Fix merge conflict

.gitignore

@@ -0,0 +1,212 @@
+.idea/
+
+# Created by https://www.gitignore.io/api/macos,linux,django,python,pycharm
+
+### Django ###
+*.log
+*.pot
+*.pyc
+__pycache__/
+local_settings.py
+db.sqlite3
+media
+
+### Linux ###
+*~
+
+# temporary files which can be created if a process still has a handle open of a deleted file
+.fuse_hidden*
+
+# KDE directory preferences
+.directory
+
+# Linux trash folder which might appear on any partition or disk
+.Trash-*
+
+# .nfs files are created when an open file is removed but is still being accessed
+.nfs*
+
+### macOS ###
+*.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+### PyCharm ###
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and Webstorm
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff:
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/dictionaries
+
+# Sensitive or high-churn files:
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.xml
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+
+# Gradle:
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# CMake
+cmake-build-debug/
+
+# Mongo Explorer plugin:
+.idea/**/mongoSettings.xml
+
+## File-based project format:
+*.iws
+
+## Plugin-specific files:
+
+# IntelliJ
+/out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+### PyCharm Patch ###
+# Comment Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-215987721
+
+# *.iml
+# modules.xml
+# .idea/misc.xml
+# *.ipr
+
+# Sonarlint plugin
+.idea/sonarlint
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+
+# Translations
+*.mo
+
+# Django stuff:
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site

