-
โฆฟ labs
-
-
-
- | File Name |
- Summary |
-
-
-
- | Untitled.ipynb |
- - Create necessary project directories and files if they do not exist within the specified structure
- This code segment ensures the presence of essential backend folders and files for the project to function correctly. |
+ - Launches the application using Gunicorn with specified configurations, such as the number of workers, timeout, and port
- The script activates the virtual environment and starts the server to handle incoming requests. |
@@ -220,11 +196,11 @@ This project simplifies the deployment and management of AI applications. The co
| app.py |
- Implement a RESTful API endpoint in src\app.py to handle user authentication for the project. |
+ - Define exception handlers and mount static files for the FastAPI app using the provided code
- Handle validation errors and custom exceptions, logging details and returning appropriate responses
- Serve static files from the specified directory for the frontend. |
| cworker.py |
- - Implement a concurrent worker system to enhance performance and scalability
- This code file in src\cworker.py manages worker threads efficiently within the project structure. |
+ - Improve concurrency by managing worker threads efficiently
- This code file in src\cworker.py orchestrates thread creation and execution within the projects architecture. |
@@ -242,11 +218,11 @@ This project simplifies the deployment and management of AI applications. The co
| logger.py |
- Capture and store application logs efficiently to enhance monitoring and debugging capabilities within the backend architecture. |
+ - Implement a custom logger using Loguru for efficient logging in the backend
- The logger supports various log levels and customization options, enhancing the logging experience
- It includes features like log rotation, retention, and different log formats
- The code ensures robust logging functionality for the project. |
| utils.py |
- Enhances backend functionality by providing utility functions for the project. |
+ - Define utility functions to retrieve database URLs based on environment and fetch values from environment variables or configuration files
- The get_db_url function constructs a database URL for different environments, while get_val retrieves values with fallback options
- These functions enhance flexibility and maintainability in managing configurations and environment variables. |
@@ -264,19 +240,23 @@ This project simplifies the deployment and management of AI applications. The co
| consts.py |
- Define and centralize core constants for the backend architecture. |
+ - Define the AI_MODEL and default_system_prompt for the GPT-4o AI assistant in the consts.py file
- The AI_MODEL specifies the model used, while the default_system_prompt outlines the AIs function of generating SWOT analyses. |
| core.py |
- Implement core functionality for backend services, facilitating seamless communication and data processing within the project architecture. |
+ - Define a SQLModel and Agent Dependencies for SWOT Analysis, creating a SwotAgent with specified parameters for AI model, system prompt, and retries
- This code file in the core module plays a crucial role in structuring and managing SWOT analysis responses within the projects architecture. |
| main.py |
- - Implement core functionality for backend operations in the main.py file within the src\backend\core directory
- This code serves as the foundation for the entire projects backend architecture, handling essential operations and logic to ensure smooth functioning of the system. |
+ Implement core functionality for backend services in the main.py file. |
+
+
+ | tools.py |
+ - Describe how the tools.py file in src\backend\core facilitates fetching website content, analyzing competition using the Gemini model, and obtaining insights from Reddit
- The functions within this file leverage various libraries and APIs to perform these tasks efficiently. |
| utils.py |
- - Enhance backend functionality by providing essential utility functions
- This code file in the core directory plays a crucial role in supporting various backend operations
- It offers a collection of utility functions that streamline common tasks and enhance the overall efficiency of the backend system. |
+ - Define a function to report tool usage and results within the projects core architecture
- The function checks and updates tool usage history, notifying an update function if available
- This contributes to tracking tool usage and ensuring status updates are communicated effectively. |
@@ -294,6 +274,10 @@ This project simplifies the deployment and management of AI applications. The co
Summary |
+
+ | base.py |
+ - Generate base SQLModel class for project, addressing Pydantic V2.5 issue with __pydantic_extra__ attribute
- Inherits from SQLModel and sets necessary attributes
- Integrated with project metadata and configured for eager defaults. |
+
| consts.py |
Define database constants for backend operations in the project structure. |
@@ -302,13 +286,17 @@ This project simplifies the deployment and management of AI applications. The co
core.py |
Manage core database operations for the backend system, ensuring efficient data handling and storage. |
+
+ | db.py |
+ - Create and manage async SQLA sessions, handle database operations, and ensure database schema creation
