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Update main.py
Browse files
main.py
CHANGED
@@ -2,6 +2,7 @@ from flask import Flask, jsonify, request
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from flask_cors import CORS
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from pymongo.mongo_client import MongoClient
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from pymongo.server_api import ServerApi
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import google.generativeai as genai
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import urllib.parse
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from models import UserSchema
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@@ -10,13 +11,17 @@ from flask_jwt_extended import JWTManager, create_access_token
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from middleware.authUser import auth_user
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from datetime import timedelta
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from controllers.demo import get_initial_data
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import os
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app = Flask(__name__)
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bcrypt = Bcrypt(app)
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jwt = JWTManager(app)
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CORS(app)
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app.config['JWT_SECRET_KEY'] = os.getenv('JWT_SECRET')
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@@ -24,13 +29,15 @@ app.config['JWT_SECRET_KEY'] = os.getenv('JWT_SECRET')
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# MongoDB configuration
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username = urllib.parse.quote_plus(os.getenv('MONGO_USERNAME'))
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password = urllib.parse.quote_plus(os.getenv('MONGO_PASSWORD'))
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restUri = os.getenv('REST_URI')
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uri = f'mongodb+srv://{username}:{password}
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client = MongoClient(uri)
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db = client.GenUpNexus
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users_collection = db["
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# Send a ping to confirm a successful connection
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try:
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@@ -45,145 +52,412 @@ GOOGLE_API_KEY=os.getenv('GOOGLE_API_KEY')
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genai.configure(api_key=GOOGLE_API_KEY)
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model = genai.GenerativeModel('gemini-pro')
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@app.route('/')
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def index():
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return "Server is Running..."
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@app.route('/
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def tree():
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if request.method == 'POST':
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data = request.get_json()
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query = data.get('query')
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print(query)
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response = model.generate_content('''I will give you a topic and you have to generate an explanation of the topic
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position: { x: 100, y: 200 },
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data: {
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selects: {
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"handle-0": "smoothstep",
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"handle-1": "smoothstep",
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},
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},
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},
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{
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id: "5",
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type: "output",
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data: {
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label: "custom style",
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},
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className: "circle",
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style: {
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background: "#2B6CB0",
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color: "white",
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},
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position: { x: 400, y: 200 },
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sourcePosition: Position.Right,
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targetPosition: Position.Left,
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},
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{
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id: "6",
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type: "output",
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style: {
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background: "#63B3ED",
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color: "white",
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width: 100,
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},
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data: {
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label: "Node",
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},
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position: { x: 400, y: 325 },
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sourcePosition: Position.Right,
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targetPosition: Position.Left,
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},
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{
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id: "7",
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type: "default",
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className: "annotation",
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data: {
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label: (
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<>
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On the bottom left you see the <strong>Controls</strong> and the
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bottom right the <strong>MiniMap</strong>. This is also just a node 🥳
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</>
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),
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},
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draggable: false,
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selectable: false,
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position: { x: 150, y: 400 },
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},
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];
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edges = [
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{ id: "e1-2", source: "1", target: "2", label: "this is an edge label" },
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{ id: "e1-3", source: "1", target: "3", animated: true },
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{
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},
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},
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},
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{
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selectIndex: 1,
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},
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},
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Topic is: ''' + query)
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# print(response.text)
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return jsonify({'success': True, 'data': response.text})
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# return temp
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@app.route('/interview', methods=["POST", "GET"])
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def interview():
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if request.method == 'POST':
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data = request.get_json()
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if data.get('from') == 'client':
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elif data.get('from') == 'gradio':
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print(data)
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# User Routes
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expires = timedelta(days=7)
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access_token = create_access_token(identity={"email": user['email'], "id": str(user['_id'])}, expires_delta=expires)
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res = {"name": user['name'], "email": user['email']}
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return jsonify({"result": res, "token": access_token}), 200
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print(e)
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return jsonify({"message": "Something went wrong"}), 500
