VFX Pulling Automation Script

A Python script designed to automate the process of pulling VFX assets from specified file directories. The script identifies and processes assets in EXR format, logs errors, sends notifications, and integrates with Airtable for tracking pulled resources.

---

Features

• Automated Asset Pulling: Processes multiple directories to locate and copy VFX assets in .exr format.
• Error Logging: Logs issues encountered during execution into a log file (vfx_pull.log).
• Notifications: Sends email notifications upon task completion or when an error occurs.
• Airtable Integration: Logs pulled files and directory statuses into Airtable for seamless tracking.
• Configurable Directories: Supports multiple source directories for flexibility.

---

Prerequisites

Ensure you have the following installed:

• Python 3.8+
• Required Python libraries:

  pip install airtable-python-wrapper
  

Additional Setup:

Airtable API Key and Table:
    Create an Airtable base to log pulled files.
    Generate an Airtable API key from your Airtable account (Airtable API Key Documentation).
SMTP Configuration:
    Ensure you have an SMTP server for sending email notifications.
    Obtain credentials and configuration details from your email provider.

Configuration

Edit the config dictionary in the script to match your setup:

config = { "directories": ["/path/to/dir1", "/path/to/dir2"], # Directories to search for EXR files "destination_dir": "/path/to/save/exr", # Destination folder for EXR files "airtable_api_key": "your_airtable_api_key", "airtable_base_id": "your_base_id", "airtable_table_name": "VFX Logs", "notification_email": "youremail@example.com", "smtp_server": "smtp.example.com", "smtp_port": 587, "smtp_username": "your_smtp_username", "smtp_password": "your_smtp_password", }

Usage

Clone the Repository:
Usage

Clone the Repository:

Run the Script: python vfx_automation.py

Logging

The script generates a vfx_pull.log file in the project directory. This file captures:

Processed files and directories.
Errors and warnings.
Script status updates.

Advanced Features Airtable Integration

The script uses the Airtable API to log:

Pulled asset filenames.
Source directories.
Directory statuses (e.g., missing).

Notifications

Email notifications are sent for:

Task completion.
Errors encountered during processing.

Example Output Log File (vfx_pull.log): 2024-11-16 10:00:00 - INFO - Starting VFX pulling script. 2024-11-16 10:01:15 - INFO - Copied /path/to/dir1/shot1.exr to /path/to/save/exr/shot1.exr 2024-11-16 10:02:00 - WARNING - Directory does not exist: /path/to/missing_dir 2024-11-16 10:05:00 - INFO - VFX pulling script completed.

Troubleshooting Missing SMTP Emails

Verify SMTP credentials and ensure the SMTP server allows programmatic access.

Airtable Issues

Check the API key, Base ID, and Table name in the config dictionary.

EXR Files Not Found

Ensure directories contain files with .exr extensions.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributions

Contributions are welcome! Feel free to submit a pull request or open an issue for suggestions or improvements.

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Social Media Engagement Analyzer

A Python-based application that fetches social media engagement data from platforms like Twitter and YouTube, performs sentiment analysis, predicts future engagement trends, visualizes the data interactively, and allows for scheduling, exporting, and CRM integration.

Features

• Data Fetching: Fetches social media engagement data from Twitter and YouTube using their respective APIs.
• Sentiment Analysis: Performs sentiment analysis on fetched comments using TextBlob.
• Predictive Analytics: Predicts future engagement trends using Linear Regression based on historical data.
• Visualization: Displays interactive charts and graphs using Plotly.
• Scheduling & Automation: Automatically fetches and updates engagement data at specified intervals using the schedule library.
• Data Export: Allows exporting data to Excel (.xlsx) and JSON formats.
• CRM Integration: Sends engagement data to a CRM system for lead processing and follow-ups.
• GUI: Provides a simple graphical interface for running analysis and exporting data using CustomTkinter.

Requirements

Before running the application, ensure you have the following dependencies installed:

• Python 3.x
• pip install -r requirements.txt

You will need to set up API keys for accessing Twitter and YouTube. The script loads these keys from environment variables for security.

