implement local vllm model usage
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@ -8,7 +8,7 @@ services:
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vllm:
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image: vllm/vllm-openai:latest
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command: "--model Qwen/Qwen2-VL-2B-Instruct"
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command: "--model Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4 --quantization gptq"
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volumes:
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- ~/.cache/huggingface:/root/.cache/huggingface
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ports:
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@ -149,5 +149,6 @@ CELERY_BROKER_URL = "amqp://guest:guest@localhost/"
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OPENAI_API_BASE = os.getenv("OPENAI_API_BASE")
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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#OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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OPENAI_API_KEY = "test"
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OPENAI_MODEL = os.getenv("OPENAI_MODEL", "openai/gpt-4o")
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@ -1,15 +1,12 @@
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import requests
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import base64
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import litellm
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import os
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from loguru import logger
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from celery import shared_task, Task
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from celery import shared_task
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from django.db import transaction
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from django.core.files.storage import default_storage
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from django.conf import settings
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from .models import ImageMemo, MemoStatus
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from datetime import datetime
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TRANSCRIBE_PROMPT = """Transcribe the hand written notes in the attached image and present them as markdown.
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@ -18,9 +15,12 @@ Do not use a fence, simply respond using markdown.
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If any words or letters are unclear, denote them with a '?<word>?'.
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For example if you were not sure whether a word is blow or blew you would transcribe it as '?blow?'
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Please include whitespace and formatting for headings too.
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"""
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@shared_task
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def process_memo(memo_id: str):
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"""Run OCR on a memo and store the output"""
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@ -67,6 +67,7 @@ def process_memo(memo_id: str):
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response = litellm.completion(
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model=settings.OPENAI_MODEL, #os.getenv("MODEL", "openai/gpt-4o"),
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messages=[message],
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temperature=0.01
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)
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response.choices[0].message["content"]
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@ -36,11 +36,11 @@
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</tr>
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<tr>
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<td class="font-medium pr-4">Created:</td>
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<td class="text-gray-600">{{ document.created_at|date:"d/m/Y H:i" }}</td>
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<td class="text-gray-600">{{ document.created_at|date:"d/m/Y H:i:s" }}</td>
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</tr>
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<tr>
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<td class="font-medium pr-4">Updated:</td>
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<td class="text-gray-600">{{ document.updated_at|date:"d/m/Y H:i" }}</td>
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<td class="text-gray-600">{{ document.updated_at|date:"d/m/Y H:i:s" }}</td>
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</tr>
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</table>
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{% if document.content %}
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