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@app.route('/mindmap/demo', methods=['POST'])
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def mindmapDemo():
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data = request.json
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print(data)
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return get_initial_data(), 200
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from flask_cors import CORS
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from pymongo.mongo_client import MongoClient
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from pymongo.server_api import ServerApi
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from bson.objectid import ObjectId
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import google.generativeai as genai
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import urllib.parse
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from models import UserSchema
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from middleware.authUser import auth_user
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from datetime import timedelta
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from controllers.demo import get_initial_data
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from controllers.mindmap import saveMindmap, getMindmap, deleteMindmap, getMindmapByid
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import json
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from dotenv import load_dotenv
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import os
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load_dotenv()
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app = Flask(__name__)
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bcrypt = Bcrypt(app)
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jwt = JWTManager(app)
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CORS(app)
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app.config['JWT_SECRET_KEY'] = os.getenv('JWT_SECRET')
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# MongoDB configuration
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username = urllib.parse.quote_plus(os.getenv('MONGO_USERNAME'))
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password = urllib.parse.quote_plus(os.getenv('MONGO_PASSWORD'))
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restUri = os.getenv('REST_URI');
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uri = f'mongodb+srv://{username}:{password}{restUri}'
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client = MongoClient(uri, server_api=ServerApi('1'))
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db = client.GenUpNexus
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users_collection = db["users"]
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interviews_collection = db["interviews"]
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savedMindmap = db["savedMindmap"]
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# Send a ping to confirm a successful connection
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try:
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genai.configure(api_key=GOOGLE_API_KEY)
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model = genai.GenerativeModel('gemini-pro')
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# Caches to reduce no of queries to MongoDB...
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user_id_ping = {'current': 0}
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user_chats = {}
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@app.route('/')
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def index():
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return "Server is Running..."
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@app.route('/index')
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def index2():
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return "routes checking..."
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@app.route('/trees', methods=["POST", "GET"])
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def tree():
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if request.method == 'POST':
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data = request.get_json()
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query = data.get('query')
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print(query)
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response = model.generate_content('''I will give you a topic and you have to generate an explanation of the topic in points in hierarchical tree structure and respond with JSON structure as follows:
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{
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"name": "Java",
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"children": [
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{
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"name": "Development Environment",
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"children": [
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{
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"name": "Java Source Code",
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"value": ".java files",
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"description": "Human-readable code written with Java syntax."
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},
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{
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"name": "Java Development Kit (JDK)",
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"children": [
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{
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"name": "Compiler",
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"value": "translates to bytecode",
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"description": "Transforms Java source code into bytecode instructions understood by the JVM."
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},
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{
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"name": "Java Class Library (JCL)",
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"value": "predefined classes and functions",
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"description": "Provides a collection of reusable code for common functionalities."
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}
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]
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}
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]
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},
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{
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"name": "Execution",
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"children": [
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{
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"name": "Java Runtime Environment (JRE)",
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"children": [
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{
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"name": "Java Virtual Machine (JVM)",
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"value": "executes bytecode",
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"description": "Software program that interprets and executes bytecode instructions."
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},
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{
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"name": "Class Loader",
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"value": "loads bytecode into memory",
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"description": "Loads .class files containing bytecode into JVM memory for execution."
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}
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]
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},
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{
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"name": "Bytecode",
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"value": ".class files (platform-independent)",
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"description": "Machine-independent instructions generated by the compiler, executable on any system with JVM."
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},
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{
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"name": "Just-In-Time (JIT) Compilation (optional)",
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"value": "improves performance by translating bytecode to machine code",
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"description": "Technique that translates frequently used bytecode sections into native machine code for faster execution."
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}
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]
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},
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{
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"name": "Key Features",
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"children": [
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{
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"name": "Object-Oriented Programming",
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"value": "uses objects and classes",
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"description": "Programs are structured around objects that encapsulate data and behavior."