Install Dependencies

To install the required libraries, run:

pip install -r requirements.txt

Where requirements.txt includes the following dependencies:

pandas
plotly
schedule
requests
textblob
sklearn
customtkinter
python-dotenv
google-api-python-client

Setup

    Environment Variables: Create a .env file in the project directory with the following content:

TWITTER_API_KEY=your_twitter_api_key
TWITTER_API_SECRET=your_twitter_api_secret
YOUTUBE_API_KEY=your_youtube_api_key

Replace your_twitter_api_key, your_twitter_api_secret, and your_youtube_api_key with your actual API keys.

    Run the Application: To launch the application, simply run the script:

python app.py

The app will open a GUI window where you can:

    Run Analysis: Fetch and analyze the data.
    Export Data: Export the data to Excel or JSON.

How it Works
Fetching Data

    Twitter: The application uses the Twitter API to fetch engagement data (likes, shares, comments) from your Twitter account or a public account.
    YouTube: It uses the YouTube API to fetch channel statistics such as views and engagement.

Sentiment Analysis

The comments from social media platforms are analyzed using TextBlob to determine the sentiment (positive, negative, or neutral).
Predictive Analytics

Linear regression is used to predict future engagement based on historical data of likes, shares, and comments.
Visualization

The data is visualized using Plotly, creating interactive graphs and charts that display engagement trends over time.
Scheduling

The application uses the schedule library to fetch and update data automatically at a specified time (e.g., every day at 10 AM).
Exporting Data

You can export the analyzed data into both Excel and JSON formats, which can be shared or further processed.
CRM Integration

The application sends the fetched engagement data to a CRM system for managing leads and follow-ups.
Example Output

When the app runs, you'll see output like the following:

Fetching Twitter data...
Fetching YouTube data...
Performing sentiment analysis...
Predicting engagement trends...
Visualizing data...
Job completed successfully.
Data exported to engagement_report.xlsx

An interactive visualization will open in your browser, showing a bar chart of engagement trends.
Screenshots

Example of the graphical user interface
Contributing

Feel free to fork this repository, submit issues, and send pull requests! All contributions are welcome.
License

This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgements

    Twitter API: Used to fetch engagement data from Twitter.
    YouTube API: Used to fetch statistics from YouTube.
    TextBlob: Used for sentiment analysis of comments.
    Plotly: Used for creating interactive visualizations.
    Scikit-learn: Used for predictive modeling with linear regression.
    CustomTkinter: Used for the GUI.

Contact

For questions or suggestions, feel free to open an issue or contact me directly at your_email@example.com.


### Key Sections:
- **Features**: Describes the main functionalities of the project.
- **Requirements**: Lists the necessary dependencies and provides installation instructions.
- **Setup**: Guides users through setting up environment variables and running the application.
- **How it Works**: Explains the different steps the application performs.
- **Example Output**: Shows what the user can expect from running the script.
- **Contributing**: Encourages others to contribute to the project.
- **License**: Mentions the licensing information.

Feel free to customize this template further with actual screenshots, links, or additio

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Image Annotation and Object Detection

This project demonstrates the process of image annotation and object detection using Python, TensorFlow, and OpenCV. The primary objective is to teach and automate object detection with a pre-trained YOLO model and also to provide guidance on manual image annotation using the LabelImg tool.
Table of Contents

    Introduction
    Features
    Installation
    Usage
    Image Annotation
    Object Detection
    Contributing
    License

Introduction

This repository contains Python code that:

    Annotates images manually using the LabelImg tool.
    Performs object detection using TensorFlow’s pre-trained YOLO model.

The project aims to help users understand and work with image annotation and object detection techniques in computer vision.
Features

    Image Annotation: Annotate images using the LabelImg tool.
    Object Detection: Detect objects in images using TensorFlow's YOLO model.
    Visualization: Visualize detected objects with bounding boxes and labels.
    Pre-trained Model: Leverages a pre-trained YOLO model for object detection.

Installation
Clone the repository

bash

git clone https://github.com/your-username/image-annotation-object-detection.git
cd image-annotation-object-detection

Install dependencies

You need Python 3.6+ and the following dependencies. You can install them using pip:

bash

pip install tensorflow opencv-python pillow matplotlib

Install LabelImg for image annotation

LabelImg is a tool for manually annotating images. You can install it using pip:

bash

pip install labelImg

Running LabelImg

bash

labelImg

Use LabelImg to annotate your images and save the annotations.
Usage
Object Detection

    Load Images: The code supports loading images in .jpg or .png format.
    Object Detection: You can perform object detection on your images using the following command:

python

python object_detection.py --image path_to_your_image.jpg

Running the Object Detection Code

    Replace "path_to_your_image.jpg" in the sample code with the path to your own image.
    Run the script to see detected objects in your image.

