import sqlite3
from flask import Flask, request, jsonify, render_template, redirect
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Embedding
import tensorflow as tf
import numpy as np

print(tf.__version__)
app = Flask(__name__)

vocab_size = 5000 
embedding_dim = 64
lstm_units = 64

model = Sequential([
    Embedding(input_dim=10000, output_dim=128),
    LSTM(units=128),
    Dense(units=1, activation='sigmoid')
])

model.summary()

def dummy_tokenizer(text):
    return np.array([0] * len(text))  

def dummy_detokenizer(tokens):
    return ''.join(['a'] * len(tokens))  

def text_generator(prompt, max_length=150):
    input_tokens = dummy_tokenizer(prompt)
    generated_tokens = []

    for _ in range(max_length):
        preds = model.predict(np.array([input_tokens]))
        next_token = np.argmax(preds, axis=-1)[0]
        if next_token == 0:  
            break
        generated_tokens.append(next_token)
        input_tokens = np.append(input_tokens, next_token)

    return dummy_detokenizer(generated_tokens)


def init_db():
    conn = sqlite3.connect('knowledge.db')
    cursor = conn.cursor()
    cursor.execute('''CREATE TABLE IF NOT EXISTS knowledge
                      (id INTEGER PRIMARY KEY, title TEXT, content TEXT, tags TEXT)''')
    conn.commit()
    conn.close()

@app.route('/add_knowledge', methods=['POST'])
def add_knowledge():
    title = request.form['title']
    content = request.form['content']
    tags = request.form['tags']
    conn = sqlite3.connect('knowledge.db')
    cursor = conn.cursor()
    cursor.execute("INSERT INTO knowledge (title, content, tags) VALUES (?, ?, ?)",
                   (title, content, tags))
    conn.commit()
    conn.close()
    return '知識添加成功!', 201

@app.route('/wiki/<path:subpath>', methods=['GET'])
def wiki(subpath):
    tag = request.args.get('tag')
    conn = sqlite3.connect('knowledge.db')
    cursor = conn.cursor()
    cursor.execute("SELECT * FROM knowledge WHERE tags LIKE ?", ('%' + tag + '%',))
    conn.close()
    return redirect(f'http://localhost/{subpath}', code=302)

@app.route('/update_knowledge/<int:id>', methods=['PUT'])
def update_knowledge(id):
    content = request.form['content']
    conn = sqlite3.connect('knowledge.db')
    cursor = conn.cursor()
    cursor.execute("UPDATE knowledge SET content = ? WHERE id = ?", (content, id))
    conn.commit()
    conn.close()
    return '知識更新成功!', 200

@app.route('/delete_knowledge/<int:id>', methods=['DELETE'])
def delete_knowledge(id):
    conn = sqlite3.connect('knowledge.db')
    cursor = conn.cursor()
    cursor.execute("DELETE FROM knowledge WHERE id = ?", (id,))
    conn.commit()
    conn.close()
    return '知識刪除成功!', 200

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/ask', methods=['POST'])
def ask():
    question = request.form.get('question')

    try:
        answer = text_generator(question)
    except Exception as e:
        return jsonify({'answer': '錯誤: ' + str(e)}), 501
    return jsonify({'answer': answer})

if __name__ == '__main__':
    init_db()
    app.run(debug=True, port=5001)

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Embedding
怎麼試都說無法解析匯入，重新安裝也沒用