Alice
Hello Bob! I've been working on an 'Integrated Graduate Management System' recently. It's quite challenging to manage all the tasks like enrollment tracking, thesis submissions, and student queries efficiently.
Bob
That sounds tough indeed. Have you considered integrating an AI assistant into your system? It could handle repetitive tasks and improve efficiency significantly.
Alice
Interesting idea! But how exactly can we implement this? Could you guide me through the process?
Bob
Sure thing! First, let's outline the basic architecture of the system. We'll need a backend server handling API requests, a database storing user data, and frontend interfaces for interaction.
Alice
Got it. For the AI part, do we use pre-trained models or build our own?
Bob
Pre-trained models like those from Hugging Face Transformers would be ideal. They provide robust NLP capabilities out-of-the-box. Let's start by setting up a simple Flask app:
from flask import Flask, request, jsonify
import torch
from transformers import BertTokenizer, BertForQuestionAnswering
app = Flask(__name__)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
@app.route('/query', methods=['POST'])
def query():
data = request.get_json()
question = data['question']
context = data['context']
inputs = tokenizer.encode_plus(question, context, return_tensors='pt')
outputs = model(**inputs)
answer_start = torch.argmax(outputs[0]) # Get the most likely beginning of answer with the argmax of the score
answer_end = torch.argmax(outputs[1]) + 1
answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs['input_ids'][0][answer_start:answer_end]))
return jsonify({'answer': answer})
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With this setup, the '/query' endpoint will take JSON input containing a question and context, then return the best possible answer using BERT.
Alice
Wow, that looks straightforward. How about integrating it with the database?
Bob
We can create tables for students, courses, and submissions. Then, modify the backend to fetch relevant information based on queries.
CREATE TABLE Students (
id INT PRIMARY KEY,
name VARCHAR(255),
email VARCHAR(255)
);
CREATE TABLE Submissions (
id INT PRIMARY KEY,
student_id INT,
thesis_title VARCHAR(255),
status ENUM('submitted', 'reviewed', 'approved'),
FOREIGN KEY (student_id) REFERENCES Students(id)
);
]]>
This SQL script sets up two essential tables for managing student records and their thesis submissions. The AI assistant can now pull data from these tables dynamically.
Alice
Thank you so much, Bob! This integration makes my work much easier. I'm excited to see what else we can achieve together.
Bob
You're welcome! Always happy to help. Keep pushing boundaries in tech!