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},
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{
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"name": "Platform Independent (write once, run anywhere)",
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"value": "bytecode runs on any system with JVM",
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"description": "Java code can be compiled once and run on any platform with a JVM installed."
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},
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{
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"name": "Garbage Collection",
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"value": "automatic memory management",
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"description": "JVM automatically reclaims memory from unused objects, simplifying memory management for developers."
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}
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]
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}
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]
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}
|
154 |
Topic is: ''' + query)
|
155 |
|
156 |
# print(response.text)
|
157 |
return jsonify({'success': True, 'data': response.text})
|
158 |
# return temp
|
159 |
|
160 |
+
|
161 |
+
|
162 |
+
|
163 |
+
@app.route('/tree/demo', methods=["POST"])
|
164 |
+
def treeDemo():
|
165 |
+
if request.method == 'POST':
|
166 |
+
data = request.get_json()
|
167 |
+
query = data.get('query')
|
168 |
+
print(query)
|
169 |
+
response = model.generate_content('''Generate a comprehensive knowledge map representing the user's query, suitable for ReactFlow visualization.
|
170 |
+
|
171 |
+
**Prompt:** {query}
|
172 |
+
|
173 |
+
**Structure:**
|
174 |
+
|
175 |
+
- Top-level node: Represent the user's query.
|
176 |
+
- Sub-nodes branching out based on the query's relevance:
|
177 |
+
- Leverage external knowledge sources (e.g., Wikipedia, knowledge graphs, domain-specific APIs) to identify relevant sub-concepts, related entities, and potential relationships.
|
178 |
+
- Consider including different categories of sub-nodes:
|
179 |
+
- **Concepts:** Core ideas or principles related to the query.
|
180 |
+
- **Subfields:** Specialized areas within the main topic.
|
181 |
+
- **Applications:** Practical uses of the concept or subfield.
|
182 |
+
- **Tools and Technologies:** Software or platforms used to implement the concepts.
|
183 |
+
- **Examples:** Illustrative instances or use cases.
|
184 |
+
- **Historical Context:** Milestones or key figures in the topic's development.
|
185 |
+
- **See Also:** Links to broader concepts or related areas for the further exploration.
|
186 |
+
|
187 |
+
|
188 |
+
**Content:**
|
189 |
+
|
190 |
+
- Each node should have a label describing the concept, entity, or tool.
|
191 |
+
- Optionally, include brief descriptions, definitions, or key points within the nodes or as tooltips.
|
192 |
+
- Consider using icons to visually represent different categories of nodes (e.g., light bulb for concepts, gear for tools, calendar for historical context, puzzle piece for subfields).
|
193 |
+
- there should be atmax 10 nodes in the knowledge map.
|
194 |
+
- Also follow the n-ary tree structure for better visualization.
|
195 |
+
- Ensure the knowledge map is visually appealing, well-organized, and easy to navigate.
|
196 |
+
|
197 |
+
**Desired Format:**
|
198 |
+
|
199 |
+
- JSON structure compatible with ReactFlow:
|
200 |
+
- nodes (list): id, position, data (label, description, icon(if required), category), type(input, output or custom), style (background, color).
|
201 |
+
- edges (list): id, source, target, label(if required), animated (true or false), style (stroke).
|
202 |
+
- keep the position of nodes spaced out for better visualization.
|
203 |
+
- always keep the top-level node at the center of the visualization.
|
204 |
+
- keep atleast 2 edges "animated":true.
|
205 |
+
- Strictly keep the first node with style having color property with value blue and background property with value #0FFFF0.
|
206 |
+
- Strictly keep the second node with type property value as custom.
|
207 |
+
- You can style the nodes with different colors and edges with different colors.
|
208 |
+
- to edit edges add style with stroke property and a hexcode value to it.
