怎么让AI变成一个真正好用的“编程搭档”，而不是添乱的实习生

import openai

def get_code_suggestion(prompt):
    response = openai.Completion.create(
        engine="davinci-codex",
        prompt=prompt,
        max_tokens=100,
        n=1,
        stop=None,
        temperature=0.5,
    )
    return response.choices[0].text.strip()

# 示例调用
prompt = "def reverse_string(s):"
suggestion = get_code_suggestion(prompt)
print(suggestion)

from transformers import GPT2LMHeadModel, GPT2Tokenizer, TextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments

# 加载预训练模型和分词器
model_name = 'gpt2'
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

# 准备训练数据
train_dataset = TextDataset(
    tokenizer=tokenizer,
    file_path='path/to/your/training_data.txt',
    block_size=128
)

data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer, mlm=False
)

# 设置训练参数
training_args = TrainingArguments(
    output_dir='./results',
    overwrite_output_dir=True,
    num_train_epochs=1,
    per_device_train_batch_size=8,
    save_steps=10_000,
    save_total_limit=2,
)

# 创建Trainer并开始训练
trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=train_dataset,
)

trainer.train()

import requests

def send_feedback(prompt, suggestion, feedback):
    url = 'https://api.example.com/feedback'
    data = {
        'prompt': prompt,
        'suggestion': suggestion,
        'feedback': feedback
    }
    response = requests.post(url, json=data)
    if response.status_code == 200:
        print("Feedback sent successfully")
    else:
        print("Failed to send feedback")

# 示例调用
prompt = "def reverse_string(s):"
suggestion = get_code_suggestion(prompt)
send_feedback(prompt, suggestion, "Good suggestion!")

import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity

# 加载数据
data = pd.read_csv('path/to/monkey_music_data.csv')

# 计算歌曲之间的相似度
similarity_matrix = cosine_similarity(data['features'])

def recommend_similar_songs(user_history, top_n=5):
    # 获取用户历史歌曲的索引
    user_song_indices = [data[data['song_id'] == song_id].index[0] for song_id in user_history]

    # 计算平均相似度
    avg_similarity = similarity_matrix[user_song_indices].mean(axis=0)

    # 获取最相似的歌曲
    similar_song_indices = avg_similarity.argsort()[-top_n:][::-1]
    recommended_songs = data.iloc[similar_song_indices]['song_id'].tolist()

    return recommended_songs

# 示例调用
user_history = ['song1', 'song2', 'song3']
recommendations = recommend_similar_songs(user_history)
print(recommendations)