minor important changes

.gitignore

@@ -1,3 +1,4 @@
 *.pyc
 *.session
-*.session-journal
+*.session-journal
+.env

.vscode/settings.json

@@ -1,9 +1,7 @@
 {
-  "cSpell.words": [
-    "websockets"
-  ],
+  "cSpell.words": ["websockets"],
   "[python]": {
-    "editor.defaultFormatter": "ms-python.black-formatter"
+    "editor.defaultFormatter": "ms-python.python"
   },
   "python.formatting.provider": "none"
 }

App/Embedding/EmbeddingRoutes.py

@@ -1,9 +1,23 @@
 from fastapi import APIRouter
-from .Schemas import BaseRequest, GetTranscriptions
-from App.Users.Model import User
+from App.Transcription.Model import Transcriptions
+from .utils.Initialize import generateChunks, encode, search
 
 embeddigs_router = APIRouter(tags=["embeddings"])
 
+
 # create
+@embeddigs_router.get("/create_embeddings")
+async def create_embeddings(task_id):
+    item = await Transcriptions.objects.filter(task_id=task_id).first()
+    temp = item.content
+    chunks = generateChunks(temp, task_id)
+    encode(chunks)
+
+    return
+
+
 # search
 # update?
+@embeddigs_router.get("/search_embeddings")
+async def search_embeddings(query, task_id):
+    return search(query=query, task_id=task_id)

App/Embedding/utils/Initialize.py

@@ -0,0 +1,139 @@
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.docstore.document import Document
+from langchain.vectorstores import MongoDBAtlasVectorSearch
+from langchain.vectorstores import FAISS
+from pymongo import MongoClient
+from motor.motor_asyncio import AsyncIOMotorClient
+
+template = """### Given the following context 
+### Context
+{context}
+
+### Use it to explain the question: {question}
+ """
+
+
+async def fetch_data(question, context):
+    url = "https://chimeragpt.adventblocks.cc/api/v1/chat/completions"
+
+    payload = json.dumps(
+        {
+            "messages": [
+                {
+                    "role": "system",
+                    "content": "### You provide explanations based on the provided context",
+                },
+                {
+                    "role": "user",
+                    "content": template.format(context=context, question=question),
+                },
+            ],
+            "model": "gpt-3.5-turbo",
+            "temperature": 1,
+            "presence_penalty": 0,
+            "top_p": 0.95,
+            "frequency_penalty": 0,
+            "stream": False,
+        }
+    )
+
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": "Bearer 7j3MQ912kGjUx8AUKHUiFLC7MCWBPT_cmlJRVdYGUtE",
+    }
+
+    async with aiohttp.ClientSession() as session:
+        async with session.post(url, headers=headers, data=payload) as response:
+            response = await response.json()
+            return response["choices"][0]["message"]["content"]
+
+
+async def delete_documents(task_id):
+    client = AsyncIOMotorClient(
+        "mongodb+srv://pMBONEA:MBONEA@cluster0.vlpsa.mongodb.net/?retryWrites=true&w=majority"
+    )
+    db = client["transcriptions"]
+    collection = db["videos"]
+
