Created using Colab

Author: OneFineStarstuff

agi_pipeline.ipynb

@@ -5,7 +5,7 @@
     "colab": {
       "private_outputs": true,
       "provenance": [],
-      "authorship_tag": "ABX9TyNk2WJFhV2VGr1cGsUqJ/ai",
+      "authorship_tag": "ABX9TyP6x+LjGFpdmRf+qQwoCRrX",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -31,35 +31,51 @@
       "cell_type": "code",
       "source": [
         "import os\n",
-        "import asyncio\n",
+        "import json\n",
         "import torch\n",
+        "import asyncio\n",
         "from typing import List\n",
         "from PIL import Image\n",
         "from fastapi import FastAPI, UploadFile, Depends, HTTPException\n",
         "from fastapi.security import OAuth2PasswordBearer\n",
-        "from pydantic import BaseModel, SecretStr\n",
+        "from pydantic import BaseModel\n",
+        "import jwt\n",
         "import pyttsx3\n",
         "from loguru import logger\n",
         "import io\n",
         "import uvicorn\n",
-        "import nest_asyncio\n",
+        "import signal\n",
+        "import sys\n",
         "from transformers import T5Tokenizer, T5ForConditionalGeneration\n",
         "from ultralytics import YOLO\n",
         "import whisper\n",
         "\n",
+        "# === Load Configuration ===\n",
+        "with open('config.json', 'r') as f:\n",
+        "    config = json.load(f)\n",
+        "\n",
         "# === Configuration and Logging Setup ===\n",
         "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-        "logger.add(\"pipeline_{time}.log\", rotation=\"1 MB\", level=\"DEBUG\", enqueue=True, backtrace=True, diagnose=True)\n",
+        "logger.add(config[\"logging\"][\"log_file\"], rotation=config[\"logging\"][\"rotation\"], level=config[\"logging\"][\"level\"], enqueue=config[\"logging\"][\"enqueue\"], backtrace=config[\"logging\"][\"backtrace\"], diagnose=config[\"logging\"][\"diagnose\"])\n",
         "logger.info(\"Application startup\")\n",
         "\n",
         "# === Security Setup ===\n",
-        "SECURE_TOKEN = SecretStr(os.getenv(\"SECURE_TOKEN\", \"YvZz9Hni0hWJPh_UWW4dQYf9rhIe9nNYcC5ZQTTZz0Q\"))\n",
+        "SECRET_KEY = os.getenv(\"SECRET_KEY\", config[\"security\"][\"secret_key\"])\n",
+        "ALGORITHM = config[\"security\"][\"algorithm\"]\n",
         "oauth2_scheme = OAuth2PasswordBearer(tokenUrl=\"token\")\n",
         "\n",
+        "def create_access_token(data: dict):\n",
+        "    to_encode = data.copy()\n",
+        "    encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)\n",
+        "    return encoded_jwt\n",
+        "\n",
         "def authenticate_user(token: str = Depends(oauth2_scheme)):\n",
-        "    if token != SECURE_TOKEN.get_secret_value():\n",
+        "    try:\n",
+        "        payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])\n",
+        "    except jwt.PyJWTError:\n",
         "        logger.warning(\"Authentication failed.\")\n",
         "        raise HTTPException(status_code=401, detail=\"Invalid token\")\n",
+        "    return payload\n",
         "\n",
         "# === Pydantic Models ===\n",
         "class TextRequest(BaseModel):\n",
@@ -71,7 +87,7 @@
         "# === NLP Module (T5 Transformer) ===\n",
         "class NLPModule:\n",
         "    def __init__(self):\n",
-        "        model_name = \"google/flan-t5-small\"\n",
+        "        model_name = config[\"nlp_model\"][\"model_name\"]\n",
         "        self.tokenizer = T5Tokenizer.from_pretrained(model_name)\n",
         "        self.model = T5ForConditionalGeneration.from_pretrained(model_name)\n",
         "        logger.info(\"NLP model loaded successfully.\")\n",
@@ -90,7 +106,8 @@
         "# === CV Module (YOLOv8 for Object Detection) ===\n",
         "class CVModule:\n",
         "    def __init__(self):\n",
-        "        self.model = YOLO('yolov8n.pt').to(device)\n",
+        "        model_path = config[\"cv_model\"][\"model_path\"]\n",
+        "        self.model = YOLO(model_path).to(device)\n",
         "        logger.info(\"CV model loaded successfully.\")\n",
         "\n",
         "    def detect_objects(self, image: Image.Image) -> str:\n",
@@ -101,7 +118,8 @@
         "# === Speech Processor (Whisper for Speech-to-Text, PyTTSX3 for Text-to-Speech) ===\n",
         "class SpeechProcessor:\n",
         "    def __init__(self):\n",
-        "        self.whisper_model = whisper.load_model(\"base\")\n",
+        "        whisper_model = config[\"speech_processor\"][\"whisper_model\"]\n",
+        "        self.whisper_model = whisper.load_model(whisper_model)\n",
         "        self.tts = pyttsx3.init()\n",
         "        logger.info(\"Speech processor initialized successfully.\")\n",
         "\n",
@@ -143,6 +161,14 @@
         "\n",
         "pipeline = EnhancedAGIPipeline()\n",
         "\n",
+        "# === Graceful Shutdown ===\n",
+        "def shutdown_signal_handler(sig, frame):\n",
+        "    print('Shutting down gracefully...')\n",
+        "    sys.exit(0)\n",
+        "\n",
+        "signal.signal(signal.SIGINT, shutdown_signal_handler)\n",
+        "signal.signal(signal.SIGTERM, shutdown_signal_handler)\n",
+        "\n",
         "# === Endpoints ===\n",
         "@app.post(\"/process-nlp/\", response_model=TextResponse, dependencies=[Depends(authenticate_user)])\n",
         "async def process_nlp(request: TextRequest):\n",
@@ -157,11 +183,8 @@
         "\n",
         "@app.post(\"/batch-cv-detection/\", dependencies=[Depends(authenticate_user)])\n",
         "async def batch_cv_detection(files: List[UploadFile]):\n",
-        "    responses = []\n",
-        "    for file in files:\n",
-        "        image = Image.open(io.BytesIO(await file.read()))\n",
-        "        response = await pipeline.process_cv(image)\n",
-        "        responses.append(response)\n",
+        "    tasks = [pipeline.process_cv(Image.open(io.BytesIO(await file.read()))) for file in files]\n",
+        "    responses = await asyncio.gather(*tasks)\n",
         "    return {\"batch_detections\": responses}\n",
         "\n",
         "@app.post(\"/speech-to-text/\", response_model=TextResponse, dependencies=[Depends(authenticate_user)])\n",
@@ -174,9 +197,8 @@
         "    await pipeline.process_text_to_speech(request.text)\n",
         "    return {\"response\": \"Speech synthesis complete.\"}\n",
         "\n",
-        "# === Run the Application with HTTPS (uvicorn) ===\n",
+        "# === Run the Application ===\n",
         "if __name__ == \"__main__\":\n",
-        "    nest_asyncio.apply()\n",
         "    uvicorn.run(app, host=\"0.0.0.0\", port=8000)"
       ],
       "metadata": {