app.py

@@ -0,0 +1,269 @@
+import discord
+from discord import Option
+import redis
+import os
+
+# Function to generate auto-incremented IDs
+def generate_id(key):
+    return r.incr(key)
+
+def generate_progress_bar(num_labels, width=20):
+    print(num_labels)
+    percentage = int(int(num_labels) / 300 * 100)
+    completed_blocks = int(percentage * width / 100)
+    remaining_blocks = width - completed_blocks
+
+    progress_bar = '▓' * completed_blocks + '░' * remaining_blocks
+    percentage_text = f' {num_labels}/50 identified 🦜🌱 '
+
+    return f'🏆 {progress_bar} {percentage_text}'
+
+# Redis client setup
+
+redis_url = 'redis://default:[email protected]:7369'
+r = redis.from_url(redis_url)
+
+r.set('species_identified', 0)
+
+def improve_player_stats(ctx):
+    # add 1 to the number of species identified
+    value = r.get(b'species_identified').decode('utf-8')
+    r.set(b'species_identified', int(value) + 1)
+
+    xp_key = f'{ctx.author.name}:XP'.encode('utf-8')
+    if not r.exists(xp_key):
+        r.set(xp_key, 0)
+
+    role_key = f'{ctx.author.name}:role'.encode('utf-8')
+    if not r.exists(role_key):
+        r.set(role_key, 'Naturalist')
+
+    oc_key = f'{ctx.author.name}:occurances'.encode('utf-8')
+    if not r.exists(oc_key):
+        r.set(oc_key, 0)
+    r.set(xp_key, int(r.get(xp_key)) + 10)
+    r.set(oc_key, int(r.get(oc_key)) + 1)
+
+bot = discord.Bot()
+
+@bot.event
+async def on_ready():
+    print(f"{bot.user} is ready and online!")
+
+@bot.command(
+            description="Get image predictions", 
+            options=[
+                Option(name="id", description="The ID of the image")
+            ])
+async def predict_image(ctx, id):
+    # Fetch the image data from Redis
+    image_data_bytes = r.hgetall(f'image:{id}'.encode('utf-8'))
+    image_data = {k.decode('utf-8'): v.decode('utf-8') for k, v in image_data_bytes.items()}
+    path = f'https://gainforest-transparency-dashboard.s3.amazonaws.com/{image_data["awsCID"]}'
+    print(image_data)
+    message = f"**Image Observation: {id} by _{image_data['sensor']}_**"
+
+    message += "\n_Below are Top 10 predictions of AI_"
+    emoji_unicode_list = [chr(0x30 + i) + '\uFE0F\u20E3' for i in range(11)]
+
+    prediction_data_bytes = r.hgetall(f'prediction:{id}:1'.encode('utf-8'))
+    prediction_data = {k.decode('utf-8'): v.decode('utf-8') for k, v in prediction_data_bytes.items()}
+
+    embed = discord.Embed(
+        title="",
+        description=f"Label: {prediction_data['label']}\nPredictions of our AI algorithms",
+        color=discord.Colour.blurple(), # Pycord provides a class with default colors you can choose from
+    )
+    embed.set_author(name=f"Image Observation {id} - AI Predictions", icon_url=ctx.author.display_avatar.url)
+
+    for i in range(1, 10):
+        prediction_data_bytes = r.hgetall(f'prediction:{id}:{i}'.encode('utf-8'))
+        prediction_data = {k.decode('utf-8'): v.decode('utf-8') for k, v in prediction_data_bytes.items()}
+        species = prediction_data['label']
+        score = prediction_data['confidence']
+        # message = message + f'\n> {emoji_unicode_list[i-1]} {species} with confidence {score}'
+        embed.add_field(name=f'{species}', value=f'Confidence: {float(score):.4f}', inline=True)
+    embed.set_thumbnail(url=path)
+
+    # Send the image
+    await ctx.respond("Hint: You can use [NParks Flora & Fauna Database](https://www.nparks.gov.sg/florafaunaweb) to verify", embed=embed)
+
+@bot.command(
+            description="Get image observation", 
+            options=[
+                Option(name="id", description="The ID of the image")
+            ])
+async def image(ctx, id):
+    # Fetch the image data from Redis
+    image_data_bytes = r.hgetall(f'image:{id}'.encode('utf-8'))
+    image_data = {k.decode('utf-8'): v.decode('utf-8') for k, v in image_data_bytes.items()}