+    result = await collection.delete_many({"task_id": task_id})
+    print(f"Deleted {result.deleted_count} document(s)")
+
+
+# mongo_client = MongoClient(
+#     "mongodb+srv://pMBONEA:MBONEA@cluster0.vlpsa.mongodb.net/?retryWrites=true&w=majority"
+# )
+# model_name = "BAAI/bge-base-en"
+# collection = mongo_client["transcriptions"]["videos"]
+# embeddings = HuggingFaceEmbeddings(model_name=model_name)
+# index_name = "test_embeddings"
+# vectorstore = MongoDBAtlasVectorSearch(collection, embeddings, index_name=index_name)
+
+
+def generateChunks(chunks, task_id, n=100):
+    combined = [chunks[i : i + n] for i in range(0, len(chunks), n)]
+    result = []
+    for chunk in combined:
+        data = {"text": ""}
+        for item in chunk:
+            if chunk.index(item) == 0:
+                data["start"] = item["start"]
+            if chunk.index(item) == len(chunk) - 1:
+                data["end"] = item["end"]
+            data["text"] += " " + item["text"]
+
+        temp = Document(
+            page_content=data["text"],
+            metadata={"start": data["start"], "end": data["end"], "task_id": task_id},
+        )
+        result.append(temp)
+    return result
+
+
+def search(query: str, task_id: str):
+    mongo_client = MongoClient(
+        "mongodb+srv://pMBONEA:MBONEA@cluster0.vlpsa.mongodb.net/?retryWrites=true&w=majority"
+    )
+    model_name = "BAAI/bge-base-en"
+    collection = mongo_client["transcriptions"]["videos"]
+    embeddings = HuggingFaceEmbeddings(model_name=model_name)
+    index_name = "test_embedding"
+    k = 5
+    vectorstore = MongoDBAtlasVectorSearch(
+        collection,
+        embedding=embeddings,
+        index_name="test_embedding",
+    )
+
+    data = vectorstore.similarity_search(
+        query=query,
+        pre_filter={"text": {"path": "task_id", "query": task_id}},
+        search_kwargs={
+            "k": k,  # overrequest k during search
+            "pre_filter": {"path": "task_id", "equals": task_id},
+            "post_filter_pipeline": [{"$limit": k}],  # limit results to top k
+        },
+    )
+    # data =[d.dict() for d in data]
+    # print(data[0].metadata.exclude({'_id','embedding'}))
+    return [{"text": d.page_content} for d in data]
+    # agent =vectorstore.as_retriever(
+
+    # )
+    # return agent.get_relevant_documents
+
+
+def encode(temp: list[Document]):
+    mongo_client = MongoClient(
+        "mongodb+srv://pMBONEA:MBONEA@cluster0.vlpsa.mongodb.net/?retryWrites=true&w=majority"
+    )
+    model_name = "BAAI/bge-base-en"
+    collection = mongo_client["transcriptions"]["videos"]
+    embeddings = HuggingFaceEmbeddings(model_name=model_name)
+    index_name = "test_embedding"
+    vectorstore = MongoDBAtlasVectorSearch(
+        collection, embeddings, index_name=index_name
+    )
+    vectorstore.from_documents(
+        temp, embedding=embeddings, collection=collection, index_name=index_name
+    )
+    # return  embeddings.embed_documents(texts = [d.page_content for d in temp])

