Black and linted app.py, finshed up a majority of the readme

README.md

@@ -1,14 +1,34 @@
 # Interview Review WebApp
-![WebApp Machine Learning build test](https://github.com/software-students-fall2023/4-containerized-app-exercise-rizzballs/blob/main/.github/workflows/event-logger.yml/badge.svg)
-![WebApp Subsystems build test](https://github.com/software-students-fall2023/4-containerized-app-exercise-rizzballs/blob/main/.github/workflows/lint.yml/badge.svg)
+![WebApp Machine Learning build test](https://https://github.com/software-students-fall2023/4-containerized-app-exercise-rizzballs/blob/main/.github/workflows/event-logger.yml/badge.svg)
+![WebApp Subsystems build test](https://https://github.com/software-students-fall2023/4-containerized-app-exercise-rizzballs/blob/main/.github/workflows/lint.yml/badge.svg)
 
 ## What is our WebApp?
 
-Our WebApp is designed to help users practice and improve their interview skills. When prompted, users will speak into the microphhone and answer 3 generic interview questions. The app will record the interview and breakdown the answer given, giving a rating based on specified critera and words used. Then, it is compared to other user attempts to give a comparison and return statistics of the prompted user. 
+Our WebApp is designed to help users practice and improve their interview skills. When prompted, users will speak into the microphhone and answer a generic interview question. Utilizing [SpeechRecogntion](https://pypi.org/project/SpeechRecognition/), the app will record the interview and breakdown the answer given, giving a rating based on specified critera and words used. Then, it is compared to other user attempts to give a comparison and return statistics of the prompted user.
 
 ## Installation and Usage
 
-In Progress
+### Method 1: Cloning the Github Repository
+1. Clone the directory through Git Bash with the command:
+
+```
+https://github.com/software-students-fall2023/4-containerized-app-exercise-rizzballs.git
+```
+
+2. Open Docker Desktop
+
+3. In your command prompt, access the directory where you cloned the repository:
+```
+cd "path_to_directory"
+```
+
+4. From here, run the commands:
+```
+docker-compose build
+docker-compose up
+```
+
+5. Now, access the http://127.0.0.1:5000/ in your browser of choice.
 
 ### Install ffmpeg (Windows)
 1. Go to the official ffmpeg website, hover over the windows icon, and click Windows builds from gyan.dev

machine-learning-client/machine_learning_client.py

@@ -22,13 +22,14 @@
 client = MongoClient("mongodb://mongodb:27017/")
 db = client["ml_databse"]
 collection = db["transcription"]
- 
+
 app.config["UPLOAD_FOLDER"] = "uploads"
 os.makedirs(app.config["UPLOAD_FOLDER"], exist_ok=True)
 PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 
 pa = pyaudio.PyAudio()
 
+
 def add_transcription_mongo(transcription, grade_report):
     """
     Save transcription to MongoDB
@@ -41,6 +42,7 @@ def add_transcription_mongo(transcription, grade_report):
     except Exception as e:
         print(f"Error saving transcription to MongoDB: {e}")
 
+
 def audio_to_text(audio_file_string):
     """Function for converting audio file to text transcription"""
     r = sr.Recognizer()
@@ -58,11 +60,12 @@ def audio_to_text(audio_file_string):
     print("success!! we transcribed it")
     return transcription
 
+
 def grade_transcription(transcription_text):
     """
     Function to generate a page giving a break down and grade of an audio transcript someone had just recorded
     """
-   
+
     grade = 0
     filler_words = 0
     buzz_words = 0
@@ -84,7 +87,7 @@ def grade_transcription(transcription_text):
     buzz_words += transcription_text.lower().count("certificate")
     buzz_words += transcription_text.lower().count("qualification")
     buzz_words += transcription_text.lower().count("work")
-    buzz_words += transcription_text.lower().count("interest")  
+    buzz_words += transcription_text.lower().count("interest")
     buzz_words += transcription_text.lower().count("develop")
     buzz_words += transcription_text.lower().count("fulfill")
     buzz_words += transcription_text.lower().count("refine")
@@ -96,21 +99,20 @@ def grade_transcription(transcription_text):
     bz_breakdown = ""
     grade_analysis = ""
 
-
     if buzz_words == 0:
         bz_breakdown = "You did not use any buzz words. When answering this question, try to talk about your education, work experiences, major, or any jobs you had."
     elif 0 < buzz_words <= 5:
         bz_breakdown = f"You used {buzz_words} buzz words. A decent answer, but you can improve. Try to find more things to say about yourself professionally."
     else:
-        bz_breakdown = f"You used {buzz_words} buzz words. The topics of your answer are acceptable. Make sure to "
+        bz_breakdown = f"You used {buzz_words} buzz words. The topics of your answer are acceptable. Make sure to keep in mind your wording and topics."
 