- Includes functions for checking database existence, creating databases, and setting the current schema
- Provides utilities for creating database engines and managing database connections
- Mainly focuses on database setup and interaction for the project. |
+
| main.py |
Implement database connection and query functions to manage data persistence for the backend services. |
| utils.py |
- - Enhances database operations by providing utility functions
- Facilitates efficient data management within the backend architecture. |
+ - Enhances database operations by providing utility functions
- Facilitates seamless interaction with the database layer. |
@@ -336,11 +324,15 @@ This project simplifies the deployment and management of AI applications. The co
| main.py |
- Implement server-side logic to handle API requests and responses, serving as the core backend functionality for the project. |
+ - Implement a RESTful API server in Python to handle backend operations for the project
- The main.py file serves as the entry point for the server, orchestrating requests and responses
- It plays a crucial role in managing the communication between the frontend and backend components of the application. |
+
+
+ | router.py |
+ Define API routes and request handling logic for the backend server. |
| utils.py |
- - Enhances server functionality by providing utility functions for backend operations
- This code file in the project architecture streamlines server-side tasks, optimizing performance and facilitating seamless data processing. |
+ - Enhances server functionality by providing utility functions
- Improves codebase architecture by centralizing common operations
- Facilitates streamlined development and maintenance processes
- Promotes code reusability and efficiency within the backend server module. |
@@ -358,21 +350,37 @@ This project simplifies the deployment and management of AI applications. The co
Summary |
+
+ | backend_options.py |
+ - Generate SQL database connection URLs based on provided configurations for different environments using accepted dialects, including PostgreSQL and SQLite
- Import these configurations into the appropriate settings file to integrate with the Django Database settings. |
+
+
+ | base.py |
+ Define project settings including backend and frontend directories, database URL, and debug mode in the base settings file. |
+
| consts.py |
- Define and store constant values used throughout the backend settings module. |
+ Define database dialects and set secret key for project configuration. |
| core.py |
- - Manage core settings for the backend system, ensuring seamless configuration across modules
- This file serves as the central hub for defining and organizing key parameters that drive the functionality of the entire codebase
- It plays a crucial role in maintaining consistency and coherence in the projects architecture. |
+ - Enhances core settings functionality by providing centralized configuration management
- Facilitates seamless access and modification of key system parameters
- Improves maintainability and scalability of the project by consolidating settings logic. |
+
+
+ | dev.py |
+ Define development settings for backend with local database configuration and debugging enabled. |
| main.py |
- - Enhances backend settings functionality by managing configuration data
- Facilitates dynamic adjustments to settings without code changes
- Improves system flexibility and scalability. |
+ - Enhances backend settings functionality by managing configurations efficiently
- Facilitates seamless customization and optimization of settings across the codebase architecture. |
+
+
+ | prod.py |
+ - Define production settings for the backend, including database configuration and debugging options
- Extends base settings to inherit common configurations
- Centralizes cloud database settings for easy management and maintenance. |
| utils.py |
- - Enhances backend functionality by providing utility functions for settings management
- Facilitates seamless configuration handling within the project architecture. |
+ - Enhance backend settings functionality by providing utility functions for the codebase architecture
- The utils.py file in the src\backend\settings directory plays a crucial role in enabling streamlined operations and improved performance within the project structure. |
@@ -392,7 +400,7 @@ This project simplifies the deployment and management of AI applications. The co
| consts.py |
- Define and store constant values used throughout the backend site architecture. |
+ - Define constants and data structures for tracking analysis progress and results in the backend of the site architecture
- Includes messages for analysis status, sets for running tasks, and dictionaries for storing status and results. |
| core.py |
@@ -400,11 +408,15 @@ This project simplifies the deployment and management of AI applications. The co
| main.py |
- - Implement a RESTful API endpoint for handling site data in the backend architecture
- This code file serves as the entry point for site-related functionalities, facilitating communication between the frontend and backend systems. |
+ Improve site performance by caching API responses in the main.py file. |
+
+
+ | router.py |
+ - Implement a backend API router for a SWOT analysis tool