Image Annotation

To annotate images, use the LabelImg tool:

bash

labelImg

Save your annotations in XML format for future training or further processing.

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Video Processing Tool with AI-Based Object Removal

This Python project processes video files to remove unwanted objects such as captions, emojis, or images. It leverages AI and image inpainting techniques to provide a clean, high-quality output video.
Features

    Object Detection: Automatically detects unwanted elements (e.g., captions, emojis) using object detection techniques.
    Object Removal: Uses DeepFill or OpenCV Inpainting to restore the video by filling in the removed areas.
    Video Quality Enhancement: Applies denoising filters to improve the overall quality of the video.
    Parallel Processing: Speeds up the processing of video frames using Python's multiprocessing module.

Table of Contents

    Requirements
    Installation
    Usage
    Project Structure
    How it Works
    Contributing
    License

Requirements

    Python 3.7+
    Libraries:
        opencv-python
        moviepy
        deepfill (or equivalent inpainting method)
        numpy
        multiprocessing

Installation

    Clone the repository:

    bash

git clone https://github.com/your-username/video-processing-tool.git
cd video-processing-tool

Install the dependencies:

bash

pip install opencv-python-headless moviepy deepfill

Verify that the required modules are installed correctly:

bash

    python -c "import cv2, moviepy, deepfill, numpy"

Usage

    Edit the input/output paths: Open the script and modify the input_video and output_video paths to point to your video files:

    python

input_video = 'input_video.mp4'
output_video = 'output_video.mp4'

Run the script: To process a video file, simply run the Python script:

bash

python video_processing.py

Optional Parallel Processing: If you want to speed up processing using multiple CPU cores, enable parallel processing by setting use_parallel_processing to True in the script:

python

    use_parallel_processing = True

    Output: The processed video will be saved as output_video.mp4 (or any file path you specify).

Project Structure

bash

video-processing-tool/
│
├── README.md              # Project documentation
├── video_processing.py    # Main Python script
├── input_video.mp4        # Sample input video (replace with your own)
└── output_video.mp4       # Output video after processing (generated)

How it Works

    Loading the Video: The script uses moviepy to load the video file and process each frame.

    Object Detection and Removal:
        A placeholder object detection function identifies areas in the video where unwanted objects (like captions, emojis, or images) are located.
        Inpainting is then applied to remove the detected objects and restore the video. The project uses DeepFill or OpenCV's cv2.inpaint for this task.

    Post-Processing: After object removal, a denoising filter is applied to enhance the overall quality of the video.

    Parallel Processing: The script can use Python’s multiprocessing.Pool to process frames in parallel, drastically reducing the processing time for large videos.

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# NASA GISTEMP Climate Score Prediction using Machine Learning

## Overview

This project predicts NASA's monthly GISTEMP climate score using machine learning algorithms and climate data from public sources. The predictions are updated daily using the latest available data and are accurate to six decimal places. The goal is to deliver the most accurate prediction for the monthly GISTEMP score.

## Features

- **Automated Data Collection**: Scrapes and gathers climate data from published sources daily.
- **Machine Learning Models**: Uses time series forecasting and regression models for prediction.
- **Real-Time Updates**: The model runs daily to provide up-to-date predictions based on the latest data.
- **Precision**: Predicts the GISTEMP score with six decimal place accuracy (e.g., 1.213805).
- **Final Submission**: The final prediction for the month is captured on the 27th at 8:00 PM EST.