+    print(image_data)
+
+    # Create an embed
+    embed = discord.Embed(color=discord.Colour.blurple())
+    embed.set_author(name=f"Image Observation {id} - Task", icon_url=ctx.author.display_avatar.url)
+    embed.add_field(name="Sensor", value=image_data['sensor'], inline=True)
+    # embed.add_field(name="Latest Label by", value=image_data['author'], inline=True)
+    path = f'https://gainforest-transparency-dashboard.s3.amazonaws.com/{image_data["awsCID"]}'
+    embed.set_image(url=path)
+    await ctx.respond(embed=embed)
+
+@bot.command(
+        description="Label image observation",
+        options=[
+            Option(name="id", description="The ID of the image"),
+            Option(name="label", description="The label for the image"),
+            Option(name="confidence", description="The confidence for the label"),
+    ])
+async def label_image(ctx, id:int, label:str, confidence:int):
+    label_cnt = generate_id(f'label:{id}')
+    r.hset(f'label:{id}:{label_cnt}'.encode('utf-8'), b'label', label.encode('utf-8'))
+    r.hset(f'label:{id}:{label_cnt}'.encode('utf-8'), b'author', ctx.author.mention.encode('utf-8'))
+    r.hset(f'label:{id}:{label_cnt}'.encode('utf-8'), b'confidence', ctx.author.mention.encode('utf-8'))
+
+    # Fetch the image data from Redis
+    image_data_bytes = r.hgetall(f'image:{id}'.encode('utf-8'))
+    image_data = {k.decode('utf-8'): v.decode('utf-8') for k, v in image_data_bytes.items()}
+    print(image_data)
+
+    # Create an embed
+    embed = discord.Embed(color=discord.Colour.blurple())
+    embed.set_author(name=f"Image Observation {id} - New Label 🎉", icon_url=ctx.author.display_avatar.url)
+    embed.add_field(name="Sensor", value=image_data['sensor'], inline=True)
+    embed.add_field(name="Label", value=image_data['label'], inline=True)
+    embed.add_field(name="Proposed by", value=image_data['author'], inline=True)
+    embed.set_image(url=image_data['path'])
+
+    improve_player_stats(ctx)
+
+    await ctx.respond(f"{ctx.author.mention} labeled observation **{id}** as **{label}**  🎉 (Earned 10 XP)", embed=embed)
+
+@bot.command(description="Get progress bar.")
+async def progress(ctx):
+    value = r.get(b'species_identified').decode('utf-8')
+    progress_bar = generate_progress_bar(value)
+    await ctx.respond(progress_bar) # this decorator makes a slash command
+
+@bot.command(description="Get sound data.") # this decorator makes a slash command
+async def sound(ctx, id): # a slash command will be created with the name "ping"
+     # Fetch the sound data from Redis
+    sound_data_bytes = r.hgetall(f'sound:{id}'.encode('utf-8'))
+    sound_data = {k.decode('utf-8'): v.decode('utf-8') for k, v in sound_data_bytes.items()}
+
+    # Create an embed
+    embed = discord.Embed(color=discord.Colour.blurple())
+    embed.set_author(name=f"Sound Observation {id} - Task", icon_url=ctx.author.display_avatar.url)
+    embed.add_field(name="Sensor", value=sound_data['sensor'], inline=True)
+    embed.add_field(name="Label", value=sound_data['label'], inline=True)
+    embed.add_field(name="Proposed by", value=sound_data['author'], inline=True)
+    embed.add_field(name="Timestamp", value=sound_data['time'], inline=True)
+
+    with open(sound_data['path'], 'rb') as f:
+        sound = discord.File(f)
+        message = await ctx.respond(file=sound, embed=embed)
+        print('The ID of the message is:', message.id)
+
+@bot.command(
+        description="Label sound observation",
+        options=[
+            Option(name="id", description="The ID of the sound"),
+            Option(name="label", description="The label for the sound"),
+            Option(name="timestamp", description="The timestamp of the observation"),
+    ])
+async def label_sound(ctx, id:int, label:str, timestamp:str):
+    r.hset(f'sound:{id}'.encode('utf-8'), b'label', label.encode('utf-8'))