- The router handles URL analysis requests, updates status messages, and provides analysis results
- It manages session IDs, progress tracking, and result storage
- The code integrates with FastAPI, Starlette, and Jinja2 for web functionality. |
| utils.py |
- Enhance backend functionality by providing utility functions for the site. |
+ - Enhances backend functionality by providing utility functions for the site
- This code file in the backend architecture aids in streamlining operations and improving overall performance. |
@@ -434,7 +446,7 @@ This project simplifies the deployment and management of AI applications. The co
| start.sh |
- - Initiate and manage Celery workers, beat scheduler, and Flower for the projects asynchronous task processing
- Handles worker availability checks and starts necessary services for efficient task execution. |
+ - Initiates and manages Celery workers, beat scheduler, and Flower for the projects asynchronous task processing
- Handles worker availability checks and starts necessary services within the specified project directory. |
@@ -454,15 +466,15 @@ This project simplifies the deployment and management of AI applications. The co
| build.sh |
- - Install necessary dependencies for the ranked jobs microservice in the Docker container
- The script sets up essential tools like Python, PostgreSQL, and Git, ensuring a smooth environment for the microservice to run effectively within the architecture. |
+ - Install necessary dependencies for the ranked jobs microservice in the Docker container
- The script sets up essential tools like Python, PostgreSQL, and Git, ensuring a robust environment for the microservice to run smoothly. |
| python_build.sh |
- Generate and synchronize Python virtual environment and dependencies for ranked jobs microservice using UV. |
+ - Automates Python environment setup and dependency management for the ranked jobs microservice in the Docker architecture
- Sets up virtual environment, compiles requirements, and syncs dependencies for smooth execution. |
| python_start.sh |
- - Launches the Python application using Gunicorn with specified configurations for workers, timeouts, and ports
- The script activates the virtual environment and starts the server, ensuring optimal performance and accessibility for the ranked jobs microservice within the project architecture. |
+ - Launches the Python application using Gunicorn with specified configurations like the number of workers, timeout, and port
- The script activates the virtual environment and starts the server to handle incoming requests. |
@@ -508,6 +520,7 @@ Build Pygentic-AI from the source and intsall dependencies:
**Using [docker](https://www.docker.com/):**
+
```sh
โฏ docker build -t fsecada01/Pygentic-AI .
```
@@ -520,7 +533,7 @@ Build Pygentic-AI from the source and intsall dependencies:
**Using [pip](https://pypi.org/project/pip/):**
```sh
- โฏ pip install -r core_requirements.in dev_requirements.in
+ โฏ pip install -r core_requirements.in, core_requirements.txt, dev_requirements.in, dev_requirements.txt
```
diff --git a/core_requirements.txt b/core_requirements.txt
index 66a644d..57b9ff7 100644
--- a/core_requirements.txt
+++ b/core_requirements.txt
@@ -166,9 +166,9 @@ jsonpath-python==1.0.6
# via mistralai
kombu==5.4.2
# via celery
-logfire==3.1.1
+logfire==3.2.0
# via pydantic-ai-examples
-logfire-api==3.1.1
+logfire-api==3.2.0
# via pydantic-ai-slim
loguru==0.7.3
# via
diff --git a/dev_requirements.txt b/dev_requirements.txt
index 2b9c831..1e2355d 100644
--- a/dev_requirements.txt
+++ b/dev_requirements.txt
@@ -300,7 +300,7 @@ pyzmq==26.2.0
# ipykernel
# jupyter-client
# jupyter-server
-referencing==0.36.0
+referencing==0.36.1
# via
# jsonschema
# jsonschema-specifications
diff --git a/pyproject.toml b/pyproject.toml
index 72b4fff..1483bd4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -129,8 +129,8 @@ dependencies = [
"jiter==0.8.2",
"jsonpath-python==1.0.6",
"kombu==5.4.2",
- "logfire-api==3.1.1",
- "logfire==3.1.1",
+ "logfire-api==3.2.0",
+ "logfire==3.2.0",
"loguru==0.7.3",
"lxml-html-clean==0.4.1",
"lxml==5.3.0",
@@ -309,7 +309,7 @@ dev = [
"pywinpty==2.0.14",
"pyyaml==6.0.2",
"pyzmq==26.2.0",
- "referencing==0.36.0",
+ "referencing==0.36.1",
"requests==2.32.3",
"rfc3339-validator==0.1.4",
"rfc3986-validator==0.1.1",
diff --git a/src/app.py b/src/app.py
index c6180a5..a32e05b 100644
--- a/src/app.py
+++ b/src/app.py
@@ -3,12 +3,15 @@ import os
from fastapi import Request
from fastapi.exceptions import RequestValidationError
from starlette import status
-from starlette.responses import JSONResponse
+from starlette.middleware.sessions import SessionMiddleware
+from starlette.responses import HTMLResponse, JSONResponse
from starlette.staticfiles import StaticFiles
from backend import create_app
from backend.logger import logger
from backend.settings import app_settings, debug_arg
+from backend.site.router import templates, user_frontend
+from backend.utils import get_val
app = create_app(debug=debug_arg, settings_obj=app_settings)
@@ -19,6 +22,14 @@ app = create_app(debug=debug_arg, settings_obj=app_settings)
async def validation_exception_handler(
request: Request, exc: RequestValidationError
):
+ """
+ Custom validation error messaging for end-users. This reduces ambiguity
+ regarding the location of errors when validating request model objects.
