JupyterJones

import os
import sqlite3
import re
import time
import uuid
import glob
from flask import Flask, render_template_string, request, url_for, session
from werkzeug.utils import secure_filename
from typing import Union

JupyterJones

import os
from flask import Flask, render_template_string, request, redirect, url_for, flash, jsonify, make_response
from flask_sqlalchemy import SQLAlchemy
import chromadb
from sentence_transformers import SentenceTransformer
from datetime import datetime
import mistune # New library for Markdown
from markupsafe import escape, Markup 
# --- App & Database Configuration ---
app = Flask(__name__)

JupyterJones

import os
from flask import Flask, request, render_template, redirect, flash, url_for, render_template_string
from moviepy.editor import ImageSequenceClip

app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = os.path.join(app.root_path, 'static/uploads')
app.secret_key = 'your_secret_key' #Replace with a strong, randomly generated secret key


@app.route('/', methods=['GET', 'POST'])

JupyterJones

import httpx
import json
import os
import re
import sqlite3
from datetime import datetime
from fastapi import FastAPI, Form, HTTPException, BackgroundTasks, Request
from fastapi.responses import StreamingResponse, HTMLResponse
from icecream import ic
# --- LangChain & Database Imports ---

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#!/bin/bash
# CONV is ffmpeg via docker
Path=$1
echo "Processing directory: $Path"

# Ensure the path ends with a slash
if [[ "$Path" != */ ]]; then
    Path="$Path"/
fi

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from PIL import Image, ImageDraw, ImageFont
import subprocess
import textwrap
from icecream import ic

#--------------------
def get_mp3_duration(mp3_path):
    cmd = [
        "ffprobe", "-v", "error",
        "-show_entries", "format=duration",

JupyterJones

from balacoon_tts import TTS
import soundfile as sf
import numpy as np
import traceback
import os
import subprocess
import re
from icecream import ic
import sys
# >>> ============================================================ <<<

JupyterJones

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 ******")

JupyterJones

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):