## Project Structure

|-- data/ # Directory to store raw and processed data |-- models/ # Machine learning models and training scripts |-- logs/ # Logs of daily predictions and performance metrics |-- scripts/ | |-- data_scraper.py # Script for scraping climate data | |-- train_model.py # Script for training the machine learning model | |-- predict.py # Script for running daily predictions |-- README.md

markdown


## Getting Started

### Prerequisites

- Python 3.x
- Climate data sources (e.g., NOAA, NASA)
- API keys (if applicable for data sources)

### Installation

1. **Clone the repository**:

```bash
git clone https://github.com/yourusername/nasa-climate-prediction.git
cd nasa-climate-prediction

    Install dependencies:

bash

pip install -r requirements.txt

Running the Project

    Collect Data:

Run the data scraper to gather the most recent climate data:

bash

python scripts/data_scraper.py

    Train the Model:

Train the machine learning model with the collected data:

bash

python scripts/train_model.py

    Make Predictions:

Schedule the script to run daily for real-time predictions:

bash

python scripts/predict.py

Results

The final prediction for NASA's GISTEMP score for each month will be logged with six decimal places accuracy. Predictions are submitted privately for evaluation against NASA's official score.

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Motorcycle Real-Time Status and Fault Monitoring System

This project is designed to monitor real-time status indicators and fault warnings from a motorcycle's CAN Bus or OBD-II interface. It displays key parameters such as engine status, battery voltage, oil pressure, and more via a user-friendly graphical interface. The system logs critical faults and warnings for future reference.
Features

    Real-Time Monitoring: Retrieves real-time data from the motorcycle's CAN Bus or OBD-II interface.
    Fault Warnings: Displays alerts for critical warnings such as:
        Engine status fault
        ABS warning (if available)
        Oil Pressure (over threshold)
        Battery Voltage (under threshold)
        Fuel Warning (low fuel level)
        Tire Pressure (low pressure) (if available)
        Temperature Alarms (engine or coolant system)
        Turn Signal Activation/Deactivation
    Logging: Logs all warnings and critical statuses to a file (motorcycle_monitor.log) for later review.
    Graphical Interface: A simple Tkinter-based interface to display real-time data and warnings.

Requirements

    Python 3.x
    OBD-II or CAN Bus adapter (USB, Bluetooth, or other supported interfaces)

Python Libraries

    obd: For OBD-II communication
    python-can: For CAN Bus communication
    tkinter: For the GUI interface
    logging: For logging faults and warnings

Installation

    Clone the repository:

    bash

git clone https://github.com/yourusername/motorcycle-monitoring-system.git
cd motorcycle-monitoring-system

Install required Python packages: You can install the dependencies via pip:

bash

pip install obd python-can

Connect your hardware:

    Plug in your OBD-II or CAN Bus adapter to the motorcycle and your computer.
    For OBD-II Bluetooth adapters, ensure it is paired with your computer.

Adjust settings (if necessary):

    For OBD-II adapters, you may need to specify the correct port for your connection. Modify the following line in the code:

    python

obd_conn = obd.OBD(portstr='COM3')  # Replace COM3 with your actual port

For CAN Bus, ensure you are using the correct channel and bustype based on your hardware:

python

        can_bus = can.interface.Bus(channel='can0', bustype='socketcan')  # For Linux

Usage

    Run the script:

    bash

    python motorcycle_monitoring.py

    Monitor your motorcycle's status:
        The GUI will display real-time status indicators for engine, battery, oil pressure, and other available metrics.
        Any warnings will be highlighted in the GUI, and logs will be saved in motorcycle_monitor.log.

Example GUI

The real-time monitoring GUI will display:

    Engine status
    Battery voltage
    Oil pressure status
    (Additional indicators as available from the motorcycle)

Troubleshooting

    No OBD-II Adapters Found:
        Ensure the adapter is connected and recognized by your system.
        If using Bluetooth, make sure the device is paired and that the correct port is specified.

    CAN Bus Errors:
        Ensure the python-can library is installed.
        Verify the correct channel and bustype are used for your CAN Bus adapter.

---------------------------------------------------------------------------------------------

Marketplace Product Parser

This Python program allows users to scrape product details from one marketplace and upload them to another marketplace by simply providing a product link and category name. It automates the process of transferring product data between two marketplaces.
Features

    Scrapes product name, price, and description from the provided marketplace link.
    Allows user input for product link and category name.
    Automatically uploads the parsed product data to your marketplace.

Requirements

    Python 3.x
    requests library
    beautifulsoup4 library

Installation

    Clone the repository to your local machine:

    bash

git clone https://github.com/yourusername/marketplace-product-parser.git
cd marketplace-product-parser

Install the required Python libraries:

bash

    pip install -r requirements.txt

Usage

    Run the script:

    bash

python parser.py

Enter the product link and the category name when prompted:

bash

    Enter the product link: https://example.com/product/123
    Enter the category name: Electronics

    The program will fetch the product details and upload them to your marketplace.

Example

If you input:

    Product link: https://example.com/product/123
    Category name: Electronics

The program will scrape the product name, price, and description from the provided link and upload it under the Electronics category to your marketplace.

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# AI Healthcare App

## Overview

The AI Healthcare App is a comprehensive solution designed to assist patients in recording their doctor visits, receiving personalized AI-generated medication recommendations, and visualizing trends in symptoms over time. The application integrates both front-end and back-end components, providing a seamless user experience and robust functionality.

## Features

- **User Authentication:** Secure login system to manage patient access.
- **Symptom Input:** Allows users to enter symptoms and receive AI-generated medication recommendations.
- **Data Visualization:** Displays trends and patterns of symptoms over time using interactive graphs.
- **Language Localization:** Supports multiple languages with dynamic UI updates.
- **Voice Input (Optional):** (Commented out) Allows users to input symptoms via voice recognition.
- **Real-time Data Processing:** Backend API for handling recommendation requests and managing data.

## Technologies Used

- **Front-End:** Python, Tkinter for GUI
- **Back-End:** Flask for API development
- **Data Visualization:** Matplotlib
- **Voice Recognition:** SpeechRecognition (Optional)
- **API Requests:** Requests

## Installation

To get started with the AI Healthcare App, follow these installation steps:

1. **Clone the Repository**

   ```bash
   git clone https://github.com/yourusername/ai-healthcare-app.git
   cd ai-healthcare-app

