Merge branch 'main' into Andrew

docker-compose.yaml

@@ -9,12 +9,16 @@ services:
       - backend
     command: python app.py # command to start up the front-end once the container is up and running
 
-
+  mongodb:
+    image: mongodb/mongodb-community-server:6.0-ubi8
+    environment:
+      - MONGO_INITDB_ROOT_USERNAME=user
+      - MONGO_INITDB_ROOT_PASSWORD=pass
+    volumes:
+      - type: bind
+        source: ./data
+        target: /data/db
 
   backend:
-    build: 
-    ports:
-      -
-    depends_on:
-      - db
-    command: python machine_learning_client.py
+    build: ./machine-learning-client
+

machine-learning-client/Dockerfile

@@ -18,4 +18,4 @@ COPY . .
 
 RUN pip install -r requirements.txt
 
-CMD ["python"]
+CMD ["python", "machine_learning_client.py"]

machine-learning-client/machine_learning_client.py

@@ -1,66 +1,89 @@
-import os
-from pymongo import MongoClient
-import pymongo
-import datetime
+"""
+Machine Learning python file providing conversion of audio 
+to text transcription and analysis of audio transcription
+"""
+from enum import Enum
 import pyaudio
 import speech_recognition as sr
-import sys
 
-r = sr.Recognizer()
-pa = pyaudio.PyAudio()
-def listAllMic():
-    """
-    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("Microphone with name \"{1}\" found for `Microphone(device_index={0})`".format(index, name))
 
-def record_microphone():
-    listAllMic()                 #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:")
-        audio = r.listen(source)
-    return audio
+class ML:
+    """Machine Learning class functions """
+    pa = pyaudio.PyAudio()
 
-def audio_to_text(audio):
-    try:
-        transcription = r.recognize_google(audio)
-        print(transcription)
-        print(type(transcription))
-    except sr.UnknownValueError:
-        print("Sorry, we could not recognize your response.")
-    except sr.RequestError as e:
-        print("Sorry, there appears to be an error with Google Speech to Text")
+    def list_all_mic(self):
+        """
+        List all available microphone device
+        """
+        if len(sr.Microphone.list_microphone_names()) == 0:
+            print("no device available")
+            return
 
-def grade_response(transcription):
-    """
-    Give out a score based on the transcribed audio
+        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
+
+
+        Args:
+            transcription (str): transcribed audio
+        """
+        print("working on it...")
+        grade = "Your response had "
+        for bw in ML.BuzzWord:
+            bw_count = (str)(transcription.lower().count(bw.value))
+            grade += bw.value + " appearing " + bw_count + " times "
+        return grade
 
-    Args:
-        transcription (str): transcribed audio
-    """
-    print("working on it...")
-
 
 def main():
+    """Main Method"""
     print("Tell me a little bit about yourself")
-    audio = record_microphone()
-    transcription = audio_to_text(audio)
-    result = grade_response(transcription)
+    ml = ML()
+    audio = ml.record_microphone()
+    transcription = ml.audio_to_text(audio)
+    result = ml.grade_response(transcription)
+    print(result)
+
 
 if __name__ == "__main__":
-    main()
+    main()

machine-learning-client/tests/ml_tests.py

@@ -0,0 +1,34 @@
+"""
+tests for machine-learning-client
+"""
+from machine_learning_client import ML
+
+
+
+
+class Tests:
+    """ 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,
+            r"C:\Users\Administrator\Desktop\NYU_Undergrad\SoftwareEngineering"
+            r"\4-containerized-app-exercise-rizzballs\machine-learning-client\tests\richard.aiff"
+        )
+        assert isinstance(
+            actual, str
+        ), f"Expected audio_to_text to return a string. Instead it returned a {actual}"
+        assert (
+            actual.lower() == "my name is richard"
+        ), f"Expected the converted audio to be 'my name is richard'. Instead it returned {actual}"
+
+    def test_grade_response(self):
+        """testing grade_response function in ML with a transcription with 5 hellos"""
+        actual = ML.grade_response(self, "hello hello hello hello hello")
+        assert isinstance(
+            actual, str
+        ), f"Expected grade_response to return a string. Instead it returned a {actual}"
+        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 \
+            ugh appearing 0 times '. Instead it returned {actual}"

web-app/Dockerfile

@@ -0,0 +1,12 @@
+FROM python:3.11
+WORKDIR /app
+ADD . /app
+
+RUN pip install --trusted-host pypi.python.org -r requirements.txt
+
+EXPOSE 80
+
+# Define an environment variable... this will be available to programs running inside the container
+ENV NAME World
+# Run app.py when the container launches
+CMD ["python", "app.py"]

web-app/app.py

@@ -34,3 +34,4 @@ def analyzeData():
 if __name__ == "__main__":
     PORT = os.getenv('PORT', 5000) 
     app.run(debug=True, port=PORT)
+

web-app/requirements.txt

@@ -0,0 +1,2 @@
+flask
+pymongo