Commit
·
95a7280
0
Parent(s):
initial commit
Browse files- .gitignore +4 -0
- App/Analytics/AnalyticsRoutes.py +7 -0
- App/Analytics/Model.py +23 -0
- App/Analytics/Schemas.py +13 -0
- App/Embedding/EmbeddingRoutes.py +22 -0
- App/Embedding/Schemas.py +14 -0
- App/Embedding/text.py +793 -0
- App/Embedding/utils/Elastic.py +29 -0
- App/Embedding/utils/Initialize.py +48 -0
- App/Embedding/utils/__init__.py +31 -0
- App/Worker.py +46 -0
- App/__main__.py +8 -0
- App/app.py +41 -0
- Dockerfile +25 -0
- README.md +8 -0
- requirements.txt +7 -0
.gitignore
ADDED
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*.pyc
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*.session
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*.session-journal
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.env
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App/Analytics/AnalyticsRoutes.py
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from fastapi import APIRouter, status
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from .Schemas import BaseRequest, editRequest
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from .Model import Comments
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from App.Users.Model import User
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from App.Post.Model import Post
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analytics_router = APIRouter(tags=["Analytics"])
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App/Analytics/Model.py
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import asyncio
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import orm
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import psycopg2
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import datetime
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import pydantic
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from App.modelInit import database, models
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from App.Users.Model import User
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class Analytics(orm.Model):
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tablename = "Analytics"
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registry = models
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fields = {
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"id": orm.Integer(primary_key=True),
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"user": orm.ForeignKey(
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User, on_delete=orm.CASCADE, allow_null=True
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), # Optional for unknown users.
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"post": orm.ForeignKey(User, on_delete=orm.CASCADE),
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"ip": orm.String(max_length=100),
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"device": orm.String(max_length=100),
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"country": orm.String(max_length=100),
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"createdAt": orm.DateTime(index=True, default=datetime.datetime.now),
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}
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App/Analytics/Schemas.py
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from typing import List, Optional
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from pydantic import EmailStr, BaseModel
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from datetime import date, datetime, time, timedelta
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class BaseRequest(BaseModel):
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user: Optional[int]
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id: Optional[int]
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content: Optional[str]
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class editRequest(BaseRequest):
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updatedAt: datetime = datetime.now()
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App/Embedding/EmbeddingRoutes.py
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from fastapi import APIRouter