App/Embedding/utils/Summarizer.py

@@ -0,0 +1,118 @@
+from langchain import OpenAI
+from langchain.chains.summarize import load_summarize_chain
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain import PromptTemplate
+import openai,os
+
+openai.api_base = os.environ.get("api_base")
+openai.api_key = os.environ.get("api_key")
+openai_api_key = openai.api_key
+
+
+import json
+def read_transcriptions_from_file(file_path):
+    transcriptions = []
+    with open(file_path, 'r') as file:
+        lines = file.readlines()
+        for line in lines:
+            part = json.loads(line)
+
+            transcriptions.append(part)
+    return transcriptions
+def combine_n_segments(transcriptions, n=2000):
+    combined_segments = []
+    current_segment = None
+
+    for i, transcription in enumerate(transcriptions):
+        if current_segment is None:
+            current_segment = transcription
+        else:
+            # Calculate the new end time based on the current segment and the next transcription
+            current_segment['end'] = transcription['end']
+            # Combine the texts with a space between
+            current_segment['text'] += transcription['text']
+
+        # If the next transcription is not continuous or n segments are combined, start a new segment
+        if (transcription['end'] != current_segment['end']) or (i % n == n - 1):
+            combined_segments.append(current_segment)
+            current_segment = None
+
+    # Append the last segment if needed
+    if current_segment is not None:
+        combined_segments.append(current_segment)
+
+    return combined_segments
+
+
+
+
+llm = OpenAI(
+    temperature=0 ,openai_api_key=openai.api_key, openai_api_base=openai.api_base
+)
+
+
+paul_graham_essay = "./sample.txt"
+
+with open(paul_graham_essay, "r") as file:
+    essay = file.read()
+
+essay = read_transcriptions_from_file("./sample.txt")
+essay = combine_n_segments(essay)
+
+def generate_summary(data):
+    template = """ {data} """.format(data=data)
+    chunk_size = 2500
+    inc = 100
+    max_tokens = 2000
+    min_tokens = 1500
+    max_token_doc = 0
+
+    # choose the appropriate chunk size
+    while True:
+        # initialize text splitter
+        text_splitter = RecursiveCharacterTextSplitter(
+            separators=["\n\n", "\n"],
+            chunk_size=chunk_size,
+            # chunk_overlap=int(chunk_size * 0.1),
+        )
+        docs = text_splitter.create_documents([template])
+        temp =[]
+        for doc in docs:
+            temp.append(llm.get_num_tokens(doc.page_content))
+        max_token_doc = max(temp)
+        if max_tokens < max_token_doc or max_token_doc < min_tokens:
+            if max_tokens < max_token_doc:
+                chunk_size -= inc
+            else:
+                chunk_size += inc
+            print(max_token_doc,chunk_size)
+            continue
+            
+        else:
+            break
+    map_prompt = """
+    ### Write a summary of the following, video transcript segment, return a detailed summary  citing the time stamps which are in seconds
+    ### cite using seconds ONLY
+    "{text}"
+    Detailed SUMMARY:
+"""
+
+
+    map_prompt_template = PromptTemplate(template=map_prompt, input_variables=["text"])
+    combine_prompt = """
+    ### Write a summary of the following, video transcript segment summaries, return a detailed summary.
+    Return your response citing the time stamps which are in seconds.
+    #cite using seconds ONLY
+    ```{text}```
+    SUMMARY:
+    """
+    combine_prompt_template = PromptTemplate(template=combine_prompt, input_variables=["text"])
+    summary_chain = load_summarize_chain(llm=llm,
+                                     chain_type='map_reduce',