    Install Dependencies

    Create a virtual environment (optional but recommended) and install the required Python packages:

    bash

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
pip install -r requirements.txt

If using voice recognition, you’ll also need to install additional packages:

bash

pip install SpeechRecognition pyaudio

Run the Application

Start the Flask server and the Tkinter application:

bash

    python app.py

Usage

    Login: Enter your username and password to access the application.
    Enter Symptom: Type your symptoms into the input field and click "Get Recommendation" to receive AI-generated medication recommendations.
    View Trends: Analyze symptom trends using the interactive graphs.
    Change Language: Select your preferred language from the dropdown menu for localized UI.

API Endpoints
/recommendation

    Method: POST

    Description: Receives symptom data and returns medication recommendations.

    Request Body:

    json

{
  "symptoms": "example symptoms"
}

Response:

json

    {
      "recommendation": "Recommended Medication for example symptoms: Example Medicine"
    }

UI Features

    User Authentication: Secure login screen with username and password fields.
    Symptom Entry: Text field for entering symptoms with a submit button.
    Data Visualization: Matplotlib-based graphs for visualizing symptom trends.
    Language Selection: Dropdown menu for changing the UI language.
    Voice Input (Optional): Button for recording symptoms via voice (currently commented out).

Troubleshooting

    ModuleNotFoundError: Ensure all dependencies are installed. Use pip install -r requirements.txt to install required libraries.
    Voice Recognition Issues: Verify microphone settings and ensure pyaudio is installed correctly.

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Stable Diffusion Bot
Overview

The Stable Diffusion Bot is a Python-based implementation that applies a stable diffusion algorithm to images. The bot processes incoming requests containing images, applies diffusion, and returns the processed images. This bot can be extended to handle more sophisticated diffusion algorithms and integrated into larger systems.
Features

    Stable Diffusion: Applies a Gaussian filter for diffusion effects.
    Request Handling: Fetches image data from a URL, processes it, and sends the results back to a specified URL.
    Modular Design: Easily customizable and extendable for different diffusion algorithms and additional functionality.

