preliminary database setups

database/database.py.py

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+import os
+
+from pymongo import MongoClient
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Create a new client and connect to the server
+uri = os.getenv("MONGODB_URI").format(
+    os.getenv("MONGODB_USER"), os.getenv("MONGODB_PASSWORD")
+)
+client = MongoClient(uri, serverSelectionTimeoutMS=3000)
+DB = None
+
+# Send a ping to confirm a successful connection
+try:
+    client.admin.command("ping")
+    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)

machine-learning-client/Dockerfile.txt

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+FROM python:3.11
+
+WORKDIR /app
+
+COPY Pipfile Pipfile.lock ./
+
+RUN pip install pipenv && pipenv install --deploy --ignore-pipfile
+RUN apt-get -y update
+RUN apt-get -y install libasound-dev
+RUN apt-get -y install portaudio19-dev
+
+WORKDIR /usr/src/app
+COPY requirements.txt ./
+
+COPY . .
+
+RUN pip install -r requirements.txt
+
+CMD ["pipenv", "run", "python", "machine_learning_client.py"]

machine-learning-client/machine_learning_client.py

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+from flask import Flask, render_template, request, redirect, url_for, make_response, session
+import os
+from pymongo import MongoClient
+import pymongo
+import datetime
+from bson.objectid import ObjectId
+import sys
+
+app = Flask('project4')
+
+@app.route("/process_wav", methods=['GET', 'POST'])
+def process_wav():
+    if request.method == 'POST':
+        audio_file = request.form.get("audio-recording")
+        dir = 'audio/'+str(audio_file)
+
+if __name__ == "__main__":
+    PORT = os.getenv('PORT', 5000) 
+    app.run(debug=True,port=PORT)