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from .utils.Initialize import TextSearch, IdSearch
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from .Schemas import SearchRequest, AddDocumentRequest
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embeddigs_router = APIRouter(tags=["embeddings"])
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# create
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@embeddigs_router.post("/add_document")
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async def create_embeddings(req: AddDocumentRequest):
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pass
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@embeddigs_router.post("/search_id")
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async def search_id(req: SearchRequest):
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return IdSearch(query=req.query)
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@embeddigs_router.post("/search_text")
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async def search_text(req: SearchRequest):
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return TextSearch(query=req.query)
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App/Embedding/Schemas.py
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from pydantic import BaseModel
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class AddDocumentRequest(BaseModel):
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content: str
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metadata: dict
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class SearchRequest(BaseModel):
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query: str
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class GetTranscriptions(BaseModel):
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userId: int
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App/Embedding/text.py
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| 704 |
+
"caption": [
|
| 705 |
+
"The Quest 2022 S01E01"
|
| 706 |
+
]
|
| 707 |
+
},
|
| 708 |
+
"sort": [
|
| 709 |
+
1001
|
| 710 |
+
]
|
| 711 |
+
}
|
| 712 |
+
]
|
| 713 |
+
}
|
| 714 |
+
}
|
| 715 |
+
}
|
| 716 |
+
},
|
| 717 |
+
{
|
| 718 |
+
"_index": "telegram_media",
|
| 719 |
+
"_type": "_doc",
|
| 720 |
+
"_id": "18777",
|
| 721 |
+
"_score": 1,
|
| 722 |
+
"_source": {
|
| 723 |
+
"rating": 7.8,
|
| 724 |
+
"genres": [
|
| 725 |
+
"action",
|
| 726 |
+
"adventure",
|
| 727 |
+
"fantasy",
|
| 728 |
+
"sci-fi"
|
| 729 |
+
],
|
| 730 |
+
"poster": "/jRXYjXNq0Cs2TcJjLkki24MLp7u.jpg",
|
| 731 |
+
"type": "movie",
|
| 732 |
+
"imageUrl": "http://telegra.ph/file/af29523b9678b3f754520.jpg",
|
| 733 |
+
"year": 2009,
|
| 734 |
+
"title": "Avatar",
|
| 735 |
+
"imdb_id": "tt0499549",
|
| 736 |
+
"image": "/app/src/Temp/tt0499549JPEG.jpeg",
|
| 737 |
+
"caption": "Avatar",
|
| 738 |
+
"filecode": "h3b15rrf8dvr"
|
| 739 |
+
},
|
| 740 |
+
"fields": {
|
| 741 |
+
"imdb_id.keyword": [
|
| 742 |
+
"tt0499549"
|
| 743 |
+
]
|
| 744 |
+
},
|
| 745 |
+
"inner_hits": {
|
| 746 |
+
"simple": {
|
| 747 |
+
"hits": {
|
| 748 |
+
"total": {
|
| 749 |
+
"value": 1,
|
| 750 |
+
"relation": "eq"
|
| 751 |
+
},
|
| 752 |
+
"max_score": null,
|
| 753 |
+
"hits": [
|
| 754 |
+
{
|
| 755 |
+
"_index": "telegram_media",
|
| 756 |
+
"_type": "_doc",
|
| 757 |
+
"_id": "18777",
|
| 758 |
+
"_score": null,
|
| 759 |
+
"_source": {
|
| 760 |
+
"rating": 7.8,
|
| 761 |
+
"genres": [
|
| 762 |
+
"action",
|
| 763 |
+
"adventure",
|
| 764 |
+
"fantasy",
|
| 765 |
+
"sci-fi"
|
| 766 |
+
],
|
| 767 |
+
"poster": "/jRXYjXNq0Cs2TcJjLkki24MLp7u.jpg",
|
| 768 |
+
"type": "movie",
|
| 769 |
+
"imageUrl": "http://telegra.ph/file/af29523b9678b3f754520.jpg",
|
| 770 |
+
"year": 2009,
|
| 771 |
+
"title": "Avatar",
|
| 772 |
+
"imdb_id": "tt0499549",
|
| 773 |
+
"image": "/app/src/Temp/tt0499549JPEG.jpeg",
|
| 774 |
+
"caption": "Avatar",
|
| 775 |
+
"filecode": "h3b15rrf8dvr"
|
| 776 |
+
},
|
| 777 |
+
"fields": {
|
| 778 |
+
"caption": [
|
| 779 |
+
"Avatar"
|
| 780 |
+
]
|
| 781 |
+
},
|
| 782 |
+
"sort": [
|
| 783 |
+
9223372036854776000
|
| 784 |
+
]
|
| 785 |
+
}
|
| 786 |
+
]
|
| 787 |
+
}
|
| 788 |
+
}
|
| 789 |
+
}
|
| 790 |
+
}
|
| 791 |
+
]
|
| 792 |
+
}
|
| 793 |
+
}
|
App/Embedding/utils/Elastic.py
