Cacher is the code snippet organizer for pro developers

You are an expert UX/SEO copywriter. 
You have a wealth of organic SEO knowledge and you understand how to make websites perform for search engines and agentic search.
You are also an established copywriter for UX, you know how to strike the perfect balance between converting a user and making it perform well in the digital space.
You write in plain english that an 8 year old can understand, you don't use emdashes and you exclusively write in british english.

npx playwright show-report

add_action( 'wpcf7_before_send_mail', function( $contact_form ) {

    $submission = WPCF7_Submission::get_instance();
    if ( ! $submission ) return;

    $url = $submission->get_meta( 'url' );
    if ( ! $url ) return;

    $post_id = url_to_postid( $url );
    if ( ! $post_id ) return;

    $post_title = get_the_title( $post_id );

    if ( function_exists( 'get_field' ) ) {
        $download_url = get_field( 'pdf_link', $post_id );
    } else {
        $download_url = get_post_meta( $post_i

Ok chill, để mình gom hết lại thành **một hướng dẫn gọn – rõ – chạy được ngay**, từ lúc cài MailHog bằng Homebrew cho tới lúc WordPress test mail, cả case chạy bằng Docker hoặc chạy thuần trên máy.

---

# 🚀 HƯỚNG DẪN FULL: CÀI MAILHOG + SETUP WORDPRESS (DOCKER & NON-DOCKER)

## 🟩 PHẦN 1 — Cài MailHog bằng Homebrew

### 1. Cài đặt

```bash
brew install mailhog
```

### 2. Chạy MailHog

```bash
mailhog
```

### 3. Truy cập giao diện

* Web UI: **[http://localhost:8025](http://localhost:8025)**
* 

curl -fsSL https://cli.kiro.dev/install | bash

/**
 * @param {number[]} bits
 * @return {boolean}
 */
var isOneBitCharacter = function(bits) {
    // Start from the first bit
    let i = 0;

    // Traverse until the second-to-last bit
    // (because the last bit is always 0, we want to see if it's consumed or not)
    while (i < bits.length - 1) {
        if (bits[i] === 1) {
            // If we see a '1', it must form a two-bit character (10 or 11)
            // So we skip TWO positions
            i += 2;
        } else {
            /

import os
from pathlib import Path

# -------------------------
# Define project name (main package)
# -------------------------
project_name = "src"  # More descriptive than "src"

# -------------------------
# Define additional folders
# -------------------------
cicd_folder       = "Github"
configs_folder    = "configs"
data_folder       = "data"
notebooks_folder  = "notebooks"
static_css_folder = "static/css"
templates_folder  = "templates"
tests_folder      = "tests"
scrip

print("Hello")
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from langchain_classic.chains import ConversationChain
from langchain_classic.memory import ConversationBufferMemory
from langchain_classic.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_classic.schema import SystemMessage,HumanMessage
# Load environment variables
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY"
if not openai_api_key:
    raise ValueError("O

## Messages

from langchain.core.messages import (
    AIMessage,
    AIMessageChunk,
    BaseMessage,
    BaseMessageChunk,
    HumanMessage,
    HumanMessageChunk,
    SystemMessage,
    SystemMessageChunk,
    ToolMessage,
    ToolMessageChunk,
    FunctionMessage,
    FunctionMessageChunk,
)

  ## Prompts
  
  from langchain.core.prompts import (
    PromptTemplate,
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
    AIMessag

🚀 Project Prompt: Build a Smart End-to-End Chatbot
🧩 Objective
Design and implement a robust, modular, and intelligent chatbot system using modern AI and web technologies. The chatbot should be capable of handling dynamic conversations, storing history, and providing a clean user interface.
🛠️ Tech Stack

🧠 Brain: LangChain + OpenAI (for LLM orchestration and prompt management)
⚙️ Backend: FastAPI (for serving the chatbot API)
💬 Frontend: Streamlit (for interactive chat UI)
🔒 Security: .

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load your dataset
df = pd.read_csv("your_data.csv")  # Replace with your actual file

# Compute correlation matrix
corr_matrix = df.corr(numeric_only=True)

# Plot heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(corr_matrix, annot=True, fmt=".2f", cmap="coolwarm", linewidths=0.5)
plt.title("Correlation Heatmap")
plt.tight_layout()
plt.show()

final_df = pd.concat([
    temperature_humidity[['time', 'day_temperature_C', 'day_humidity_percent',
                          'dayofweek_sin', 'dayofweek_cos',
                          'dayofmonth_sin', 'dayofmonth_cos',
                          'dayofyear_sin', 'dayofyear_cos']],
    daily_counts[['COUNT']].rename(columns={'COUNT': 'complaint_count'})
], axis=1)

import pandas as pd

# Sample data
data = {'temperature': [22, 23, 21, 24, 100, 22, 23, 25, 20, 21]}
df = pd.DataFrame(data)

# Function to remove outliers using IQR
def remove_outliers_iqr(df, column):
    Q1 = df[column].quantile(0.25)
    Q3 = df[column].quantile(0.75)
    IQR = Q3 - Q1
    lower_bound = Q1 - 1.5 * IQR
    upper_bound = Q3 + 1.5 * IQR
    return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]

# Call the function
df_clean = remove_outliers_iqr(df

import matplotlib.pyplot as plt
import seaborn as sns

# List of columns to plot
columns_to_plot = [
    'day_temperature_C', 'day_humidity_percent', 'complaint_count',
    'dayofweek_sin', 'dayofweek_cos',
    'dayofmonth_sin', 'dayofmonth_cos',
    'dayofyear_sin', 'dayofyear_cos'
]

# Loop through each column and plot KDE
for col in columns_to_plot:
    plt.figure(figsize=(10, 6))
    sns.kdeplot(df[col], shade=True, color='purple')
    plt.title(f'Density Plot of {col}')
    

import pandas as pd
from sklearn.preprocessing import MinMaxScaler

# Example dataset (replace this with your own DataFrame)
df = pd.DataFrame({
    'temp_max_C': [25, 30, 35, 40],
    'precip_mm': [0.0, 5.0, 10.0, 50.0],
    'wind_speed_max_m_s': [2.5, 5.0, 7.5, 10.0],
    'day_of_week_sin': [0.5, 0.7, -0.3, -0.9]  # example of other feature
})

# Columns to scale
scale_cols = ['temp_max_C', 'precip_mm', 'wind_speed_max_m_s']

# Initialize scaler
scaler = MinMaxScaler()

# Fit 

import matplotlib.pyplot as plt

# Create box plot
plt.figure(figsize=(14, 6))  # Optional: enlarge canvas
plt.boxplot(new_df['Power_Load_kW'], patch_artist=True, boxprops=dict(facecolor='lightblue', color='blue'),
            medianprops=dict(color='red'), whiskerprops=dict(color='black'),
            capprops=dict(color='black'), flierprops=dict(marker='o', color='orange', alpha=0.5))

# Add title and labels
plt.title('Box Plot Example', fontsize=16)
plt.ylabel('Value', fontsize=14)