Requirements

    Python 3.x
    numpy
    scipy
    requests

Install the required packages using pip:

bash

pip install numpy scipy requests

Installation

    Clone the repository:

    bash

git clone https://github.com/your-username/stable-diffusion-bot.git
cd stable-diffusion-bot

Install dependencies:

bash

    pip install -r requirements.txt

Usage
Example Code

Here's an example of how to use the Stable Diffusion Bot:

python

import numpy as np

from stable_diffusion_bot import StableDiffusionBot

# Initialize the bot
bot = StableDiffusionBot()

# Example image (100x100 random values)
example_image = np.random.rand(100, 100).tolist()
request_data = {'image': example_image, 'result_url': 'http://example.com/result'}

# Process the image
response = bot.process_request(request_data)
print("Diffused Image:", response)

Handling Requests

To handle requests from a URL and respond with the processed image:

python

request_url = 'http://example.com/request'
bot.handle_request(request_url)

Implementation Details
StableDiffusionBot Class

    apply_diffusion: Applies a Gaussian filter to simulate the diffusion process.
    process_request: Processes the incoming image request, applies diffusion, and prepares the result.
    handle_request: Fetches request data from a URL, processes it, and posts the result to another URL.

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PDF to Excel/Google Sheets Parser

This project provides two different solutions for parsing PDF files and exporting the data to either an Excel spreadsheet (using Python) or directly to Google Sheets (using Google Apps Script).
Features

    PDF Parsing: Extracts text or specific fields from a collection of PDF files.
    Excel Output: Saves the parsed data into an Excel file using Python.
    Google Sheets Output: Saves the parsed data directly to a Google Sheets document using Google Apps Script.
    Folder Monitoring: The Python script checks a specified folder for new PDFs and processes them automatically.

Getting Started
Prerequisites

For the Python version:

    Python 3.x
    Required libraries: pandas, pdfplumber, openpyxl, os

For the Google Apps Script version:

    A Google Account
    Access to Google Drive and Google Sheets

Installation

    Clone the repository:

    bash

git clone https://github.com/your-username/pdf-parser.git
cd pdf-parser

Install the required Python libraries:

bash

    pip install pdfplumber pandas openpyxl

Usage
Python Version (Excel Output)

    Modify the scan_folder_and_save_to_excel() function to specify the folder path containing the PDFs and the desired output Excel file path.
    Run the Python script:

    bash

    python pdf_parser.py

    The script will scan the specified folder, parse the PDFs, and output the results to an Excel file.

Google Apps Script Version (Google Sheets Output)

    Create a new Google Sheet and open the Script Editor (Extensions > Apps Script).
    Copy and paste the Google Apps Script code into the editor.
    Modify the folder ID and sheet name in the script to match your environment.
    Set up a trigger in Google Apps Script to run the scanFolderAndSaveToSheet function automatically when new PDFs are added to the folder.

Python Example

Here's a snippet of the Python script that parses PDFs and saves the data to an Excel sheet:

python

import os
import pandas as pd
import pdfplumber

def parse_pdf(file_path):
    """Parse fields from PDF"""
    with pdfplumber.open(file_path) as pdf:
        first_page = pdf.pages[0]
        text = first_page.extract_text()
        # Custom logic to extract fields
        parsed_data = {
            'Field1': extract_field_1(text),
            'Field2': extract_field_2(text),
        }
    return parsed_data

def scan_folder_and_save_to_excel(folder_path, excel_path):
    parsed_results = []
    for filename in os.listdir(folder_path):
        if filename.endswith('.pdf'):
            file_path = os.path.join(folder_path, filename)
            parsed_results.append(parse_pdf(file_path))
    
    df = pd.DataFrame(parsed_results)
    df.to_excel(excel_path, index=False)

# Example Usage
scan_folder_and_save_to_excel('path/to/pdf/folder', 'output.xlsx')

Google Apps Script Example

Here is a snippet of the Google Apps Script for parsing PDFs and saving data to Google Sheets:

javascript

function parsePDF(fileId) {
  var file = DriveApp.getFileById(fileId);
  var blob = file.getBlob();
  // Custom logic to parse PDF text
  var text = PDFParserApp.parseText(blob);
  
  return [extractField1(text), extractField2(text)];
}

function scanFolderAndSaveToSheet() {
  var folder = DriveApp.getFolderById('folderId');
  var files = folder.getFilesByType(MimeType.PDF);
  var sheet = SpreadsheetApp.getActiveSpreadsheet().getSheetByName('PDF Data');
  var row = 1;
  
  while (files.hasNext()) {
    var file = files.next();
    var parsedData = parsePDF(file.getId());
    sheet.getRange(row, 1, 1, parsedData.length).setValues([parsedData]);
    row++;
  }
}

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Advanced Trading Application