+
+ :param request: Request
+ :param exc: RequestValidationError
+ :return: JSONResponse
+ """
exc_str = f"{exc}".replace("\n", "; ").replace(" ", " ")
logger.error(f"{request}: {exc_str}")
content = {"status_code": 10422, "message": exc_str, "data": None}
@@ -29,12 +40,25 @@ async def validation_exception_handler(
class UnicornException(Exception):
+ """
+ Inherited from Exception to provide the proper name for the error being
+ raised.
+ """
+
def __init__(self, name: str):
self.name = name
@app.exception_handler(UnicornException)
async def unicorn_exception_handler(request: Request, exc: UnicornException):
+ """
+ Returns a JSON response with a 418 response code instead of a default
+ 500. This provides some greater information for APIs/end-users.
+
+ :param request: Request
+ :param exc: UnicornException
+ :return: JSONResponse
+ """
return JSONResponse(
status_code=418,
content={
@@ -49,3 +73,66 @@ app.mount(
StaticFiles(directory=os.path.join(app_settings.frontend_dir, "static")),
name="static",
)
+
+app.add_middleware(
+ SessionMiddleware,
+ secret_key=get_val("SECRET_KEY"),
+ max_age=get_val("MAX_AGE", 3600),
+ same_site="lax",
+ https_only=get_val("HTTPS_ONLY", False),
+)
+
+app.include_router(user_frontend)
+
+
+@app.get("/", response_class=HTMLResponse)
+async def home_page(request: Request) -> HTMLResponse:
+ """
+ default homepage for the web application
+ :param request:
+ :return: HTMLResponse
+ """
+ return templates.TemplateResponse("home.html", {"request": request})
+
+
+if app_settings.DEBUG in (True, "True"):
+ from debug_toolbar.middleware import DebugToolbarMiddleware
+ from debug_toolbar.panels.sqlalchemy import SQLAlchemyPanel
+
+ from backend.db.db import engine
+
+ logger.debug(f"App Debug settings flag is {app_settings.DEBUG}")
+
+ class SQLAModelPanel(SQLAlchemyPanel):
+ """
+ Inheriting from SQLAlchemyPanel to include the sync engine object
+ from the SQLModel async engine instance.
+ """
+
+ async def add_engines(self, request: Request):
+ """
+ Adding SQLModel engine to middleware object.
+ :param request: Request
+ :return:
+ """
+ self.engines.add(engine.sync_engine)
+
+ app.add_middleware(
+ DebugToolbarMiddleware,
+ panels=["app.SQLModelPanel"],
+ disable_panels=["debug_toolbar.panels.profiling.ProfilingPanel"],
+ )
+
+
+if __name__ == "__main__":
+ import uvicorn
+
+ if debug_arg:
+ uvicorn.run("app:app", port=5000, reload=True)
+ else:
+ uvicorn.run(
+ "app:app",
+ host="0.0.0.0",
+ port=get_val("APP_PORT", 5000),
+ workers=get_val("WORKERS", 1),
+ )
diff --git a/src/backend/core/tools.py b/src/backend/core/tools.py
index 7b8df00..ab77518 100644
--- a/src/backend/core/tools.py
+++ b/src/backend/core/tools.py
@@ -3,7 +3,7 @@ from bs4 import BeautifulSoup as soup
from pydantic_ai import RunContext
from backend.core.consts import AI_MODEL
-from backend.core.core import SwotAgentDeps, swot_agent
+from backend.core.core import SwotAgentDeps, SwotAnalysis, swot_agent
from backend.core.utils import report_tool_usage
from backend.logger import logger
@@ -93,3 +93,32 @@ async def get_reddit_insights(
)
return "\n".join(insights)
+
+
+async def run_agent(
+ url: str, deps: SwotAgentDeps = SwotAgentDeps()
+) -> SwotAnalysis | Exception:
+ """
+ Runs the SWOT Analysis Agent
+
+ :param url: str
+ :param deps: SwotAgentDeps
+ :return: SwotAnalysis | Exception
+ """
+ try:
+ deps.tool_history = []
+ result = await swot_agent.run(
+ f"Perform a comprehensive SWOT analysis for this product: {url}",
+ deps=deps,
+ )
+ logger.info(f"Agent Result: {result}")
+
+ if deps.update_status_func:
