emotion in sentiment_analysis for emotion in ["Happy", "Suprised"]):
+        return "Low Stress"
+    else:
+        return "Netural Stress"
+
+@app.route("/conversation", methods=["POST"])
+def conversation():
+    # Existing code for the conversation logic
+
+    # Redirect back to the home page
+    return redirect("/")
+
+@app.route("/reset", methods=["POST"])
+def reset():
+    ai.sentiment_analysis = []  # Clear the emotions list
+    return redirect("/")
+
+
+
+if __name__ == "__main__":
+    app.run()
+
+
+
+
+

IT20201364/Flask/emotion_model.py

+import keras.models
+import numpy as np
+
+# Load the saved emotion identification model
+loaded_model = keras.models.load_model('C:/Users/malithg/PycharmProjects/AI_Virtual_C/my_model.h5')
+classLabels = ('Angry', 'Fear', 'Disgust', 'Happy', 'Sad', 'Surprised', 'Neutral')

IT20201364/Flask/main.py

+from chatbot_web import app
+
+if __name__ == "__main__":
+    app.run()

IT20201364/Flask/record_analysis.py

+import os
+import sounddevice as sd
+import soundfile as sf
+import numpy as np
+import librosa
+import keras.models
+import sys
+
+import os
+import sounddevice as sd
+import soundfile as sf
+import numpy as np
+import librosa
+import keras.models
+
+# Load the saved emotion identification model
+loaded_model = keras.models.load_model('C:/Users/dell/Desktop/AI_Virtual_C/my_model.h5')
+classLabels = ('Angry', 'Fear', 'Disgust', 'Happy', 'Sad', 'Surprised', 'Neutral')
+
+def record_and_analyze_user_input(chatbot):
+    duration = 2.5  # Set the duration of the recording (in seconds)
+    sample_rate = 22050 * 2  # Set the sample rate of the recording
+
+    # Redirect standard output to a null device
+    sys.stdout = open(os.devnull, 'w')
+
+    # Record the live audio
+    recording = sd.rec(int(duration * sample_rate), samplerate=sample_rate, channels=1)
+    sd.wait()  # Wait until the recording is finished
+
+    # Restore standard output
+    sys.stdout = sys.__stdout__
+
+    # Preprocess the recorded audio
+    mfccs = librosa.feature.mfcc(y=recording.flatten(), sr=sample_rate, n_mfcc=39)
+    input_data = mfccs[np.newaxis, ..., np.newaxis]
+
+    # Perform emotion prediction
+    with np.printoptions(suppress=True):
+        predictions = loaded_model.predict(input_data)
+
+    # Get the predicted label
+    predicted_label = classLabels[np.argmax(predictions)]
+
+    # Store the predicted emotion in the array
+    chatbot.sentiment_analysis.append(predicted_label)
+
+    return predicted_label
+

IT20201364/Flask/speech_to_text.py

+import speech_recognition as sr
+
+def speech_to_text():
+    recognizer = sr.Recognizer()
+    mic = sr.Microphone()
+
+    with sr.Microphone() as mic:
+        print("Listening...")
+        audio = recognizer.listen(mic)
+
+        text = "ERROR"
+    try:
+        text = recognizer.recognize_google(audio)
+        print("Me  -->", text)
+    except:
+        print("Me  --> ERROR")
+
+    return text
+
+

IT20201364/Flask/static/style.css

+body {
+    background-color: rgba(0, 0, 255, 0.5);
+    margin: 0;
+    padding: 0;
+}
+
+.container {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    justify-content: center;
+    height: 100vh;
+}
+
+.start-button {
+    background-color: rgba(255, 255, 255, 0.5);
+    border: none;
+    border-radius: 50px;
+    padding: 15px 30px;
+    color: #ffffff;
+    font-size: 18px;
+    text-align: center;
+    text-decoration: none;
+    display: inline-block;
+    transition-duration: 0.4s;
+    cursor: pointer;
+    box-shadow: 0px 8px 15px rgba(0, 0, 0, 0.1);
+    position: absolute;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    z-index: 2;
+}
+
+.table-wrapper {
+    background-color: lightgrey;
+    border-radius: 20px;
+    padding: 20px;
+    position: relative;
+    z-index: 1;
+}
+
+table {
+    width: 100%;
+}
+
+th, td {
+    padding: 10px;
+    text-align: left;
+    color: #333333;
+}
+
+th {
+    background-color: rgba(255, 255, 255, 0.3);
+}
+
+form {
+    text-align: center;
+    margin-top: 20px;
+}
+
+.btn-danger {
+    background-color: #ff0000;
+    color: #ffffff;
+    border: none;
+    border-radius: 50px;
+    padding: 10px 20px;
+    font-size: 16px;
+    transition-duration: 0.4s;
+    cursor: pointer;
+}
+
+.btn-danger:hover {
+    background-color: #cc0000;
+}
+
+