+                                     map_prompt=map_prompt_template,
+                                     combine_prompt=combine_prompt_template,
+                                     verbose=True
+                                    )
+    summary=summary_chain.run(docs)
+    print(summary)
+generate_summary(essay)

App/Transcription/TranscriptionRoutes.py

@@ -18,6 +18,7 @@ from .Model import Transcriptions
 from .Utils.fastapi_tasks import perform_background_task
 import yt_dlp
 from fastapi_jwt_auth import AuthJWT
+from App.Embedding.utils.Initialize import delete_documents
 
 # from .Model import User
 # from sqlalchemy import and_
@@ -51,7 +52,7 @@ async def download_audio(
         r"(?u)[^-\w.]", "", video_title
     )  # Ensure the title is file-friendly
     filename = f"{sanitized_title}.mp3"
-    file_path = os.path.join("/srv/App/", "Downloads", filename)
+    file_path = os.path.join("./", "Downloads", filename)
 
     ydl_opts = {
         "format": "bestaudio/best",
@@ -88,10 +89,18 @@ async def delete_transcription(
         .filter(task_id=task_id)
         .first()
     )
-    await transcript.delete()
-    task = AsyncResult(task_id)
-    task.revoke(terminate=True)
-    return {"code": 200, "message": f"deleted {task_id}", "payload": None}
+    if transcript:
+        await transcript.delete()
+        task = AsyncResult(task_id)
+        task.revoke(terminate=True)
+        await delete_documents(task_id=task_id)
+        return {"code": 200, "message": f"deleted {task_id}", "payload": None}
+    else:
+        return {
+            "code": 404,
+            "message": f"task {task_id} does not exist",
+            "payload": None,
+        }
 
 
 @transcription_router.post("/uploadfile/")

App/Worker.py

@@ -1,18 +1,29 @@
-from celery import Celery
+from celery import Celery, chain
 import os
 import time
 from App import celery_config
 import yt_dlp
 import tempfile
 from App.Transcription.Utils.audio_transcription import transcribe_file
+from App.Embedding.utils.Initialize import encode, generateChunks
 
 celery = Celery()
 celery.config_from_object(celery_config)
 
 
+@celery.task(name="embbeding", bind=True)
+def generate_store(self, data, task_id):
+    chunks = generateChunks(data, task_id)
+    encode(chunks)
+    print("hellooo")
+
+
 @celery.task(name="transcription", bind=True)
 def transcription_task(self, file_path, model_size="tiny"):
-    return transcribe_file(state=self, file_path=file_path, model_size=model_size)
+    # data = transcribe_file(state=self, file_path=file_path, model_size=model_size)
+    chunks = generateChunks(data["content"], task_id)
+    encode(chunks)
+    return data
 
 
 @celery.task(name="download", bind=True)
@@ -27,7 +38,9 @@ def downloadfile(self, url, ydl_opts, model_size="tiny"):
     # updated
     self.update_state(state="Downloading complete", meta={})
     audio_file = ydl_opts["outtmpl"]
-    print(audio_file["default"])
-    return transcribe_file(
+    # print(audio_file["default"])
+    data = transcribe_file(
         state=self, file_path=audio_file["default"], model_size=model_size
     )
+    generate_store.delay(data["content"], self.request.id)
+    return data

App/app.py

@@ -1,5 +1,6 @@
 from fastapi import FastAPI, Request
 from fastapi.responses import JSONResponse
+from fastapi.middleware.gzip import GZipMiddleware
 from App import bot
 
 
@@ -9,6 +10,7 @@ from .Transcription.TranscriptionRoutes import transcription_router
 from .Streaming.StreamingRoutes import streaming_router
 from .UserTranscriptions.UserTranscriptionsRoutes import user_transcriptions_router
 from .Monitor.monitorRoutes import monitor_router
+from .Embedding.EmbeddingRoutes import embeddigs_router
 
 