+ await deps.update_status_func(deps.request, "Analysis Complete")
+ except Exception as e:
+ logger.exception(f"Error during agent run: {type(e), e, e.args}")
+
+ if deps.update_status_func:
+ await deps.update_status_func(deps.request, f"Error: {e}")
+
+ return e
diff --git a/src/backend/site/router.py b/src/backend/site/router.py
index df45376..9db3aa0 100644
--- a/src/backend/site/router.py
+++ b/src/backend/site/router.py
@@ -1,8 +1,5 @@
import asyncio
import os
-import random
-import time
-from typing import Any
from fastapi import APIRouter, Form, Request
from jinjax import Catalog, JinjaX
@@ -10,7 +7,6 @@ from starlette.responses import HTMLResponse
from starlette.staticfiles import StaticFiles
from starlette.templating import Jinja2Templates
-from backend.core.core import SwotAnalysis
from backend.logger import logger
from backend.settings import app_settings
from backend.site.consts import (
@@ -19,6 +15,7 @@ from backend.site.consts import (
result_store,
status_store,
)
+from backend.site.utils import run_agent_with_progress
user_frontend = APIRouter(prefix="", tags=["frontend"])
frontend = app_settings.frontend_dir
@@ -42,10 +39,6 @@ user_frontend.mount(
)
-def run_agent_with_progress(session_id, url):
- pass
-
-
@user_frontend.post("analyze", response_class=HTMLResponse)
async def analyze_url(request: Request, url: str = Form(...)) -> HTMLResponse:
"""
@@ -121,37 +114,3 @@ async def get_result(request: Request) -> HTMLResponse:
"result.html",
{"request": request, "result": result},
)
-
-
-def emulate_tool_completion(session_id: str, message: str) -> None:
- """Pydantic AI doesn't provide a post-processing hook, so we need to emulate one."""
-
- # Sleep a random amount of time between 0 and 5 seconds
- time.sleep(random.randint(0, 5))
- status_store[session_id].append(message)
-
-
-async def update_status(session_id: str, message: Any) -> None:
- """Updates status messages and handles SWOT analysis results."""
- logger.info(f"Updating status for session {session_id}: {message}")
-
- # Handle SWOT analysis result
- if isinstance(message, SwotAnalysis):
- result_store[session_id] = message.model_dump()
- status_store[session_id].append(ANALYSIS_COMPLETE_MESSAGE)
- return
-
- # Handle string messages
- if isinstance(message, str):
- # Instantly store first status message, emulate tool completion for others
- if message == ANALYSIS_COMPLETE_MESSAGE:
- status_store[session_id].append(message)
- else:
- loop = asyncio.get_running_loop()
- await loop.run_in_executor(
- None, emulate_tool_completion, session_id, message
- )
-
- logger.info(
- f"Status messages for session {session_id}: {status_store[session_id]}"
- )
diff --git a/src/backend/site/utils.py b/src/backend/site/utils.py
index e69de29..5355bb4 100644
--- a/src/backend/site/utils.py
+++ b/src/backend/site/utils.py
@@ -0,0 +1,84 @@
+import asyncio
+import random
+import time
+from typing import Any
+
+from loguru import logger
+
+from backend.core.core import SwotAgentDeps, SwotAnalysis
+from backend.core.tools import run_agent
+from backend.site.consts import (
+ ANALYSIS_COMPLETE_MESSAGE,
+ result_store,
+ status_store,
+)
+
+
+def emulate_tool_completion(session_id: str, message: str) -> None:
+ """Pydantic AI doesn't provide a post-processing hook, so we need to emulate one."""
+
+ # Sleep a random amount of time between 0 and 5 seconds
+ time.sleep(random.randint(0, 5))
+ status_store[session_id].append(message)
+
+
+async def update_status(session_id: str, message: Any) -> None:
+ """
+ Updates status messages and handles SWOT analysis results.