+    r.hset(f'sound:{id}'.encode('utf-8'), b'time', timestamp.encode('utf-8'))
+    r.hset(f'sound:{id}'.encode('utf-8'), b'author', ctx.author.mention.encode('utf-8'))
+
+    # Fetch the sound data from Redis
+    sound_data_bytes = r.hgetall(f'sound:{id}'.encode('utf-8'))
+    sound_data = {k.decode('utf-8'): v.decode('utf-8') for k, v in sound_data_bytes.items()}
+
+    # Create an embed
+    embed = discord.Embed(color=discord.Colour.blurple())
+    embed.set_author(name=f"Sound Observation {id} - New Label 🎉", icon_url=ctx.author.display_avatar.url)
+    embed.add_field(name="Sensor", value=sound_data['sensor'], inline=True)
+    embed.add_field(name="Label", value=sound_data['label'], inline=True)
+    embed.add_field(name="Proposed by", value=sound_data['author'], inline=True)
+    embed.add_field(name="Time", value=sound_data['time'], inline=True)
+
+    improve_player_stats(ctx)
+
+    with open(sound_data['path'], 'rb') as f:
+        sound = discord.File(f)
+        await ctx.respond(f"{ctx.author.mention} labeled sound observation **{id}** as **{label}** at **{timestamp}**  🎉 (Earned 10 XP)", file=sound, embed=embed)
+
+@bot.command(
+        description="List all unlabeled sound observations"
+)
+async def list_unlabeled_sound(ctx):
+
+    unlabeled_ids = []
+
+    # Fetch all the sound data from Redis
+    keys = r.keys('sound:*'.encode('utf-8'))
+    for key in keys:
+        sound_data_bytes = r.hgetall(key)
+        sound_data = {k.decode('utf-8'): v.decode('utf-8') for k, v in sound_data_bytes.items()}
+        if '❓' == sound_data['label']:
+            unlabeled_ids = unlabeled_ids + [key.decode('utf-8')]
+
+    # Create an embed
+    embed = discord.Embed(color=discord.Colour.blurple())
+    embed.set_author(name=f"Unlabeled Sound Observations", icon_url=ctx.author.display_avatar.url)
+
+    for id in unlabeled_ids:
+        embed.add_field(name=f"{id}", value="❓", inline=True)
+
+    await ctx.respond(embed=embed)
+
+@bot.command(
+        description="List all unlabeled image observations"
+)
+async def list_unlabeled_image(ctx):
+
+    unlabeled_ids = []
+
+    # Fetch all the image data from Redis
+    keys = r.keys('image:*'.encode('utf-8'))
+    for key in keys:
+        image_data_bytes = r.hgetall(key)
+        image_data = {k.decode('utf-8'): v.decode('utf-8') for k, v in image_data_bytes.items()}
+        if '❓' == image_data['label']:
+            unlabeled_ids = unlabeled_ids + [key.decode('utf-8')]
+
+    # Create an embed
+    embed = discord.Embed(color=discord.Colour.blurple())
+    embed.set_author(name=f"Unlabeled Image Observations", icon_url=ctx.author.display_avatar.url)
+
+    for id in unlabeled_ids:
+        embed.add_field(name=f"{id}", value="❓", inline=True)
+
+    await ctx.respond(embed=embed)
+
+@bot.command(
+        description="Check your profile"
+    )
+async def profile(ctx):
+    xp_key = f'{ctx.author.name}:XP'.encode('utf-8')
+    if not r.exists(xp_key):
+        r.set(xp_key, 0)
+
+    role_key = f'{ctx.author.name}:role'.encode('utf-8')
+    if not r.exists(role_key):
+        r.set(role_key, 'NATURALIST')
+
+    oc_key = f'{ctx.author.name}:occurances'.encode('utf-8')
+    if not r.exists(oc_key):
+        r.set(oc_key, 0)
+
+     # Create an embed
+    embed = discord.Embed(color=discord.Colour.blurple())
+    embed.set_author(name=f"{ctx.author.name} - Profile", icon_url=ctx.author.display_avatar.url)
+    embed.add_field(name="**PROGRESS**", value=f"**Level**: 1\n**XP**: {r.get(xp_key).decode()}/1000\n", inline=False)
+    embed.add_field(name="**STATS**", value=f"**🦜 Labels**: {r.get(oc_key).decode()}", inline=False)
+    embed.set_thumbnail(url=ctx.author.display_avatar.url)
+    embed.set_footer(text=f"🏆 RANK: {r.get(role_key).decode()}")
+    await ctx.respond(embed=embed)
+
+    bot.run('MTA5NzMzMDI2MDI4NDAzNTEyMg.GNFiQP.hZC_2HLTvVAROlKKUmnVQjviT0G4wHeQq23-rs')