     if filler_words == 0:
         fw_breakdown = "You did not use any filler words. Or there was a problem with the audio transcript. Either way, remember to talk confidently and at a good pace."
     elif 0 < filler_words <= 5:
         fw_breakdown = f"You used {filler_words} filler words. From here, you just need to practice your answer until it comes out naturally. Make sure to keep relaxed."
     else:
         fw_breakdown = f"You used {filler_words} filler words. When giving your answer, try speaking slower and more clearly. Don't confuse yourself by thinking too far ahead."
-    
+
     grade = (buzz_words * 3) - filler_words
     print(grade)
     if grade > 20:
@@ -127,31 +129,37 @@ def grade_transcription(transcription_text):
         grade_analysis = f"Your grade is {grade}/20. You gave a very good response. Everything beyond this is fine tuning your response to the interviewer."
     else:
         grade_analysis = f"Your grade is {grade}/20. This should be your go to answer, but it all depends on the interviewer. This answer should could out naturally and be your basis."
-    
+
     grade_report = {
         "filler_words": fw_breakdown,
         "buzz_words": bz_breakdown,
-        "grade_analysis": grade_analysis
+        "grade_analysis": grade_analysis,
     }
 
     print(fw_breakdown)
     print(bz_breakdown)
     print(grade_analysis)
     return grade_report
 
+
 @app.route("/analyzeAudio", methods=["POST"])
 def analyze_Audios():
     """
     Endpoint to receive and analyze audio file
     """
     try:
         if "audio" not in request.files:
-            return jsonify({"status": "error", "message": "No audio file provided"}), 400
-
+            return (
+                jsonify({"status": "error", "message": "No audio file provided"}),
+                400,
+            )
+
         audio_file = request.files["audio"]
 
         # Delete existing files if they exist
-        existing_audio_path = os.path.join(app.config["UPLOAD_FOLDER"], "user_audio.wav")
+        existing_audio_path = os.path.join(
+            app.config["UPLOAD_FOLDER"], "user_audio.wav"
+        )
         if os.path.exists(existing_audio_path):
             os.remove(existing_audio_path)
 
@@ -174,20 +182,24 @@ def analyze_Audios():
         print(true_audio_path)
         # Call your functions from ml.py to process the audio
         print("calling audio to text method")
-        transcription = audio_to_text(true_audio_path_str) 
+        transcription = audio_to_text(true_audio_path_str)
         print(transcription)
-        print("calling grade to text method") 
+        print("calling grade to text method")
         gradedTranscription = grade_transcription(transcription)
         add_transcription_mongo(transcription, gradedTranscription)
-
 
         print("we did it baby")
-        return jsonify({"status": "success", "message": "Audio analysis completed"}), 200
-
+        return (
+            jsonify({"status": "success", "message": "Audio analysis completed"}),
+            200,
+        )
+
     except Exception as e:
-        return jsonify({"status": "error", "message": f"An error occurred: {str(e)}"}), 500
+        return (
+            jsonify({"status": "error", "message": f"An error occurred: {str(e)}"}),
+            500,
+        )
 
 
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=int(os.getenv("PORT", "5001")), debug=True)
-

web-app/app.py

@@ -4,13 +4,10 @@
 
 import os
 from flask import Flask, render_template, request, jsonify
-import sys
-import pymongo
 from pymongo import MongoClient
 import requests
 
 
-
 app = Flask("project4")
 
 
@@ -37,8 +34,9 @@ def display_results():
     if not my_transcript:
         return jsonify({"error": "Result not found"}), 404
 
-    return render_template("results.html", transcription_result=my_transcript, activePage="results.html")
-
+    return render_template(
+        "results.html", transcription_result=my_transcript, activePage="results.html"
+    )
 
 
 @app.route("/analyzeData", methods=["POST"])
@@ -60,16 +58,16 @@ def analyze_data():
         if response.status_code == 200:
             result = response.json()
             return jsonify(result)
-        else:
-            return (
-                jsonify(
-                    {"error": "Failed to send and process audio. Please try again."}
-                ),
-                500,
-            )
+        return (
+            jsonify(
+                {"error": "Failed to send and process audio. Please try again."}
+            ),
+            500,
+        )
 
     except FileNotFoundError as e:
         return jsonify({"status": "error", "message": f"File not found: {str(e)}"})
 
+
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=int(os.getenv("PORT", "5000")), debug=True)
+    app.run(host="0.0.0.0", port=int(os.getenv("PORT", "5000")), debug=True)

web-app/templates/root.html

@@ -9,7 +9,7 @@
 <body>
     {% extends 'base.html' %} 
     {% block Base %}
-        <p>our application aims to help you get better at interviewing by help you practice one of th emost common interview question. To use our application press teh start recording button and begin answering the question. When you finish answering press stop recording and wait for a grade response is ready message before navigating to teh results page</p>
+        <p>Our application aims to help you improve your interview skills by helping you practice one of the most common interview questions. To use our application press the start recording button and begin answering the question. When you finish answering press stop recording and wait for a grade response is ready message before navigating to the results page.</p>
         <h1>Question: tell me about yourself?</h3>
 
         <div class="buttonContainer">