+
+ :param session_id: str
+ :param message: Any
+ :return: None
+ """
+ logger.info(f"Updating status for session {session_id}: {message}")
+
+ # Handle SWOT analysis result
+ if isinstance(message, SwotAnalysis):
+ result_store[session_id] = message.model_dump()
+ status_store[session_id].append(ANALYSIS_COMPLETE_MESSAGE)
+ return
+
+ # Handle string messages
+ if isinstance(message, str):
+ # Instantly store first status message, emulate tool completion for others
+ if message == ANALYSIS_COMPLETE_MESSAGE:
+ status_store[session_id].append(message)
+ else:
+ loop = asyncio.get_running_loop()
+ await loop.run_in_executor(
+ None, emulate_tool_completion, session_id, message
+ )
+
+ logger.info(
+ f"Status messages for session {session_id}: {status_store[session_id]}"
+ )
+
+
+async def run_agent_with_progress(session_id, url):
+ """
+ This provides ongoing progress updates for a running agent. A custom deps
+ object is used to store the session_id value and then triggers the
+ `run_agent` function
+ :param session_id: str
+ :param url: str
+ :return: None
+ """
+ try:
+ deps = SwotAgentDeps(
+ request=None,
+ update_status_func=lambda request, msg: update_status(
+ session_id, msg
+ ),
+ )
+
+ result = await run_agent(url=url, deps=deps)
+
+ if not isinstance(result, Exception):
+ logger.info(f"Successfully analyzed URL: {url}")
+ result_store[session_id] = result
+ except Exception as e:
+ logger.error(
+ f"An unexpected error occurred. See here: " f"{type(e), e, e.args}"
+ )
+ await update_status(session_id, f"Unexpected error: {e}")
+ raise
diff --git a/uv.lock b/uv.lock
index 32b76da..97b92fc 100644
--- a/uv.lock
+++ b/uv.lock
@@ -326,8 +326,8 @@ requires-dist = [
{ name = "jiter", specifier = "==0.8.2" },
{ name = "jsonpath-python", specifier = "==1.0.6" },
{ name = "kombu", specifier = "==5.4.2" },
- { name = "logfire", specifier = "==3.1.1" },
- { name = "logfire-api", specifier = "==3.1.1" },
+ { name = "logfire", specifier = "==3.2.0" },
+ { name = "logfire-api", specifier = "==3.2.0" },
{ name = "loguru", specifier = "==0.7.3" },
{ name = "lxml", specifier = "==5.3.0" },
{ name = "lxml-html-clean", specifier = "==0.4.1" },
@@ -506,7 +506,7 @@ dev = [
{ name = "pywinpty", specifier = "==2.0.14" },
{ name = "pyyaml", specifier = "==6.0.2" },
{ name = "pyzmq", specifier = "==26.2.0" },
- { name = "referencing", specifier = "==0.36.0" },
+ { name = "referencing", specifier = "==0.36.1" },
{ name = "requests", specifier = "==2.32.3" },
{ name = "rfc3339-validator", specifier = "==0.1.4" },
{ name = "rfc3986-validator", specifier = "==0.1.1" },
@@ -1793,7 +1793,7 @@ wheels = [
[[package]]
name = "logfire"
-version = "3.1.1"
+version = "3.2.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "executing" },
@@ -1804,9 +1804,9 @@ dependencies = [
{ name = "rich" },
{ name = "typing-extensions" },
]
-sdist = { url = "https://files.pythonhosted.org/packages/bf/2f/396e9c3c95961645c440951d5730c73a3cfbfcef0df1dde2929e21279289/logfire-3.1.1.tar.gz", hash = "sha256:9c086dd3061a8847d2a6e19df9a9c2b08f914449acaff7eb0b7b75db56340f66", size = 261918 }
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[[package]]
@@ -46,16 +46,15 @@
## Overview
-**Pygentic-AI**
+Pygentic-AI simplifies project setup and backend operations for developers.
**Why Pygentic-AI?**
-This project simplifies the deployment and management of AI applications. The core features include:
+This project automates Python environment setup and dependency management, ensuring a smooth development workflow. The custom logger enhances backend logging efficiency, while the RESTful API server streamlines backend operations.
-- **๐ Orchestration:** Define deployment configurations with compose.yaml for seamless scalability.
-- **๐ป Automation:** Build Docker images effortlessly using Dockerfile for efficient environment setup.
-- **๐ง Streamlined Setup:** Manage project dependencies and setup with provided build scripts.
-- **๐ Backend Functionality:** Implement essential backend features like RESTful APIs and database operations.
+- **๐ Automated Python Setup:** Simplify environment configuration and dependency management.
+- **๐ก Custom Logger:** Efficient logging with customizable features for backend operations.
+- **๐ RESTful API Server:** Facilitate seamless backend operations with a robust API server.
---
@@ -63,15 +62,15 @@ This project simplifies the deployment and management of AI applications. The co
| | Component | Details |
| :--- | :-------------- | :----------------------------------- |
-| โ๏ธ | **Architecture** |