Merge pull request #20 from software-students-fall2023/Andrew

microphone input

.github/workflows/lint.yml

@@ -10,7 +10,10 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: "3.10"
-      - name: Install dependencies
+      - name: Install system dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y portaudio19-dev
         if: ${{ hashFiles('**/*.py') != '' }}
         run: |
           python -m pip install --upgrade pip

machine-learning-client/machine_learning_client.py

@@ -5,6 +5,66 @@
 from enum import Enum
 import pyaudio
 import speech_recognition as sr
+<<<<<<< HEAD
+
+class ML:
+    """Machine Learning class functions """
+    pa = pyaudio.PyAudio()
+
+    def list_all_mic(self):
+        """
+        List all available microphone device
+        """
+        if len(sr.Microphone.list_microphone_names()) == 0:
+            print("no device available")
+            return
+
+        for index, name in enumerate(sr.Microphone.list_microphone_names()):
+            print(f'Microphone with name "{name}"')
+            print(f" found for Microphone(device_index={index})")
+
+    def record_microphone(self):
+        """Function for recording microphone input"""
+        ML.list_all_mic(self)  # Use for debug
+        mc = None
+        try:
+            mc = sr.Microphone()
+            print("Mic init successful")
+        except OSError:
+            mc = sr.Microphone(0)
+            print("no default mic")
+        with mc as source:
+            print("Please give your answer:")
+            r = sr.Recognizer()
+            audio = r.listen(source)
+        return audio
+
+    def audio_to_text(self, audio_file_string):
+        """Function for converting audio file to text transcription"""
+        r = sr.Recognizer()
+        with sr.AudioFile(audio_file_string) as source:
+            ad = r.listen(source)
+        try:
+            transcription = r.recognize_google(ad)
+            print(transcription)
+            print(type(transcription))
+        except sr.UnknownValueError:
+            print("Sorry, we could not recognize your response.")
+        except sr.RequestError:
+            print("Sorry, there appears to be an error with Google Speech to Text")
+        return transcription
+
+    class BuzzWord(Enum):
+        """List of Buzzwords"""
+        HELLO = "hello"
+        UGH = "ugh"
+
+    def grade_response(self, transcription):
+        """
+        Give out a score based on the transcribed audio
+
+
+=======
 
 
 class ML:
@@ -64,6 +124,7 @@ def grade_response(self, transcription):
         Give out a score based on the transcribed audio
 
 
+>>>>>>> 5a0af371e4dbfe2fdc0328cf9e5f1e13a84def20
         Args:
             transcription (str): transcribed audio
         """
@@ -79,10 +140,19 @@ def main():
     """Main Method"""
     print("Tell me a little bit about yourself")
     ml = ML()
+<<<<<<< HEAD
+    audio = r'..\\web-app\uploads\user_audio.wav'
+    transcription = ml.audio_to_text(audio)
+    result = ml.grade_response(transcription)
+    print(result)
+    print("test main")
+
+=======
     audio = ml.record_microphone()
     transcription = ml.audio_to_text(audio)
     result = ml.grade_response(transcription)
     print(result)
+>>>>>>> 5a0af371e4dbfe2fdc0328cf9e5f1e13a84def20
 
 
 if __name__ == "__main__":

web-app/app.py

@@ -1,35 +1,38 @@
-"""Module designed to supplement front end webpage"""
+from flask import Flask, render_template, request, jsonify
 import os
-import datetime
-import sys
-from flask import (
-    Flask,
-    render_template,
-    request,
-    redirect,
-    url_for,
-    make_response,
-    session,
-)
-from pymongo import MongoClient
-import pymongo
-from bson.objectid import ObjectId
+import subprocess
 
-app = Flask("project4")
+app = Flask('project4')
 
+app.config['UPLOAD_FOLDER'] = 'uploads'
+os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
 
-@app.route("/")
-def root_page():
-    """Root page route"""
-    return render_template("root.html")
+@app.route('/')
+def RootPage():
+    return render_template('root.html')
 
+@app.route('/analyzeData', methods=['POST'])
+def analyzeData():
+    try:
+        if 'audio' not in request.files:
+            return jsonify({"status": "error", "message": "No audio file provided"})
 