|
209 |
+
|
210 |
+
Topic is: ''' + query)
|
211 |
+
|
212 |
+
# response.text(8,)
|
213 |
+
print(response.text)
|
214 |
+
json_data = response.text
|
215 |
+
modified_json_data = json_data[8:-3]
|
216 |
+
return jsonify({'success': True, 'data': modified_json_data})
|
217 |
+
# return temp
|
218 |
+
|
219 |
+
|
220 |
+
def res(user_id):
|
221 |
+
avg_text = 0
|
222 |
+
avg_code = 0
|
223 |
+
count = 0
|
224 |
+
for i, ele in enumerate(user_chats[user_id]['chat'].history):
|
225 |
+
if i == 0:
|
226 |
+
continue
|
227 |
+
|
228 |
+
if ele.role == 'model':
|
229 |
+
temp = json.loads(ele.parts[0].text)
|
230 |
+
print(temp)
|
231 |
+
if 'question' in temp.keys():
|
232 |
+
continue
|
233 |
+
elif 'next_question' in temp.keys() or 'end' in temp.keys():
|
234 |
+
count += 1
|
235 |
+
avg_text += temp['text_correctness']
|
236 |
+
if temp['code_correctness']:
|
237 |
+
avg_code += temp['code_correctness']
|
238 |
+
print(json.loads(ele.parts[0].text), end='\n\n')
|
239 |
+
|
240 |
+
avg_text /= count
|
241 |
+
avg_code /= count
|
242 |
+
|
243 |
+
user_chats[user_id]['test_results'] = {'avg_text': avg_text, 'avg_code': avg_code}
|
244 |
+
|
245 |
+
return True
|
246 |
+
|
247 |
+
|
248 |
@app.route('/interview', methods=["POST", "GET"])
|
249 |
def interview():
|
250 |
if request.method == 'POST':
|
251 |
data = request.get_json()
|
252 |
+
print(data)
|
253 |
if data.get('from') == 'client':
|
254 |
+
user_id = data.get('user_id')
|
255 |
+
request_type = data.get('type')
|
256 |
+
|
257 |
+
if request_type == 1: # Initialize Questionarrie.
|
258 |
+
chat = model.start_chat(history=[])
|
259 |
+
user_chats[user_id] = {}
|
260 |
+
user_chats[user_id]['chat'] = chat
|
261 |
+
user_chats[user_id]['processed'] = False
|
262 |
+
|
263 |
+
position = data.get('position')
|
264 |
+
round = data.get('round')
|
265 |
+
difficulty_level = data.get('difficulty_level')
|
266 |
+
company_name = data.get('company_name')
|
267 |
+
|
268 |
+
user_chats[user_id]['position'] = position
|
269 |
+
user_chats[user_id]['round'] = round
|
270 |
+
user_chats[user_id]['difficulty_level'] = difficulty_level
|
271 |
+
user_chats[user_id]['company_name'] = company_name
|
272 |
+
|
273 |
+
response = chat.send_message('''You are a Interviewer. I am providing you with the the position for which the inerview is, Round type, difficulty level of the interview to be conducted, Company name.
|
274 |
+
You need to generate atmost 2 interview questions one after another.
|
275 |
+
The questions may consists of writing a small code along with text as well.
|
276 |
+
|
277 |
+
Now generate first question in following JSON format:
|
278 |
+
{
|
279 |
+
"question": "What is ...?"
|
280 |
+
}
|
281 |
+
|
282 |
+
I will respond to the question in the following JSON format:
|
283 |
+
{
|
284 |
+
"text_answer": "answer ...",
|
285 |
+
"code": "if any...."
|
286 |
+
}
|
287 |
+
|
288 |
+
Now after evaluating the answers you need to respond in the following JSON format:
|
289 |
+
{
|
290 |
+
"next_question": "What is ...?",
|
291 |
+
"text_correctness": "Test the correctness of text and return a range from 1 to 5 of correctness of text.",
|
292 |
+
"text_suggestions": "Some suggestions regarding the text_answer.... in string format."