This project is a comprehensive trading application written in Python that includes backtesting, real-time trading, a machine learning prediction model, performance tracking, a user interface, and API integration. The app is designed to support multiple users, providing risk management, stop-loss/take-profit capabilities, and more.
Features

    Backtesting Engine: Test trading strategies using historical data from Yahoo Finance with custom parameters for moving average crossovers, stop-loss, and take-profit.
    Real-Time Trading: Execute trades in real-time using the Alpaca API.
    Machine Learning Prediction: Use an LSTM model for predictive analytics based on historical price data.
    Performance Tracking and Reporting: Save backtesting results and track portfolio performance.
    Dash Web Interface: Interactive dashboard for visualizing trading strategies and portfolio performance.
    Flask REST API: RESTful API for external trade requests.
    Multi-User Support: Basic user management with SQLite for storing user credentials and settings.

Tech Stack

    Backtesting: Backtrader
    Market Data: Yahoo Finance and Alpaca API
    Machine Learning: TensorFlow for building LSTM models
    User Interface: Dash and Flask for the UI and REST API
    Database: SQLite for multi-user management

Getting Started
Prerequisites

    Python 3.7+
    Alpaca API Key for live trading
    Dependencies: see the requirements.txt section below.

Installation

    Clone the repository:

git clone https://github.com/yourusername/AdvancedTradingApp.git
cd AdvancedTradingApp

Install dependencies:

pip install -r requirements.txt

Set up your Alpaca API credentials:

    Replace the placeholder values in the script:

        api_key = "your_alpaca_api_key"
        api_secret = "your_alpaca_api_secret"

    Database Setup:
        The SQLite database will automatically set up user tables for multi-user functionality. Simply run the app, and it will create users.db.

Requirements

Create a requirements.txt file with these dependencies:

backtrader
yfinance
tensorflow
alpaca-trade-api
dash
flask
pandas

Project Structure

.
├── app.py                 # Main application script
├── requirements.txt       # Python dependencies
├── README.md              # Project README
├── users.db               # SQLite database for user management
├── backtest_report.json   # JSON file for backtesting reports
└── ...

Usage
Running the Application

    Flask API: Start the Flask server to enable REST API access.

python app.py

Dash Dashboard: View the Dash dashboard at http://127.0.0.1:8050/, where you can visualize strategy performance and portfolio data.

Backtesting: Run the backtesting feature to test strategies on historical data:

run_backtest()

Live Trading: Execute trades in real time by calling the trade_live(symbol) function with a stock ticker (e.g., "AAPL").

Machine Learning Model: Train an LSTM model for predictive analytics and use the predictions in your strategies:

    create_lstm_model()

Example Commands

    Execute backtesting:

run_backtest()

Place a real-time trade:

    trade_live("AAPL")

API Endpoints
Trade Endpoint

Make trade requests via the REST API:

POST /trade

Body:

{
  "symbol": "AAPL",
  "side": "buy",
  "quantity": 1
}

Response:

{
  "status": "order placed"
}

Troubleshooting

    Alpaca API Issues: Make sure to use the correct API base URL and credentials. Test with the paper trading environment if you're new to Alpaca.
    TensorFlow Compatibility: Ensure you have a compatible TensorFlow version for your Python environment.
    SQLite Database Lock: Avoid running the app multiple times simultaneously to prevent SQLite locks.

Future Enhancements

    Enhanced strategy customization
    Additional ML models (Random Forest, SVM)
    More interactive dashboard visualizations
    Expanded support for alternative data sources

License

MIT License

Customization

    You can customize the parsing logic based on the structure of your PDFs. Modify the extract_field_1 and extract_field_2 functions to match the fields you're extracting.
    For the Python version, you can use different libraries for Excel export like xlsxwriter or openpyxl depending on your needs.

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Task Automation Framework

This Python script enables task automation using services such as Twilio for SMS, SendGrid for email, and a placeholder function for data queries. Tasks are defined in a JSON configuration file, and the script executes them sequentially while logging progress and errors.
Features

    SMS Notifications: Sends SMS messages via Twilio.
    Email Notifications: Sends emails using SendGrid.
    Custom Data Queries: Placeholder function for future data integration.
    Task Sequencing: Executes tasks sequentially as defined in a configuration file.
    Logging: Logs task execution status for monitoring and debugging.

