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This commit is contained in:
Josh Knapp 2025-01-02 19:38:42 -08:00
parent 6c57a8591f
commit 91bbe303ba
4 changed files with 160 additions and 15 deletions

1
.gitignore vendored
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@ -1 +1,2 @@
scripts/.env
v2/.env

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"""
title: Bedrock Image Description
author: Josh Knapp
version: 0.1.0
description="Provide Direct Bedrock call for image generation"
"""
import subprocess
import json
from pydantic import BaseModel, Field
# Try to import boto3, install if not present
try:
import boto3
except ImportError:
print("boto3 package not found. Attempting to install...")
try:
subprocess.check_call([sys.executable, "-m", "pip", "install", "boto3"])
import boto3
print("boto3 package installed successfully")
except subprocess.CalledProcessError as e:
print(f"Failed to install boto3 package: {str(e)}")
class Tools:
class Valves(BaseModel):
AWS_ACCESS_KEY: str = Field(
default="",
description="AWS Access Key",
)
AWS_SECRET_KEY: str = Field(
default="",
description="AWS Secret Key",
)
AWS_BEDROCK_MODEL: str = Field(
default="",
description="AWS Bedrock Model to use"
)
def __init__(self):
self.valves = self.Valves()
pass
def analyze_image(self, base64_image: str) -> str:
"""
Analyze an image using AWS Bedrock's vision model
Args:
base64_image (str): Base64 encoded image string
Returns:
str: Description of the image
"""
try:
# Initialize Bedrock runtime client
bedrock = boto3.client(
service_name="bedrock-runtime",
aws_access_key_id=self.valves.AWS_ACCESS_KEY,
aws_secret_access_key=self.valves.AWS_SECRET_KEY,
region_name="us-east-1" # or your preferred region
)
# Prepare the request body
request_body = {
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": 1000,
"messages": [
{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/jpeg",
"data": base64_image
}
},
{
"type": "text",
"text": "Please describe this image in detail."
}
]
}
]
}
# Invoke the model
response = bedrock.invoke_model(
modelId=self.valves.AWS_BEDROCK_MODEL,
body=json.dumps(request_body)
)
# Parse and return the response
response_body = json.loads(response['body'].read())
return response_body['messages'][0]['content'][0]['text']
except Exception as e:
print(f"Error analyzing image: {str(e)}")
return f"Error analyzing image: {str(e)}"

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For any model to use this tool, you must set the valve values, and add something to the model to let it know to use the tool.
```
You have access to a tool that allows you to get descriptions of images called "Bedrock Image Description". Any image handling should be sent through this tool.
```

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import os
import discord
from discord.ext import commands
import openai
from openai import OpenAI
import base64
import requests
from io import BytesIO
from collections import deque
from dotenv import load_dotenv
@ -35,28 +36,55 @@ bot = commands.Bot(command_prefix='!', intents=intents)
# Message history cache
channel_history = {}
async def download_image(url):
response = requests.get(url)
if response.status_code == 200:
image_data = BytesIO(response.content)
base64_image = base64.b64encode(image_data.read()).decode('utf-8')
return base64_image
return None
async def get_chat_history(channel, limit=100):
messages = []
async for message in channel.history(limit=limit):
messages.append(f"{message.author.name}: {message.content}")
content = f"{message.author.name}: {message.content}"
# Handle attachments (images)
for attachment in message.attachments:
if any(attachment.filename.lower().endswith(ext) for ext in ['.png', '.jpg', '.jpeg', '.gif', '.webp']):
content += f" [Image: {attachment.url}]"
messages.append(content)
return "\n".join(reversed(messages))
async def get_ai_response(context, user_message):
formatted_prompt = f"##CONTEXT##\n{context}\n##ENDCONTEXT##\n\n{user_message}"
async def get_ai_response(context, user_message, image_urls=None):
messages = [{"role": "user", "content": []}]
# Add text content
text_content = f"##CONTEXT##\n{context}\n##ENDCONTEXT##\n\n{user_message}"
messages[0]["content"].append({"type": "text", "text": text_content})
# Add image content if present
if image_urls:
for url in image_urls:
base64_image = await download_image(url)
if base64_image:
messages[0]["content"].append({
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
})
try:
response = client.chat.completions.create(model=MODEL_NAME,
messages=[
{"role": "user", "content": formatted_prompt}
])
response = client.chat.completions.create(
model=MODEL_NAME,
messages=messages
)
return response.choices[0].message.content
except Exception as e:
return f"Error: {str(e)}"
@bot.event
async def on_ready():
print(f'{bot.user} has connected to Discord!')
@bot.event
async def on_message(message):
# Ignore messages from the bot itself
@ -81,14 +109,25 @@ async def on_message(message):
# Remove bot mention from the message
user_message = message.content.replace(f'<@{bot.user.id}>', '').strip()
# Collect image URLs from the message
image_urls = []
for attachment in message.attachments:
if any(attachment.filename.lower().endswith(ext) for ext in ['.png', '.jpg', '.jpeg', '.gif', '.webp']):
image_urls.append(attachment.url)
# Get AI response
response = await get_ai_response(history, user_message)
response = await get_ai_response(history, user_message, image_urls)
# Send response
await message.reply(response)
await bot.process_commands(message)
@bot.event
async def on_ready():
print(f'{bot.user} has connected to Discord!')
def main():
if not all([DISCORD_TOKEN, OPENAI_API_KEY, OPENWEBUI_API_BASE, MODEL_NAME]):
print("Error: Missing required environment variables")