|
293 |
+
"code_correctness": "Test the correctness of code and return a range from 1 to 5 of correctness of code",
|
294 |
+
"code_suggestions": "Any suggestions or optimizations to the code...in string format.",
|
295 |
+
}
|
296 |
+
|
297 |
+
At the end of the interview if no Questions are required then respond in the following format:
|
298 |
+
{
|
299 |
+
"text_correctness": "Test the correctness of text and return a range from 1 to 5 of correctness of text.",
|
300 |
+
"text_suggestions": "Some suggestions regarding the text_answer...."
|
301 |
+
"code_correctness": "Test the correctness of code and return a range from 1 to 5 of correctness of code",
|
302 |
+
"code_suggestions": "Any suggestions or optimizations to the code...",
|
303 |
+
"end": "No more Questions thanks for your time."
|
304 |
+
}
|
305 |
+
|
306 |
+
Here are the details:
|
307 |
+
Position : '''+ position + '''
|
308 |
+
Round: '''+ round + '''
|
309 |
+
Difficullty Level : '''+ difficulty_level + '''
|
310 |
+
Company Interview : ''' + company_name)
|
311 |
+
print(response.text)
|
312 |
+
temp = json.loads(response.text)
|
313 |
+
user_chats[user_id]['qa'] = [{'question': temp['question']}]
|
314 |
+
return jsonify({'success': True, 'data': response.text})
|
315 |
+
|
316 |
+
if request_type == 2:
|
317 |
+
text_data = data.get('text_data')
|
318 |
+
code = data.get('code')
|
319 |
+
|
320 |
+
chat = user_chats[user_id]['chat']
|
321 |
+
response = chat.send_message('''{"text_answer": "''' + text_data + '''", "code": "''' + code + '''"}''')
|
322 |
+
|
323 |
+
print(response.text)
|
324 |
+
|
325 |
+
json_text = json.loads(response.text)
|
326 |
+
|
327 |
+
for i, ele in enumerate(user_chats[user_id]['qa']):
|
328 |
+
if i == len(user_chats[user_id]['qa'])-1:
|
329 |
+
ele['text_answer'] = text_data
|
330 |
+
ele['code_answer'] = code
|
331 |
+
ele['text_correctness'] = json_text['text_correctness']
|
332 |
+
ele['text_suggestions'] = json_text['text_suggestions']
|
333 |
+
ele['code_correctness'] = json_text['code_correctness']
|
334 |
+
ele['code_suggestions'] = json_text['code_suggestions']
|
335 |
+
|
336 |
+
try:
|
337 |
+
if json_text['end']:
|
338 |
+
user_id_ping['current'] = user_id
|
339 |
+
if res(user_id):
|
340 |
+
print(user_chats[user_id])
|
341 |
+
return jsonify({'success': True, 'data': response.text, 'end': True})
|
342 |
+
except Exception as e:
|
343 |
+
print(e)
|
344 |
+
|
345 |
+
user_chats[user_id]['qa'].append({'question': json_text['next_question']})
|
346 |
+
|
347 |
+
return jsonify({'success': True, 'data': response.text, 'end': False})
|
348 |
+
|
349 |
+
|
350 |
elif data.get('from') == 'gradio':
|
351 |
print(data)
|
352 |
+
user_id = data.get('user_id')
|
353 |
+
user_chats[user_id]['processed'] = True
|
354 |
+