ADDED
|
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from elasticsearch import Elasticsearch
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
# initialize elasticSearch
|
| 5 |
+
es = Elasticsearch(
|
| 6 |
+
[
|
| 7 |
+
"https://u46hxt12c:[email protected]:443"
|
| 8 |
+
]
|
| 9 |
+
)
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
def FetchDocuments(ids):
|
| 13 |
+
es_index_name = "telegram_media"
|
| 14 |
+
res = es.search(
|
| 15 |
+
index=es_index_name,
|
| 16 |
+
body={
|
| 17 |
+
"query": {"terms": {"imdb_id.keyword": ids, "boost": 1.0}},
|
| 18 |
+
"collapse": {
|
| 19 |
+
"field": "imdb_id.keyword",
|
| 20 |
+
"inner_hits": {
|
| 21 |
+
"name": "simple",
|
| 22 |
+
"collapse": {"field": "caption"},
|
| 23 |
+
"sort": [{"season_number": {"order": "asc"}}],
|
| 24 |
+
"size": 1,
|
| 25 |
+
},
|
| 26 |
+
},
|
| 27 |
+
},
|
| 28 |
+
)
|
| 29 |
+
return res
|
App/Embedding/utils/Initialize.py
ADDED
|
@@ -0,0 +1,48 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from langchain.embeddings import HuggingFaceEmbeddings
|
| 2 |
+
from langchain.docstore.document import Document
|
| 3 |
+
from langchain.vectorstores import Pinecone
|
| 4 |
+
import os,requests
|
| 5 |
+
import pinecone,pprint
|
| 6 |
+
from .Elastic import FetchDocuments
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
index_name = 'movie-recommender-fast'
|
| 10 |
+
model_name = "thenlper/gte-base"
|
| 11 |
+
embeddings = HuggingFaceEmbeddings(model_name=model_name)
|
| 12 |
+
|
| 13 |
+
TMDB_API=os.environ.get('TMDB_API')
|
| 14 |
+
|
| 15 |
+
# get api key from app.pinecone.io
|
| 16 |
+
PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY')
|
| 17 |
+
# find your environment next to the api key in pinecone console
|
| 18 |
+
PINECONE_ENV = os.environ.get('PINECONE_ENVIRONMENT')
|
| 19 |
+
|
| 20 |
+
pinecone.init(
|
| 21 |
+
api_key=PINECONE_API_KEY,
|
| 22 |
+
environment=PINECONE_ENV
|
| 23 |
+
)
|
| 24 |
+
|
| 25 |
+
docsearch = Pinecone.from_existing_index(index_name, embeddings)
|
| 26 |
+
|
| 27 |
+
def generate_text(doc):
|
| 28 |
+
if doc['tv_results']:
|
| 29 |
+
return pprint.pformat(doc['tv_results'][0]),doc['tv_results'][0]
|
| 30 |
+
return pprint.pformat(doc['movie_results'][0]),doc['movie_results'][0]
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
def IdSearch(query:str):
|
| 35 |
+
doc=requests.get(f'https://api.themoviedb.org/3/find/{query}?external_source=imdb_id&language=en&api_key={TMDB_API}').json()
|
| 36 |
+
text,_=generate_text(doc)
|
| 37 |
+
return TextSearch(text,filter={"key": {"$ne":query}})
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
def TextSearch(query: str,filter=None):
|
| 42 |
+
docs = docsearch.similarity_search(query,k=10,filter=filter)
|
| 43 |
+
keys= [ doc.metadata['key'] for doc in docs ]
|
| 44 |
+
return FetchDocuments(keys)
|
| 45 |
+
|
| 46 |
+
|
| 47 |
+
|
| 48 |
+
|
App/Embedding/utils/__init__.py
ADDED
|
@@ -0,0 +1,31 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from elasticsearch import Elasticsearch
|
| 2 |
+
import os
|
| 3 |
+
|
| 4 |
+
elastic_host=os.environ.get('ELASTIC_HOST')
|
| 5 |
+
|
| 6 |
+
# initialize elasticSearch
|
| 7 |
+
es = Elasticsearch(
|
| 8 |
+
[
|
| 9 |
+
elastic_host
|
| 10 |
+
]
|
| 11 |
+
)
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
def FetchDocuments(ids):
|
| 15 |
+
es_index_name = "telegram_media"
|
| 16 |
+
res = es.search(
|
| 17 |
+
index=es_index_name,
|
| 18 |
+
body={
|
| 19 |
+
"size": 10,
|
| 20 |
+
"query": {"terms": {"imdb_id.keyword": ids, "boost": 1.0}},
|
| 21 |
+
"collapse": {
|
| 22 |
+
"field": "imdb_id.keyword",
|
| 23 |
+
"inner_hits": {
|
| 24 |
+
"name": "simple",
|
| 25 |
+
"collapse": {"field": "caption"},
|
| 26 |
+
"sort": [{"season_number": {"order": "asc"}}],
|
| 27 |
+
"size": 1,
|
| 28 |
+
},
|
| 29 |
+
},
|
| 30 |
+
},
|
| 31 |
+
)
|
App/Worker.py
ADDED
|
@@ -0,0 +1,46 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from celery import Celery, chain
|
| 2 |
+
import os
|
| 3 |
+
import time
|
| 4 |
+
from App import celery_config
|
| 5 |
+
import yt_dlp
|
| 6 |
+
import tempfile
|
| 7 |
+
from App.Transcription.Utils.audio_transcription import transcribe_file
|
| 8 |
+
from App.Embedding.utils.Initialize import encode, generateChunks
|
| 9 |
+
|