Prerequisites
1. Install Dependencies

Ensure you have Python 3.6+ and the following libraries installed:

pip install twilio sendgrid

2. Twilio and SendGrid Accounts

    Twilio: Create an account and obtain your Account SID, Auth Token, and a Twilio phone number.
    SendGrid: Create an account and obtain an API key.

Setup

    Clone the Repository

git clone <repository-url>
cd <repository-folder>

Add Configuration File
Create a tasks_config.json file in the project directory with the following format:

{
    "tasks": [
        {
            "name": "send_sms",
            "params": {
                "to": "+1234567890",
                "message": "Hello from Twilio!"
            }
        },
        {
            "name": "send_email",
            "params": {
                "to": "recipient@example.com",
                "subject": "Greetings!",
                "content": "<h1>Hello from SendGrid!</h1>"
            }
        },
        {
            "name": "search_data",
            "params": {
                "query": "example search query"
            }
        }
    ]
}

Update API Credentials
Replace placeholders in the script with your actual Twilio and SendGrid credentials:

    TWILIO_SID = "your_twilio_account_sid"
    TWILIO_TOKEN = "your_twilio_auth_token"
    SENDGRID_API_KEY = "your_sendgrid_api_key"

Usage

Run the script to execute the tasks defined in the tasks_config.json file:

python task_automation.py

Functions Overview
1. load_task_config(file_path: str) -> Dict[str, Any]

Loads task configurations from a JSON file.
2. send_sms(params: Dict[str, Any])

Sends an SMS message using Twilio.
3. send_email(params: Dict[str, Any])

Sends an email using SendGrid.
4. search_data(params: Dict[str, Any])

Performs a placeholder data query.
5. execute_task(task: Dict[str, Any])

Executes a specific task based on its type and parameters.
6. run_task_sequence(config: Dict[str, Any])

Runs a sequence of tasks defined in the configuration.
Logs

Logs are stored in the console with the following format:

2024-11-15 14:22:30 - INFO - Starting task: send_sms
2024-11-15 14:22:31 - INFO - SMS sent successfully: SMXXXXXXXXXXXXXXXXXX
2024-11-15 14:22:31 - INFO - Starting task: send_email
2024-11-15 14:22:32 - INFO - Email sent successfully: 202 Accepted

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Muscle Analysis Model

This repository contains a TensorFlow-based pipeline for building, training, optimizing, and deploying a deep learning model for muscle analysis. The project includes dataset loading, preprocessing, augmentation, training a DenseNet121-based model, optimizing it for TensorFlow Lite, performing inference, and generating classification reports.
Features

    Model Architecture: Fine-tunes DenseNet121 for muscle group classification.
    Data Augmentation: Applies transformations to enhance dataset diversity.
    Optimization: Converts the trained model to TensorFlow Lite for faster inference.
    Inference Pipeline: Supports predictions on new images with post-processing for detailed muscle group analysis.
    Reporting: Generates classification reports for performance evaluation.

Installation
Requirements

    Python 3.6+
    TensorFlow 2.0+
    OpenCV
    NumPy
    scikit-learn

Install dependencies with:

pip install tensorflow opencv-python-headless numpy scikit-learn

Usage
1. Dataset Preparation

Organize the dataset into directories. Each directory corresponds to a muscle group. Update the parse_label function to extract labels from filenames based on your dataset structure.

def parse_label(file_name):
    # Define logic to parse label from file name or directory structure
    raise NotImplementedError("Define label parsing logic here.")

2. Training

Run the script with the path to your dataset:

python muscle_analysis.py

The dataset is split into training (80%) and validation (20%) sets. Adjust the num_classes variable to match the number of muscle groups.
3. Model Optimization

The trained model is converted to TensorFlow Lite format for optimized inference.
4. Inference

Test the model with a new image:

sample_image_path = "path_to_sample_image.jpg"

Ensure muscle_names corresponds to your dataset labels.
5. Reports

Generate a classification report for validation or test data:

generate_reports(model, test_images, test_labels)

File Structure

    muscle_analysis.py: Main script with functions for dataset preprocessing, model training, optimization, and inference.
    muscle_analysis_model.tflite: Optimized TensorFlow Lite model (generated after training).