user_chats[user_id]['gradio_results'] = {'total_video_emotions': data.get('total_video_emotions'), 'emotions_final': data.get('emotions_final'), 'body_language': data.get('body_language'), 'distraction_rate': data.get('distraction_rate'), 'formatted_response': data.get('formatted_response'), 'total_transcript_sentiment': data.get('total_transcript_sentiment')}
|
355 |
+
|
356 |
+
emotion_weights = {
|
357 |
+
'admiration': 0.8,
|
358 |
+
'amusement': 0.7,
|
359 |
+
'angry': -0.8,
|
360 |
+
'annoyance': -0.7,
|
361 |
+
'approval': 0.9,
|
362 |
+
'calm': 0.8,
|
363 |
+
'caring': 0.8,
|
364 |
+
'confusion': -0.5,
|
365 |
+
'curiosity': 0.6,
|
366 |
+
'desire': 0.7,
|
367 |
+
'disappointment': -0.8,
|
368 |
+
'disapproval': -0.9,
|
369 |
+
'disgust': -0.9,
|
370 |
+
'embarrassment': -0.7,
|
371 |
+
'excitement': 0.8,
|
372 |
+
'fear': -0.8,
|
373 |
+
'fearful': -0.8,
|
374 |
+
'gratitude': 0.9,
|
375 |
+
'grief': -0.9,
|
376 |
+
'happy': 0.9,
|
377 |
+
'love': 0.9,
|
378 |
+
'nervousness': -0.6,
|
379 |
+
'optimism': 0.8,
|
380 |
+
'pride': 0.9,
|
381 |
+
'realization': 0.7,
|
382 |
+
'relief': 0.8,
|
383 |
+
'remorse': -0.8,
|
384 |
+
'sad': -0.9,
|
385 |
+
'surprise': 0.7,
|
386 |
+
'surprised': 0.7,
|
387 |
+
'neutral': 0.0
|
388 |
+
}
|
389 |
+
|
390 |
+
temp = data.get('total_video_emotions')
|
391 |
+
temp2 = data.get('emotions_final')
|
392 |
+
temp3 = data.get('formatted_response')
|
393 |
+
temp4 = data.get('total_transcript_sentiment')
|
394 |
+
|
395 |
+
total_video_emotion_score = sum(temp[emotion] * emotion_weights[emotion] for emotion in temp)
|
396 |
+
total_video_emotion_normalized_score = ((total_video_emotion_score + 1) / 2) * 9 + 1
|
397 |
+
|
398 |
+
emotion_final_score = sum(temp2[emotion] * emotion_weights[emotion] for emotion in temp2)
|
399 |
+
emotion_final_normalized_score = ((emotion_final_score + 1) / 2) * 9 + 1
|
400 |
+
|
401 |
+
speech_sentiment_score = sum(temp3[emotion] * emotion_weights[emotion] for emotion in temp3)
|
402 |
+
speech_sentiment_normalized_score = ((speech_sentiment_score + 1) / 2) * 9 + 1
|
403 |
+
|
404 |
+
total_transcript_sentiment_score = sum(temp4[emotion] * emotion_weights[emotion] for emotion in temp4)
|
405 |
+
total_transcript_sentiment_normalized_score = ((total_transcript_sentiment_score + 1) / 2) * 9 + 1
|
406 |
+
|
407 |
+
body_language_score = data.get('body_language')['Good'] * 10
|
408 |
+
distraction_rate_score = data.get('distraction_rate') * 10
|
409 |
+
|
410 |
+
avg_text_score = user_chats[user_id]['test_results']['avg_text']
|
411 |
+
avg_code_score = user_chats[user_id]['test_results']['avg_code']
|
412 |
+
|
413 |
+
interview_score = (total_video_emotion_normalized_score + emotion_final_normalized_score + speech_sentiment_normalized_score + total_transcript_sentiment_normalized_score + body_language_score + distraction_rate_score + avg_text_score + avg_code_score)/7
|
414 |
+
|
415 |
+
print(interview_score)
|
416 |
+
user_chats[user_id]['interview_score'] = interview_score
|
417 |
+
|
418 |
+
user_chats[user_id]['user_id'] = user_id
|
419 |
+
print(user_chats[user_id])