IT20201364/Flask/templates/index.html

+<!doctype html>
+<html lang="en">
+
+  <head>
+
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <title>zenBot</title>
+    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.1/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-4bw+/aepP/YC94hEpVNVgiZdgIC5+VKNBQNGCHeKRQN+PtmoHDEXuppvnDJzQIu9" crossorigin="anonymous">
+
+
+  <!------ display shortcut icon ------>
+	<link rel = " shortcut icon " href = "../static/logo.png"> 
+
+
+  </head>
+
+  <body style="background-color: black;">
+
+    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.1/dist/js/bootstrap.bundle.min.js" integrity="sha384-HwwvtgBNo3bZJJLYd8oVXjrBZt8cqVSpeBNS5n7C8IVInixGAoxmnlMuBnhbgrkm" crossorigin="anonymous"></script>
+
+
+
+    <div class = "container mt-5" style="background-color: whitesmoke; width : 40%;">
+
+        <br>
+        <center>
+        <img src="../static/chatbot.png" alt="bot">
+        <div> <h1> zenBot Emotion Level Check </h1> </div> 
+        <br>
+
+        <button type="button" class="btn btn-outline-danger btn-lg" onclick="window.location.href='/process'">Start</button> <br> <br>
+
+         <img src="../static/mic.png" alt="mic"> </center> 
+
+         <br>
+
+        <table class="table table-danger table-striped table-hover" style="text-align: center;">
+          <thead>
+            <tr>
+              <th scope="col">Emotion</th>
+              <th scope="col">Precentage</th>
+            </tr>
+          </thead>
+          <tbody>
+            {% for emotion, percentage in emotion_percentages.items() %}
+            <tr>
+              <td>{{ emotion }}</td>
+              <td>{{ percentage }}%</td>
+            </tr>
+            {% endfor %}
+          </tbody>
+        </table>
+
+        <br>
+
+        
+<!--
+
+          <h5> {{ emotion }} </h5>
+        <div class="progress" role="progressbar" aria-label="Danger  example" aria-valuenow="0" aria-valuemin="0" aria-valuemax="100">
+            <div class="progress-bar text-bg-danger" style="width: '{{ percentage }}%'">{{ percentage }}%</div>
+          </div>
+
+          <br>
+
+        -->
+
+        <center> <div> <h1> zenBot Stress Level Check </h1> </div>  
+
+
+        <br>
+
+
+
+        <h2 style="color: crimson;">{{ stress_level }}</h2>
+
+        <br>
+
+       </center>
+
+
+
+          <form method="POST" action="/reset" id="reset-form">
+            <center> <button type="submit" class="btn btn-outline-dark btn-lg">Reset</button> </center> 
+          </form>
+
+          <br>
+
+    </div>
+
+    <br>
+
+  </body>
+</html>
\ No newline at end of file

IT20201364/Flask/templates/index11.html

+ -<!DOCTYPE html>
+<html>
+<head>
+    <title>AI Counsellor</title>
+    <style>
+        body {
+            background-color: rgba(0, 0, 128, 0.7);
+            margin: 0;
+            padding: 0;
+        }
+
+        .container {
+            position: relative;
+            height: 100vh;
+            display: flex;
+            flex-direction: column;
+            align-items: center;
+            justify-content: center;
+        }
+
+        .table-wrapper {
+            background-color: rgba(0, 0, 0, 0.3);
+            border-radius: 20px;
+            padding: 20px;
+            margin-bottom: 30px;
+            box-shadow: 0px 0px 20px rgba(0, 0, 0, 0.4);
+        }
+
+        table {
+            width: 100%;
+        }
+
+        th, td {
+            padding: 10px;
+            text-align: left;
+            color: #ffffff;
+        }
+
+        th {
+            background-color: rgba(255, 255, 255, 0.3);
+        }
+
+        .start-button {
+            background-color: rgba(255, 255, 255, 0.5);
+            border: none;
+            border-radius: 50px;
+            padding: 15px 30px;
+            color: #ffffff;
+            font-size: 18px;
+            text-align: center;
+            text-decoration: none;
+            display: inline-block;
+            transition-duration: 0.4s;
+            cursor: pointer;
+            position: absolute;
+            top: 50%;
+            left: 50%;
+            transform: translate(-50%, -50%);
+            z-index: 2;
+            box-shadow: 0px 8px 15px rgba(0, 0, 0, 0.4);
+        }
+
+        .start-button:hover {
+            background-color: rgba(255, 255, 255, 0.7);
+            box-shadow: 0px 8px 15px rgba(0, 0, 0, 0.6);
+        }
+
+        form {
+            text-align: center;
+            margin-top: 20px;
+        }
+
+        .btn-danger {
+            background-color: #ff0000;
+            color: #ffffff;
+            border: none;
+            border-radius: 50px;
+            padding: 10px 20px;
+            font-size: 16px;
+            transition-duration: 0.4s;
+        }
+
+        .btn-danger:hover {
+            background-color: #cc0000;
+        }
+        h1 {
+    text-align: center;
+    color: #333333;
+        }
+    </style>
+</head>
+<body>
+    <h1>AI Counsellor</h1>
+    <button onclick="window.location.href='/process'" class="start-button">Start</button>
+    <div class="container">
+
+        <div class="table-wrapper">
+            <table id="emotion-table">
+                <thead>
+                    <tr>
+                        <th>Emotion</th>
+                        <th>Percentage</th>
+                    </tr>
+                </thead>
+                <tbody>
+                    {% for emotion, percentage in emotion_percentages.items() %}
+                        <tr>
+                            <td>{{ emotion }}</td>
+                            <td>{{ percentage }}%</td>
+                        </tr>
+                    {% endfor %}
+                </tbody>
+            </table>
+        </div>
+            <form method="POST" action="/reset" id="reset-form">
+                <button type="submit" class="btn btn-danger">Reset</button>
+            </form>
+    </div>
+
+</body>
+</html>
+
+
+
+
+