requirements.txt

@@ -0,0 +1,3 @@
+py-cord
+redis
+os

scripts.py

@@ -0,0 +1,18 @@
+import os
+import sys
+
+def run_ffmpeg(input_variable, output_variable):
+    # Construct the FFmpeg command using the input and output variables
+    command = f'ffmpeg -i {input_variable} -vf "scale=640:480" -b:v 1000k -vcodec libx264 -acodec aac {output_variable}'
+
+    # Execute the FFmpeg command using os.system()
+    os.system(command)
+
+if __name__ == '__main__':
+    # Check if the script is being executed directly
+    if len(sys.argv) != 3:
+        print('Usage: python script.py input_file output_file')
+    else:
+        input_file = sys.argv[1]
+        output_file = sys.argv[2]
+        run_ffmpeg(input_file, output_file)

sql2redis.py

@@ -0,0 +1,125 @@
+import redis
+import psycopg2
+from gradio_client import Client
+import json
+
+redis_url = 'redis://default:[email protected]:7369'
+r = redis.from_url(redis_url)
+# run josh's algorithm
+client = Client("https://diverseco-metaformer.hf.space/")
+
+# Postgres database get the observations
+def connect():
+    conn = None
+    try:
+        # Connecting to your PostgreSQL server
+        print('Connecting to the PostgreSQL database...')
+        conn = psycopg2.connect('postgresql://postgres:[email protected]:7297/railway')
+        # conn = psycopg2.connect('postgresql://postgres:[email protected]:6771/railway') #staging
+        # conn = psycopg2.connect('postgresql://postgres:[email protected]:5772/railway') #development
+
+    except (Exception, psycopg2.DatabaseError) as error:
+        print(error)
+
+    print("Connection successful")
+    return conn
+
+def get_all_images():
+    query = """SELECT "id", "awsCID" FROM "Asset" WHERE "projectId" = 37 AND "classification" = 'Camera Traps'"""
+    conn = connect()
+    cur = conn.cursor()
+    
+    try:
+        # Execute a simple SQL command
+        cur.execute(query)
+        
+        # Fetch all the data returned by the database
+        rows = cur.fetchall()
+        for row in rows:
+            image_cnt = generate_id('image_cnt')
+            # Set multiple field-value pairs using HMSET
+            fields_values = {
+                'uuid': row[0],
+                'awsCID': row[1],
+                'sensor': 'Rover/RGB',
+                'label': '❓',
+                'author': '❓',
+                'timestamp': '❓'
+            }
+            r.hmset(f'image:{image_cnt}', fields_values)
+
+            image_url = f'https://gainforest-transparency-dashboard.s3.amazonaws.com/{row[1]}'
+            print(f'predicting {image_url}')
+
+            result = client.predict(
+                image_url,	
+                api_name="/predict"
+            )
+
+            with open(result, 'r') as file:
+                json_data = json.load(file)
+
+            # Extract labels and confidences from JSON data
+            labels = [data['label'] for data in json_data['confidences']]
+            confidences = [data['confidence'] for data in json_data['confidences']]
+
+            # Store labels and confidences in Redis using HMSET
+            for label, confidence in zip(labels, confidences):
+                pred_cnt = generate_id(f'prediction:{image_cnt}')
+                fields_values = {
+                    'label': label,
+                    'confidence': confidence,
+                }
+                r.hmset(f'prediction:{image_cnt}:{pred_cnt}', fields_values)
+
+            
+    except (Exception, psycopg2.DatabaseError) as error:
+        print(error)
+
+    finally:
+        # Close the cursor and connection
+        cur.close()
+        conn.close()
+        r.close()
+
+def get_all_sounds():
+    query = """SELECT "id", "awsCID" FROM "Asset" WHERE "projectId" = 37 AND "classification" = 'Bioacoustics'"""
+    conn = connect()
+    cur = conn.cursor()
+    
+    try:
+        # Execute a simple SQL command
+        cur.execute(query)
+        
+        # Fetch all the data returned by the database
+        rows = cur.fetchall()
+        for row in rows:
+            sound_cnt = generate_id('sound_cnt')
+            # Set multiple field-value pairs using HMSET
+            fields_values = {
+                'uuid': row[0],
+                'awsCID': row[1],
+                'sensor': 'Speedybee/Acoustic',
+                'label': '❓',
+                'author': '❓',
+                'label_at': '❓',
+                'timestamp': '❓'
+            }
+            r.hmset(f'sound:{sound_cnt}', fields_values)
+            
+    except (Exception, psycopg2.DatabaseError) as error:
+        print(error)
+
+    finally:
+        # Close the cursor and connection
+        cur.close()
+        conn.close()
+        r.close()
+
+# Function to generate auto-incremented IDs
+def generate_id(key):
+    return r.incr(key)
+
+r.flushdb()
+get_all_images()
+get_all_sounds()