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Guide: Generating and Using Topic-Specific Learning Assistant Backend 
https://github.com/JupyterJones/frontend
This document outlines how to use the Flask-based App Generator to create new topic-specific FastAPI Backend Applications for your learning assistant project.1. OverviewThe system consists of two main Python scripts:Flask App Generator (app_builder.py - your second script):A Flask web application that provides a user interface to input a topic name and a structured prompt.It uses a master template (template_app_v2.py) to generate a new, customized FastAPI backend application tailored to the specified topic.It also creates necessary directories and the topic-specific structured prompt file.FastAPI Backend Template (template_app_v2.py - your first script):A comprehensive FastAPI application template designed to serve as a backend for a learning assistant.It includes features like Q&A with Gemini, conversation history, file management, vector search with ChromaDB, context-enhanced responses, conversati

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#!/usr/bin/python3
import sys
import sqlite3
DATABASE = "notes.db"
conn = sqlite3.connect(DATABASE)
from datetime import datetime
conn.text_factory = str
c = conn.cursor()
if len(sys.argv) < 3:
     print ("\n******* NOTE - Notes Editor ******")

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import subprocess
import numpy as np
import logging
import glob
import random
import uuid
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(levelname)s - %(message)s')

def zoom_pan_nonlinear1(input_path, output_path):

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import logging
from moviepy.editor import VideoFileClip, VideoClip, concatenate_videoclips
from PIL import Image
import numpy as np
from sklearn.cluster import KMeans
import random
import os

# Configure logging
logging.basicConfig(filename='script_log.log', level=logging.DEBUG)

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from PIL import Image, ImageFilter
import os
import cv2
import random
import time
path = r"/home/jack/Desktop/HDD500/SCRIPTS/UGLY_DOLLS/"
#path = r"crawler4/"
base_image = random.choice([
    x for x in os.listdir(path)

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#!/home/jack/miniconda3/envs/cloned_base/bin/python
import os
import subprocess
import random
from sys import argv
import uuid
import shutil
def diag_trans(DIR,output_file):
    image_dir=DIR
    # Get a list of image file names in the directory

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from moviepy.editor import VideoFileClip, AudioFileClip, CompositeVideoClip
import random
import glob

def add_title_image(video_path, title_image_path, output_path):
    # Load the video file and title image
    video_clip = VideoFileClip(video_path)
    print(video_clip.size)
    width, height = video_clip.size

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#!/bin/bash

# Define the paths to your input videos
video1='0001.mp4'
video2='0002.mp4'
video3='0003.mp4'
output='real-fish_123.mp4'

# Create a text file to list the input videos
echo "file '$video1'" > input.txt

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#!/bin/bash
convert -size 512x666 -background black -fill white -font /home/jack/fonts/Arimo-Regular.ttf -pointsize 30 -gravity center caption:@topic.txt -set delay 4 -loop 0 -coalesce -layers Optimize topic.png