IT20201364/Flask/text_to_speech.py

+import pyttsx3
+
+def text_to_speech(text):
+    print("Dev -->", text)
+    engine = pyttsx3.init()
+    engine.say(text)
+    engine.runAndWait()
+

IT20201364/Flask/utils.py

+import datetime
+
+def wake_up(text, name):
+    return True if name in text.lower() else False
+
+def action_time():
+    return datetime.datetime.now().time().strftime('%H:%M')

IT20201364/Flask/voice_assistant.py

+import os
+import datetime
+import pyttsx3
+import sounddevice as sd
+import soundfile as sf
+import numpy as np
+import keras.models
+import librosa
+import speech_recognition as sr
+import transformers
+import threading
+import sys
+from utils import wake_up, action_time
+from record_analysis import record_and_analyze_user_input
+from text_to_speech import text_to_speech
+
+# Load the saved emotion identification model
+loaded_model = keras.models.load_model('C:/Users/dell/Desktop/AI_Virtual_C/my_model.h5')
+classLabels = ('Angry', 'Fear', 'Disgust', 'Happy', 'Sad', 'Surprised', 'Neutral')
+
+class ChatBot:
+    def __init__(self, name):
+        self.text = None
+        print("----- Starting up", name, "-----")
+        self.name = name
+        self.user_input_counter = 0
+        self.user_inputs = []
+        self.sentiment_analysis = []
+        self.record_thread = None
+
+    def speech_to_text(self):
+        recognizer = sr.Recognizer()
+        mic = sr.Microphone()
+
+        with sr.Microphone() as mic:
+            print("Listening...")
+            audio = recognizer.listen(mic)
+
+            self.text = "ERROR"
+        try:
+            self.text = recognizer.recognize_google(audio)
+            print("Me  -->", self.text)
+        except:
+            print("Me  --> ERROR")
+
+    def run(self):
+        nlp = transformers.pipeline("conversational", model="microsoft/DialoGPT-medium")
+        os.environ["TOKENIZERS_PARALLELISM"] = "true"
+
+        res1 = "Hello, I am zenBot. What can I do for you?"
+        text_to_speech(res1)
+        while True:
+            self.speech_to_text()
+
+            if wake_up(self.text, self.name):
+                res = "Hello, I am ZenBot the AI. What can I do for you?"
+            elif "time" in self.text:
+                res = action_time()
+            elif any(i in self.text for i in ["thank", "thanks"]):
+                res = np.random.choice(
+                    ["You're welcome!", "Anytime!", "No problem!", "Cool!", "I'm here if you need me!", "You're welcome!"]
+                )
+            elif any(i in self.text for i in ["exit", "close"]):
+                res = np.random.choice(
+                    ["Tata", "Have a good day", "Bye", "Goodbye", "Hope to meet soon", "Peace out!"]
+                )
+                text_to_speech(res)
+                break
+            else:
+                if self.text == "ERROR":
+                    res = "Sorry, come again?"
+                else:
+                    if self.record_thread and self.record_thread.is_alive():
+                        self.record_thread.join()  # Wait for the previous thread to finish if it's still running
+
+                    # Start a new thread to run record_and_analyze_user_input()
+                    self.record_thread = threading.Thread(target=record_and_analyze_user_input, args=(self,))
+                    self.record_thread.start()
+
+                    chat = nlp(transformers.Conversation(self.text), pad_token_id=50256)
+                    res = str(chat)
+                    res = res[res.find("bot >> ") + 7:].strip()
+
+            text_to_speech(res)
+
+        overall_emotion = self.sentiment_analysis
+        print("Overall Emotion of the conversation:", overall_emotion)
+        print("----- Closing down Dev -----")