| 10 |
+
celery = Celery()
|
| 11 |
+
celery.config_from_object(celery_config)
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
@celery.task(name="embbeding", bind=True)
|
| 15 |
+
def generate_store(self, data, task_id):
|
| 16 |
+
chunks = generateChunks(data, task_id)
|
| 17 |
+
encode(chunks)
|
| 18 |
+
print("hellooo")
|
| 19 |
+
|
| 20 |
+
|
| 21 |
+
@celery.task(name="transcription", bind=True)
|
| 22 |
+
def transcription_task(self, file_path, model_size="tiny"):
|
| 23 |
+
data = transcribe_file(state=self, file_path=file_path, model_size=model_size)
|
| 24 |
+
generate_store.delay(data["content"], self.request.id)
|
| 25 |
+
return data
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
@celery.task(name="download", bind=True)
|
| 29 |
+
def downloadfile(self, url, ydl_opts, model_size="base"):
|
| 30 |
+
# updated
|
| 31 |
+
self.update_state(state="Downloading File..", meta={})
|
| 32 |
+
|
| 33 |
+
####
|
| 34 |
+
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
|
| 35 |
+
ydl.download([url])
|
| 36 |
+
|
| 37 |
+
# updated
|
| 38 |
+
self.update_state(state="Downloading complete", meta={})
|
| 39 |
+
audio_file = ydl_opts["outtmpl"]
|
| 40 |
+
print(model_size, "worker after")
|
| 41 |
+
# print(audio_file["default"])
|
| 42 |
+
data = transcribe_file(
|
| 43 |
+
state=self, file_path=audio_file["default"], model_size=model_size
|
| 44 |
+
)
|
| 45 |
+
generate_store.delay(data["content"], self.request.id)
|
| 46 |
+
return data
|
App/__main__.py
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from App.app import app
|
| 2 |
+
|
| 3 |
+
import uvicorn
|
| 4 |
+
import asyncio
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
if __name__ == "__main__":
|
| 8 |
+
uvicorn.run(app, host="0.0.0.0", port=7860)
|
App/app.py
ADDED
|
@@ -0,0 +1,41 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from fastapi import FastAPI
|
| 2 |
+
|
| 3 |
+
from fastapi.middleware.gzip import GZipMiddleware
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
from .Embedding.EmbeddingRoutes import embeddigs_router
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
from fastapi.middleware.cors import CORSMiddleware
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
import logging
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
# Configure logging
|
| 16 |
+
logging.basicConfig(
|
| 17 |
+
level=logging.DEBUG,
|
| 18 |
+
format="%(asctime)s - %(levelname)s - %(message)s",
|
| 19 |
+
datefmt="%Y-%m-%d %H:%M:%S",
|
| 20 |
+
)
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
app = FastAPI()
|
| 24 |
+
origins = ["*"]
|
| 25 |
+
|
| 26 |
+
app.add_middleware(
|
| 27 |
+
CORSMiddleware,
|
| 28 |
+
allow_origins=origins,
|
| 29 |
+
allow_credentials=True,
|
| 30 |
+
allow_methods=["*"],
|
| 31 |
+
allow_headers=["*"],
|
| 32 |
+
)
|
| 33 |
+
app.add_middleware(GZipMiddleware, minimum_size=1000)
|
| 34 |
+
|
| 35 |
+
|
| 36 |
+
@app.get("/")
|
| 37 |
+
async def landing_page():
|
| 38 |
+
return {"code": 200, "message": "we are back!"}
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
app.include_router(embeddigs_router)
|
Dockerfile
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Builder stage
|
| 2 |
+
FROM python:latest as builder
|
| 3 |
+
|
| 4 |
+
RUN useradd -ms /bin/bash admin
|
| 5 |
+
|
| 6 |
+
WORKDIR /srv
|
| 7 |
+
RUN chown -R admin:admin /srv
|
| 8 |
+
RUN chmod 755 /srv
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
COPY requirements.txt .
|
| 15 |
+
RUN pip install --no-cache-dir -r requirements.txt
|
| 16 |
+
|
| 17 |
+
# Copy the application code
|
| 18 |
+
USER admin
|
| 19 |
+
|
| 20 |
+
COPY --chown=admin . /srv
|
| 21 |
+
|
| 22 |
+
# Command to run the application
|
| 23 |
+
CMD uvicorn App.app:app --host 0.0.0.0 --port 7860 --workers 8
|
| 24 |
+
# Expose the server port
|
| 25 |
+
EXPOSE 7860
|
README.md
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
title: Embedding
|
| 3 |
+
emoji: 🐳
|
| 4 |
+
colorFrom: purple
|
| 5 |
+
colorTo: gray
|
| 6 |
+
sdk: docker
|
| 7 |
+
app_port: 7860
|
| 8 |
+
---
|
requirements.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
fastapi
|
| 2 |
+
elasticsearch==7.7.0
|
| 3 |
+
pinecone-client[grpc]
|
| 4 |
+
sentence-transformers
|
| 5 |
+
langchain
|
| 6 |
+
uvicorn[standard]
|
| 7 |
+
pydantic
|