-@app.route("/analyze_data", methods=["POST"])
-def anaylze_data():
-    """Analyze data by sending it to the ml client"""
-    return
+        audio_file = request.files['audio']
+        audio_path = os.path.join(app.config['UPLOAD_FOLDER'], 'user_audio.wav')
+        audio_file.save(audio_path)
+
+
+        print("Audio file saved at:", audio_path)  
+        result = subprocess.run(["C:\\Users\\Andrew - User\\AppData\\Local\\Microsoft\\WindowsApps\\python.exe", "E:\\4-containerized-app-exercise-rizzballs\\machine-learning-client\\machine_learning_client.py", audio_path], capture_output=True, text=True)
+
+
+        return jsonify({"status": "success"})
+
+    except Exception as e:
+        return jsonify({"status": "error", "message": str(e)})
 
 
 if __name__ == "__main__":
-    PORT = os.getenv("PORT", 5000)
+    PORT = os.getenv('PORT', 5000) 
     app.run(debug=True, port=PORT)
+

web-app/templates/root.html

@@ -7,64 +7,64 @@
     <link rel="stylesheet" href="/static/style.css">
 </head>
 <body>
-    {% extends 'base.html' %} {% block Base %}
+    {% extends 'base.html' %} 
+    {% block Base %}
         <div class="buttonContainer">
             <h2 style="text-align:center">What is our machine Learning app?</h2>
             <button type="submit" onclick="beginRecording()"> Start Recording Audio </button>
             <button type="submit" onclick="stopRecording()"> Stop Recording Audio </button>
         </div>
         <div class="buttonContainer">
-            <p class="displayReuslts"> reuslts of analysis will be place here</p>
+            <p class="displayReuslts">Results of analysis will be placed here</p>
         </div>
 
         <script>
             let mediaRecorder;
             let audioChunks = [];
-            let wordCount = 0;
 
             async function beginRecording() {
-                const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
-                const audioContext = new AudioContext();
-                const audioInput = audioContext.createMediaStreamSource(stream);
-                mediaRecorder = new MediaREcorder(audioContext);
+                try {
+                    const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
 
-                mediaRecorder.ondataavailable = (event) =>{
-                    audioChunks.push(event.data);
-                };
+                    if (stream instanceof MediaStream) {
+                        mediaRecorder = new MediaRecorder(stream);
 
-                mediaRecorder.onstop = async () =>{
-                    const audioBlob = new Blob(audioChunks, { type: 'audio/wav' });
+                        mediaRecorder.ondataavailable = (event) => {
+                            audioChunks.push(event.data);
+                        };
 
-                    const formData = new FormData();
-                    formData.append('audio', audioBlob);
+                        mediaRecorder.onstop = async () => {
+                            const audioBlob = new Blob(audioChunks, { type: 'audio/wav' });
 
-                    const response = await fetch('/anaylzeData', {
-                        method: 'POST',
-                        body: formData
-                    });
+                            const formData = new FormData();
+                            formData.append('audio', audioBlob);
 
-                    /**
-                     * const result = await response.json();
-                     * const transcription = result.transcription;
-                     * 
-                     *
-                     * 
-                     */
+                            try {
+                                const response = await fetch('/analyzeData', {
+                                    method: 'POST',
+                                    body: formData
+                                });
 
-                }
+                                const result = await response.json();
+                                console.log(result);
+                            } catch (error) {
+                                console.error('Error sending data to server:', error);
+                            }
+                        }
 
-                //audioInput.connect(mediaRecorder);
-                //mediaRecorder.connect(audioContext.destination);
-                //mediaRecorder.start();
+                        mediaRecorder.start();
+                    } else {
+                        console.error('Failed to obtain a valid MediaStream object.');
+                    }
+                } catch (error) {
+                    console.error('Error getting user media:', error);
+                }
             }
 
-            function stopRecording(){
-                mediaRecorder.stop()
+            function stopRecording() {
+                mediaRecorder.stop();
             }
         </script>
-
     {% endblock %}
-
-
 </body>
-</html>
+</html>