Kei edits two

.github/workflows/lint.yml

@@ -14,6 +14,7 @@ jobs:
         run: |
           sudo apt-get update
           sudo apt-get install -y portaudio19-dev
+      - name: Install dependencies
         if: ${{ hashFiles('**/*.py') != '' }}
         run: |
           python -m pip install --upgrade pip

Pipfile

@@ -12,6 +12,10 @@ pymongo = "*"
 speechrecognition = "*"
 pyaudio = "*"
 setuptools = "*"
+tomli = "*"
+typing-extensions = "*"
+dill = "*"
+python-dotenv = "*"
 
 [dev-packages]
 

database/database.py.py → database/database.py

@@ -1,5 +1,7 @@
+"""
+database file to connect ot database
+"""
 import os
-
 from pymongo import MongoClient
 from dotenv import load_dotenv
 
@@ -18,4 +20,4 @@
     DB = client[os.getenv("MONGODB_DATABASE")]
     print("Pinged your deployment. You successfully connected to MongoDB!")
 except Exception as e:  # pylint: disable=broad-except
-    print(e)
+    print(e)

machine-learning-client/machine_learning_client.py

@@ -5,10 +5,11 @@
 from enum import Enum
 import pyaudio
 import speech_recognition as sr
-<<<<<<< HEAD
+
 
 class ML:
-    """Machine Learning class functions """
+    """Machine Learning class functions"""
+
     pa = pyaudio.PyAudio()
 
     def list_all_mic(self):
@@ -56,66 +57,7 @@ def audio_to_text(self, audio_file_string):
 
     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:
-    """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"
 
@@ -124,7 +66,6 @@ def grade_response(self, transcription):
         Give out a score based on the transcribed audio
 
 
->>>>>>> 5a0af371e4dbfe2fdc0328cf9e5f1e13a84def20
         Args:
             transcription (str): transcribed audio
         """
@@ -140,19 +81,11 @@ def main():
     """Main Method"""
     print("Tell me a little bit about yourself")
     ml = ML()
-<<<<<<< HEAD
-    audio = r'..\\web-app\uploads\user_audio.wav'
+    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__":

machine-learning-client/tests/ml_tests.py

@@ -4,15 +4,15 @@
 from machine_learning_client import ML
 
 
-
-
 class Tests:
-    """ Tests class for ml_tests.py """
+    """Tests class for ml_tests.py"""
+
     def test_audio_text_conversion(self):
         """testing audio_to_text function in ML with valid audiofile as input"""
-        actual = ML.audio_to_text(self,
+        actual = ML.audio_to_text(
+            self,
             r"C:\Users\Administrator\Desktop\NYU_Undergrad\SoftwareEngineering"
-            r"\4-containerized-app-exercise-rizzballs\machine-learning-client\tests\richard.aiff"
+            r"\4-containerized-app-exercise-rizzballs\machine-learning-client\tests\richard.aiff",
         )
         assert isinstance(
             actual, str
@@ -30,5 +30,5 @@ def test_grade_response(self):
         assert (
             actual.lower()
             == "your response had hello appearing 5 times ugh appearing 0 times "
-        ),f"Expected the result to be 'your response had hello appearing 5 times \
+        ), f"Expected the result to be 'your response had hello appearing 5 times \
             ugh appearing 0 times '. Instead it returned {actual}"

web-app/app.py

@@ -1,38 +1,64 @@
-from flask import Flask, render_template, request, jsonify
+"""
+app.py file that helps serve as the front end of our application
+"""
+
 import os
 import subprocess
+from flask import Flask, render_template, request, jsonify
+
+
+app = Flask("project4")
 
-app = Flask('project4')
+app.config["UPLOAD_FOLDER"] = "uploads"
+os.makedirs(app.config["UPLOAD_FOLDER"], exist_ok=True)
 
-app.config['UPLOAD_FOLDER'] = 'uploads'
-os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
 
-@app.route('/')
-def RootPage():
-    return render_template('root.html')
+@app.route("/")
+def root_page():
+    """
+    template to render root page
+    """
+    return render_template("root.html")
 
-@app.route('/analyzeData', methods=['POST'])
-def analyzeData():
+
+@app.route("/analyzeData", methods=["POST"])
+def analyze_data():
+    """
+    Function to send genreated audio file to the machine learning client
+    """
     try:
-        if 'audio' not in request.files:
+        if "audio" not in request.files:
             return jsonify({"status": "error", "message": "No audio file provided"})
 
-        audio_file = request.files['audio']
-        audio_path = os.path.join(app.config['UPLOAD_FOLDER'], 'user_audio.wav')
+        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)
-
+        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",  # pylint: disable=line-too-long
+                audio_path,  # pylint: disable=line-too-long disable=trailing-whitespace
+            ],
+            capture_output=True,
+            text=True,
+            check=True,
+        )
+        if result.returncode == 0:
+            print("Process succeeded!")
+        else:
+            print("Process failed. Exit code:", result.returncode)
 
         return jsonify({"status": "success"})
 
-    except Exception as e:
-        return jsonify({"status": "error", "message": str(e)})
+    except FileNotFoundError as e:
+        return jsonify({"status": "error", "message": f"File not found: {str(e)}"})
+
+    except subprocess.CalledProcessError as e:
+        return jsonify({"status": "error", "message": f"Subprocess failed: {str(e)}"})
 
 
 if __name__ == "__main__":
-    PORT = os.getenv('PORT', 5000) 
+    PORT = os.getenv("PORT", "5000")
     app.run(debug=True, port=PORT)
-