 from fastapi.middleware.cors import CORSMiddleware
@@ -35,7 +37,7 @@ app.add_middleware(
     allow_methods=["*"],
     allow_headers=["*"],
 )
-
+app.add_middleware(GZipMiddleware, minimum_size=1000)
 
 @app.exception_handler(AuthJWTException)
 def authjwt_exception_handler(request: Request, exc: AuthJWTException):
@@ -75,3 +77,4 @@ app.include_router(transcription_router)
 app.include_router(streaming_router)
 app.include_router(monitor_router)
 app.include_router(user_transcriptions_router)
+app.include_router(embeddigs_router)

App/celery_config.py

@@ -7,7 +7,7 @@ accept_content = ["json"]
 timezone = "Europe/Oslo"
 enable_utc = True
 
-broker_url = f"rediss://default:59c9ffda43f61cc18b44a3407a2a7793@master.transcription--legal-stuff--a96n-7tyr.addon.code.run:6379?ssl_cert_reqs=none"
+broker_url = f"rediss://default:8a715dd4651c8739b157d0ee0bb2d924@master.cache--j5zxzwppzvjs.addon.code.run:6379?ssl_cert_reqs=none"
 result_backend = f"db+postgresql+psycopg2://postgres:PkkneZrSFsnJR6B@db.vfhoydxvxuesxhrcdnmx.supabase.co:5432/postgres"
 
 # SSL/TLS and SNI configuration
@@ -15,5 +15,5 @@ broker_use_ssl = {
     "ssl_cert_reqs": ssl.CERT_NONE,
     "ssl_match_hostname": False,
     "ssl_check_hostname": False,
-    "ssl_sni": "master.transcription--legal-stuff--a96n-7tyr.addon.code.run",
+    "ssl_sni": "master.cache--j5zxzwppzvjs.addon.code.run",
 }

App/modelInit.py

@@ -1,18 +1,12 @@
 import databases
 import orm, asyncio
+import os
 
+dbLink = os.environ.get("DBLINK")
+print(dbLink)
 
-# deployment
-# database = databases.Database(
-#     "postgresql+asyncpg://postgres:PkkneZrSFsnJR6B@db.vfhoydxvxuesxhrcdnmx.supabase.co:5432/postgres"
-# )
-
-
-# development
-# dbLink = "postgresql+asyncpg://postgres:zJAkapOh3IVt8R0h@db.mygpwobpwxttmilnxrtu.supabase.co:5432/postgres"
-# dbLink = "postgresql+asyncpg://postgres:PkkneZrSFsnJR6B@db.vfhoydxvxuesxhrcdnmx.supabase.co:5432/postgres"
-dbLink = "mysql+aiomysql://i9xlhjk0gkor62rstpq3:pscale_pw_sIK2kegLrSMnWl3VtDuU8XTdYXxbu8jrnQoHVTnbpl3@aws.connect.psdb.cloud/transcription?ssl=true"
-
+for key, value in os.environ.items():
+    print(f"{key}: {value}")
 database = databases.Database(dbLink)
 
 

requirements.txt

@@ -21,6 +21,7 @@ gunicorn
 ujson
 yt-dlp
 psutil
+
 orm[mysql]
 celery
 python-multipart
@@ -30,4 +31,11 @@ telethon
 fastapi-jwt-auth
 bcrypt
 asyncmy
-aiomysql
+aiomysql
+pymongo
+sentence_transformers
+motor
+dnspython
+openai
+tiktoken
+langchain

sample.txt

@@ -0,0 +1,1598 @@
+{"end":0.77,"text":" This","start":0.11}
+{"end":1.11,"text":" video","start":0.77}
+{"end":1.41,"text":" was","start":1.11}
+{"end":1.61,"text":" brought","start":1.41}
+{"end":1.83,"text":" to","start":1.61}
+{"end":2.03,"text":" you","start":1.83}
+{"end":2.17,"text":" by","start":2.03}
+{"end":2.51,"text":" Ground","start":2.17}
+{"end":3.19,"text":" News.","start":2.51}
+{"end":3.91,"text":" Towards","start":3.75}
+{"end":4.09,"text":" the","start":3.91}
+{"end":4.17,"text":" end","start":4.09}
+{"end":4.25,"text":" of","start":4.17}
+{"end":4.45,"text":" last","start":4.25}
+{"end":4.83,"text":" week,","start":4.45}
+{"end":5.13,"text":" US","start":4.93}
+{"end":5.55,"text":" President","start":5.13}
+{"end":5.85,"text":" Joe","start":5.55}
+{"end":6.25,"text":" Biden","start":5.85}
+{"end":6.65,"text":" hosted","start":6.25}
+{"end":7.09,"text":" his","start":6.65}
+{"end":7.51,"text":" Japanese","start":7.09}
+{"end":8.07,"text":" and","start":7.51}
+{"end":8.31,"text":" South","start":8.07}
+{"end":8.71,"text":" Korean","start":8.31}
+{"end":9.39,"text":" counterparts","start":8.71}
+{"end":9.91,"text":" for","start":9.39}
+{"end":10.07,"text":" a","start":9.91}
+{"end":10.51,"text":" landmark","start":10.07}
