[ADD] send message when analysis is done

.gitignore

@@ -1,3 +1,5 @@
 audioclip/assets/*
 **/*/__pycache__/*
-singing_in_noise/Figures_MS/*
+singing_in_noise/Figures_MS/*
+cloud_analysis/gmail_logs.env
+logs/logfile.log

CONFIG.yaml

@@ -3,6 +3,6 @@
 ######################################
 
 # List of paths to initialise the model
-path_tokenizer_file: "./assets/bpe_simple_vocab_16e6.txt.gz" 
-path_audioclip: './assets/AudioCLIP-Full-Training.pt' 
-path_snowmobile_model: "./assets/snowmobile_model.pth"
+path_tokenizer_file: "/app/audioclip/assets/bpe_simple_vocab_16e6.txt.gz" 
+path_audioclip: '/app/audioclip/assets/AudioCLIP-Full-Training.pt' 
+path_snowmobile_model: "/app/audioclip/assets/snowmobile_model.pth"

cloud_Dockerfile

@@ -24,9 +24,10 @@ RUN cd ../
 # Package install
 COPY . ./
 RUN poetry config virtualenvs.create false
-RUN poetry install --no-root
 RUN poetry add distlib
+RUN poetry install --no-root
 RUN poetry add Flask
+RUN poetry add python-dotenv
 
 
 ENV PYTHONPATH "${PYTHONPATH}:/app:/app/audioclip:/app:/app/src"

cloud_analysis/main.py

@@ -4,23 +4,65 @@
 from flask import Flask, request, jsonify
 app = Flask(__name__)
 
+import os
+from dotenv import load_dotenv
+
 import argparse
 import numpy as np
 
+import smtplib
+from email.message import EmailMessage
+
 import torch
 from torch.utils.data import DataLoader
 
 from predict import predict
 from predict import initModel
 from utils.utils import AudioList
 
+import logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+
+# Get the environment variables
+load_dotenv(dotenv_path="/app/cloud_analysis/gmail_logs.env")
+
+GMAIL_USER = os.environ.get('GMAIL_USER')
+GMAIL_PASS = os.environ.get('GMAIL_PASS')
+RECEIVER_EMAIL = os.environ.get('RECEIVER_EMAIL')
+
+if not GMAIL_USER or not GMAIL_PASS:
+    logging.error("GMAIL_USER or GMAIL_PASS environment variables are not set.")
+
+def send_email(subject, body):
+    msg = EmailMessage()
+    msg.set_content(body)
+    msg['Subject'] = subject
+    msg['From'] = GMAIL_USER
+    msg['To'] = RECEIVER_EMAIL
+
+    # Establish a connection to Gmail
+    try:
+        logging.info("Attempting to connect to Gmail SMTP server...")
+        server = smtplib.SMTP('smtp.gmail.com', 587)
+        server.starttls()
+        logging.info("Connected to Gmail SMTP server. Attempting login...")
+        server.login(GMAIL_USER, GMAIL_PASS)
+        logging.info("Logged in to Gmail. Sending email...")
+        server.send_message(msg)
+        server.quit()
+        logging.info("Email sent successfully!")
+    except Exception as e:
+        logging.error(f"Error sending email: {e}")
+
+
 
 def analyseAudioFile(
         audio_file_path, batch_size=1, num_workers=4, min_hr = 0.1, min_conf = 0.99
 ):
     # Initiate model
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model_path = "./assets/snowmobile_model.pth"
+    model_path = "/app/audioclip/assets/snowmobile_model.pth"
     model = initModel(model_path=model_path, device=device)
 
     # Run the predictions
@@ -67,22 +109,25 @@ def on_process_audio(
 
     # After processing we record each detection in the database. 
     # Each detection should have a start and end time which is used to create an audio clip later. 
+    count = 0
     detections = []
     for r in results: 
         start, end, confidence, harmonic_ratio = r
-        # create the detections
+        if harmonic_ratio > 0.1 and confidence > 0.99:
+            count += 1
+
+        # create the detections dataset
         detections.append({
             u"start": start,
             u"end": end,
             u"tags": ["snowmobile"],
+            u"confidence": confidence,
+            u"harmonic_ratio": harmonic_ratio,
             #u"analysisId": analysis_id,
             # Add any other information you want to record here
-            u"confidence": confidence,
-            u"harmonic_ratio": harmonic_ratio
         })
 
-    print(detections)
-    print(f"{audio_id} completed with {len(detections)} detections")
+    return count
 
 
 @app.route('/process-audio', methods=['POST'])
@@ -91,12 +136,15 @@ def process_audio_endpoint():
     audio_id = request.json['audio_id']
     audio_rec = request.json['audio_rec']
 
-    on_process_audio(audio_id, audio_rec, audio_file_path)
+    detection_count = on_process_audio(audio_id, audio_rec, audio_file_path)
+
+    #if detection_count > 0:
+    send_email("Snowmobile Detection Alert", f"{detection_count} snowmobile detections were made in the audio file!")
 
     return jsonify({"message": "Audio processing completed!"})
 
 
 if __name__ == "__main__":
-
+    app.debug = True
     app.run(host='0.0.0.0', port=8080)
 

cloud_analysis/test.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+
+curl -X POST -H "Content-Type: application/json" \
+    -d '{"audio_file_path": "/app/example/example_audio.mp3", "audio_id": "test-id", "audio_rec": {"location": {"latitude": 0, "longitude": 0}}}' \
+    http://localhost:8080/process-audio