feat: Integrate edge gpt

apps/open-sourcerer/Dockerfile

@@ -10,20 +10,8 @@ ADD . /app
 # Install any needed packages specified in requirements.txt
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Clone the Pycord-Development repository and install it
-RUN git clone https://github.com/Pycord-Development/pycord && \
-    cd pycord && \
-    pip install -U .
-
 # Make port 80 available to the world outside this container
 EXPOSE 80
 
-ENV ELEVEN_LABS_API_KEY="" \
-    OPENAI_API_KEY="" \
-    DISCORD_TOKEN="" \
-    API_KEY="" \
-    API_BASE="" \
-    SYSTEM_MESSAGE=""
-
 # Run DiscordInterpreter.py when the container launches
 CMD ["python", "main.py"]

apps/open-sourcerer/docker-compose.yaml

@@ -4,3 +4,5 @@ services:
     build: .
     ports:
       - "80:80"
+    env_file:
+      - ./.env

apps/open-sourcerer/main.py

@@ -3,15 +3,15 @@ import discord
 from discord.ext import commands
 import interpreter
 import dotenv
-from voice import transcribe
+import whisper
 
 dotenv.load_dotenv(".env")
 
 bot_id = os.getenv("BOT_ID")
 bot_token = os.getenv("DISCORD_TOKEN")
 
-interpreter.api_key = os.getenv("API_KEY")
-interpreter.api_base = os.getenv("API_BASE")
+interpreter.api_key = os.getenv("OPENAI_API_KEY")
+# interpreter.api_base = os.getenv("API_BASE")
 # interpreter.auto_run = True
 
 def split_text(text, chunk_size=1500):
@@ -26,12 +26,31 @@ client = commands.Bot(command_prefix="$", intents=intents)
 message_chunks = []
 send_image = False
 
+model = whisper.load_model("base")
+
+def transcribe(audio):
+
+    # load audio and pad/trim it to fit 30 seconds
+    audio = whisper.load_audio(audio)
+    audio = whisper.pad_or_trim(audio)
+
+    # make log-Mel spectrogram and move to the same device as the model
+    mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+    # detect the spoken language
+    _, probs = model.detect_language(mel)
+
+    # decode the audio
+    options = whisper.DecodingOptions()
+    result = whisper.decode(model, mel, options)
+    return result.text
+
 @client.event
 async def on_message(message):
     await client.process_commands(message)
     bot_mention = f"<@{bot_id}>"
-    if (bot_mention in message.content) or (message.author == client.user or message.content[0] == '$'):
-        return
+    # if ("<@1158923910855798804>" in message.content) or (message.author == client.user or message.content[0] == '$'):
+        # return
     response = []
     for chunk in interpreter.chat(message.content, display=False, stream=False):
         # await message.channel.send(chunk)

apps/open-sourcerer/requirements.txt

@@ -1,4 +1,5 @@
 openai-whisper
+py-cord
 discord
 open-interpreter
 elevenlabs

apps/open-sourcerer/voice.py

@@ -5,33 +5,14 @@ import gradio as gr
 from pydub import AudioSegment
 import io
 from elevenlabs import generate, play, set_api_key
-import whisper
 import dotenv
 
 dotenv.load_dotenv(".env")
 
 # interpreter.model = "TheBloke/Mistral-7B-OpenOrca-GGUF"
 interpreter.auto_run = True
-model = whisper.load_model("base")
 
 
-def transcribe(audio):
-
-    # load audio and pad/trim it to fit 30 seconds
-    audio = whisper.load_audio(audio)
-    audio = whisper.pad_or_trim(audio)
-
-    # make log-Mel spectrogram and move to the same device as the model
-    mel = whisper.log_mel_spectrogram(audio).to(model.device)
-
-    # detect the spoken language
-    _, probs = model.detect_language(mel)
-
-    # decode the audio
-    options = whisper.DecodingOptions()
-    result = whisper.decode(model, mel, options)
-    return result.text
-
 
 set_api_key("ELEVEN_LABS_API_KEY")
 

apps/orchistrator/Dockerfile

@@ -7,8 +7,4 @@ RUN npm install
 
 COPY . .
 
-ENV DISCORD_TOKEN "" \
-    DISCORD_CLIENT_ID "" \
-    DISCORD_GUILD_ID ""
-
 CMD [ "node", "index.js" ]

apps/orchistrator/docker-compose.yml

@@ -6,7 +6,5 @@ services:
     image: allenrkeen/server-bot:latest
     volumes:
       - /var/run/docker.sock:/var/run/docker.sock #required
-    environment:
-      - DISCORD_TOKEN=your_token_here #required
-      - DISCORD_CLIENT_ID=your_client_id_here #required
-      - DISCORD_GUILD_ID=your_guild_id_here #optional
+    env_file:
+      - ./.env  #  environment:

swarms/memory/requirements.txt

@@ -0,0 +1,8 @@
+attrs==21.2.0
+griptape==0.18.2
+oceandb==0.1.0
+pgvector==0.2.3
+pydantic==1.10.8
+SQLAlchemy==1.4.49
+SQLAlchemy==2.0.20
+swarms==1.8.2

swarms/models/bing_chat.py

@@ -0,0 +1,62 @@
+"""EdgeGPT model by OpenAI"""
+import asyncio, json
+from EdgeGPT.EdgeGPT import Chatbot, ConversationStyle
+from EdgeGPT.EdgeUtils import ImageQuery, Query, Cookie
+from EdgeGPT.ImageGen import ImageGen
+from pathlib import Path
+
+class EdgeGPTModel:
+    """
+    EdgeGPT model by OpenAI
+
+    Parameters
+    ----------
+    cookies_path : str
+        Path to the cookies.json necessary for authenticating with EdgeGPT
+    
+    Examples
+    --------
+    >>> edgegpt = EdgeGPTModel(cookies_path="./path/to/cookies.json")
+    >>> response = edgegpt.ask("Hello, my name is ChatGPT")
+    >>> image_path = edgegpt.generate_image("Sunset over mountains")
+
+    """
+    
+    def __init__(self, cookies_path: str):
+        self.cookies = json.loads(open(cookies_path, encoding="utf-8").read())
+        self.bot = asyncio.run(Chatbot.create(cookies=self.cookies))
+        
+    def ask(self, prompt: str, style: ConversationStyle = ConversationStyle.creative) -> str:
+        """
+        Get a text response using the EdgeGPT model based on the provided prompt.
+        """
+        response = asyncio.run(self.bot.ask(prompt=prompt, conversation_style=style, simplify_response=True))
+        return response['text']
+
+    def generate_image(self, prompt: str, output_dir: str = "./output", auth_cookie: str = None) -> str:
+        """
+        Generate an image based on the provided prompt and save it in the given output directory.
+        Returns the path of the generated image.
+        """
+        if not auth_cookie:
+            raise ValueError("Auth cookie is required for image generation.")
+        
+        image_generator = ImageGen(auth_cookie, quiet=True)
+        images = image_generator.get_images(prompt)
+        image_generator.save_images(images, output_dir=output_dir)
+        
+        return Path(output_dir) / images[0]['path']
+
+    @staticmethod
+    def set_cookie_dir_path(path: str):
+        """
+        Set the directory path for managing cookies.
+        """
+        Cookie.dir_path = Path(path)
+
+# Example Usage:
+# edgegpt = EdgeGPTModel(cookies_path="./path/to/cookies.json")
+# text_response = edgegpt.ask("Hello, my name is ChatGPT")
+# image_path = edgegpt.generate_image("Sunset over mountains", auth_cookie="YOUR_AUTH_COOKIE")
+
+