+{"end":11.11,"text":" summit,","start":10.51}
+{"end":11.51,"text":" in","start":11.37}
+{"end":11.71,"text":" which","start":11.51}
+{"end":11.93,"text":" the","start":11.71}
+{"end":12.11,"text":" two","start":11.93}
+{"end":12.43,"text":" Asian","start":12.11}
+{"end":12.93,"text":" countries","start":12.43}
+{"end":13.47,"text":" appeared","start":12.93}
+{"end":13.67,"text":" to","start":13.47}
+{"end":13.97,"text":" put","start":13.67}
+{"end":14.41,"text":" decades","start":13.97}
+{"end":14.75,"text":" of","start":14.41}
+{"end":15.07,"text":" difficult","start":14.75}
+{"end":15.61,"text":" relations","start":15.07}
+{"end":16.07,"text":" behind","start":15.61}
+{"end":16.41,"text":" them.","start":16.07}
+{"end":16.77,"text":" In","start":16.75}
+{"end":17.11,"text":" favor","start":16.77}
+{"end":17.59,"text":" of","start":17.11}
+{"end":17.89,"text":" greater","start":17.59}
+{"end":18.51,"text":" cooperation","start":17.89}
+{"end":19.43,"text":" in","start":18.51}
+{"end":19.51,"text":" the","start":19.43}
+{"end":19.81,"text":" face","start":19.51}
+{"end":20.01,"text":" of","start":19.81}
+{"end":20.19,"text":" a","start":20.01}
+{"end":20.37,"text":" growing","start":20.19}
+{"end":20.87,"text":" security","start":20.37}
+{"end":21.41,"text":" challenge","start":20.87}
+{"end":21.83,"text":" posed","start":21.41}
+{"end":22.11,"text":" by","start":21.83}
+{"end":22.41,"text":" both","start":22.11}
+{"end":22.83,"text":" China","start":22.41}
+{"end":23.31,"text":" and","start":22.83}
+{"end":23.57,"text":" North","start":23.31}
+{"end":23.95,"text":" Korea.","start":23.57}
+{"end":24.57,"text":" So","start":24.29}
+{"end":24.69,"text":" in","start":24.57}
+{"end":24.89,"text":" this","start":24.69}
+{"end":25.21,"text":" video","start":24.89}
+{"end":25.47,"text":" we'll","start":25.21}
+{"end":25.65,"text":" take","start":25.47}
+{"end":25.79,"text":" you","start":25.65}
+{"end":25.99,"text":" through","start":25.79}
+{"end":26.23,"text":" the","start":25.99}
+{"end":26.47,"text":" summit","start":26.23}
+{"end":26.81,"text":" and","start":26.47}
+{"end":26.89,"text":" what","start":26.81}
+{"end":27.11,"text":" was","start":26.89}
+{"end":27.49,"text":" agreed,","start":27.11}
+{"end":28.17,"text":" explain","start":27.97}
+{"end":28.49,"text":" its","start":28.17}
+{"end":29.03,"text":" significance","start":28.49}
+{"end":29.83,"text":" and","start":29.03}
+{"end":30.31,"text":" consider","start":29.83}
+{"end":30.63,"text":" whether","start":30.31}
+{"end":31.11,"text":" Japan","start":30.63}
+{"end":31.39,"text":" and","start":31.11}
+{"end":31.59,"text":" South","start":31.39}
+{"end":32.27,"text":" Korea's","start":31.59}
+{"end":32.63,"text":" improved","start":32.27}
+{"end":33.35,"text":" relations","start":32.63}
+{"end":34.51,"text":" are","start":33.35}
+{"end":34.67,"text":" actually","start":34.51}
+{"end":35.15,"text":" likely","start":34.67}
+{"end":35.49,"text":" to","start":35.15}
+{"end":35.87,"text":" last.","start":35.49}
+{"end":41.76,"text":" Now","start":41.6}
+{"end":42.08,"text":" before","start":41.76}
+{"end":42.28,"text":" we","start":42.08}
+{"end":42.56,"text":" delve","start":42.28}
+{"end":42.72,"text":" into","start":42.56}
+{"end":43.04,"text":" the","start":42.72}
+{"end":43.26,"text":" summit","start":43.04}
+{"end":43.56,"text":" and","start":43.26}
+{"end":43.76,"text":" the","start":43.56}
+{"end":44.02,"text":" subsequent","start":43.76}
+{"end":44.68,"text":" statements,","start":44.02}
+{"end":45.18,"text":" it's","start":45.04}
+{"end":45.48,"text":" important","start":45.18}
+{"end":45.76,"text":" to","start":45.48}
+{"end":45.96,"text":" note","start":45.76}
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