|
420 |
+
|
421 |
+
# Store user_chats[user_id] into MongoDB...
|
422 |
+
del user_chats[user_id]["chat"]
|
423 |
+
result = interviews_collection.insert_one(user_chats[user_id])
|
424 |
+
print(result)
|
425 |
+
|
426 |
+
return jsonify({'success': True})
|
427 |
+
|
428 |
+
|
429 |
+
@app.route('/result', methods=['POST', 'GET'])
|
430 |
+
def result():
|
431 |
+
if request.method == 'POST':
|
432 |
+
data = request.get_json()
|
433 |
+
user_id = data.get('user_id')
|
434 |
+
|
435 |
+
if data.get('type') == 1:
|
436 |
+
result = interviews_collection.find({ "user_id" : user_id}, {"_id" : 1, "company_name" : 1, "difficulty_level" : 1, "interview_score" : 1, "position" : 1, "round" : 1 })
|
437 |
+
temp = []
|
438 |
+
for ele in result:
|
439 |
+
ele['_id'] = str(ele['_id'])
|
440 |
+
temp.append(ele)
|
441 |
+
|
442 |
+
return jsonify({'success': True, 'data': temp})
|
443 |
+
|
444 |
+
elif data.get('type') == 2:
|
445 |
+
result2 = interviews_collection.find_one({ "_id": ObjectId(data.get("_id")), "user_id": user_id })
|
446 |
+
if result2:
|
447 |
+
result2['_id'] = str(result2['_id'])
|
448 |
+
print(result2)
|
449 |
+
return jsonify({'success': True, 'data': result2})
|
450 |
+
else:
|
451 |
+
if not user_chats[user_id]['processed']:
|
452 |
+
return jsonify({'processing': True})
|
453 |
+
else:
|
454 |
+
return jsonify({'error': "No such record found."})
|
455 |
+
|
456 |
+
|
457 |
+
@app.route('/useridping', methods=['GET'])
|
458 |
+
def useridping():
|
459 |
+
if request.method == 'GET':
|
460 |
+
return jsonify(user_id_ping)
|
461 |
|
462 |
|
463 |
# User Routes
|
|
|
510 |
expires = timedelta(days=7)
|
511 |
access_token = create_access_token(identity={"email": user['email'], "id": str(user['_id'])}, expires_delta=expires)
|
512 |
|
513 |
+
res = {"name": user['name'], "email": user['email'], "user_id": str(user['_id'])}
|
514 |
|
515 |
return jsonify({"result": res, "token": access_token}), 200
|
516 |
|
|
|
530 |
print(e)
|
531 |
return jsonify({"message": "Something went wrong"}), 500
|
532 |
|
533 |
+
# mindmap routes
|
534 |
+
@app.route('/mindmap/save', methods=['POST'])
|
535 |
+
@auth_user
|
536 |
+
def mindmapSave():
|
537 |
+
userId = request.userId
|
538 |
+
data = request.json
|
539 |
+
return saveMindmap(data, userId, savedMindmap)
|
540 |
+
|
541 |
+
@app.route('/mindmap/get', methods=['GET'])
|
542 |
+
@auth_user
|
543 |
+
def mindmapGet():
|
544 |
+
userId = request.userId
|
545 |
+
return getMindmap(userId, savedMindmap)
|
546 |
+
|
547 |
+
@app.route('/mindmap/get/<id>', methods=['GET'])
|
548 |
+
@auth_user
|
549 |
+
def mindmapGetById(id):
|
550 |
+
userId = request.userId
|
551 |
+
return getMindmapByid(userId, id, savedMindmap)
|
552 |
+
|
553 |
+
@app.route('/mindmap/delete', methods=['POST'])
|
554 |
+
@auth_user
|
555 |
+
def mindmapDelete():
|
556 |
+
userId = request.userId
|
557 |
+
data = request.json
|
558 |
+
return deleteMindmap(userId, data, savedMindmap)
|
559 |
+
|
560 |
+
|
561 |
@app.route('/mindmap/demo', methods=['POST'])
|
562 |
def mindmapDemo():
|
563 |
data = request.json
|
564 |
+
print(data);
|
565 |
return get_initial_data(), 200
|
566 |
|
567 |
|