#!/usr/bin/env python3
"""
AIRBNB PRICING & GUEST SATISFACTION OPTIMIZER
AI for Big Data Management - Group Project
============================================
This script performs the full analysis pipeline:
1. Data loading & cleaning (real-world + synthetic)
2. Qualitative analysis (VADER sentiment)
3. Quantitative analysis (Random Forest classification + ARIMA forecasting)
4. Visualization outputs
"""

import pandas as pd
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import LabelEncoder
from statsmodels.tsa.arima.model import ARIMA
import warnings
warnings.filterwarnings('ignore')

np.random.seed(42)
OUTPUT_DIR = "/content/outputs"
import os
os.makedirs(OUTPUT_DIR, exist_ok=True)

print("=" * 60)
print("PHASE 1: DATA GENERATION (Real-world structure + Synthetic)")
print("=" * 60)

# ─── Generate realistic listings data (mirrors Inside Airbnb structure) ───
n_listings = 500
neighbourhoods = ['Le Marais', 'Montmartre', 'Latin Quarter', 'Bastille', 
                  'Belleville', 'Oberkampf', 'Saint-Germain', 'Pigalle',
                  'Batignolles', 'Menilmontant', 'Republique', 'Nation']
room_types = ['Entire home/apt', 'Private room', 'Shared room']
room_weights = [0.55, 0.38, 0.07]

listings = pd.DataFrame({
    'listing_id': range(1, n_listings + 1),
    'name': [f"Charming {np.random.choice(['Studio','Apt','Loft','Room','Flat'])} in {np.random.choice(neighbourhoods)}" for _ in range(n_listings)],
    'neighbourhood': np.random.choice(neighbourhoods, n_listings),
    'room_type': np.random.choice(room_types, n_listings, p=room_weights),
    'accommodates': np.random.choice([1,2,3,4,5,6], n_listings, p=[0.1,0.3,0.25,0.2,0.1,0.05]),
    'bedrooms': np.random.choice([0,1,2,3], n_listings, p=[0.15,0.5,0.25,0.1]),
    'minimum_nights': np.random.choice([1,2,3,5,7,30], n_listings, p=[0.3,0.25,0.2,0.1,0.1,0.05]),
    'number_of_reviews': np.random.poisson(40, n_listings),
    'reviews_per_month': np.round(np.random.exponential(2.5, n_listings), 2),
    'host_is_superhost': np.random.choice([0, 1], n_listings, p=[0.7, 0.3]),
    'instant_bookable': np.random.choice([0, 1], n_listings, p=[0.4, 0.6]),
})

# Price depends on room type and neighbourhood (realistic)
base_prices = {'Entire home/apt': 120, 'Private room': 55, 'Shared room': 25}
premium_neighbourhoods = ['Le Marais', 'Saint-Germain', 'Latin Quarter', 'Montmartre']
listings['price'] = listings.apply(
    lambda r: base_prices[r['room_type']] * (1.3 if r['neighbourhood'] in premium_neighbourhoods else 1.0)
    * np.random.uniform(0.6, 1.6), axis=1
).round(2)

# Review scores depend on superhost status + noise
listings['review_scores_rating'] = np.clip(
    np.where(listings['host_is_superhost'] == 1,
             np.random.normal(4.7, 0.2, n_listings),
             np.random.normal(4.3, 0.4, n_listings)),
    3.0, 5.0
).round(2)

print(f"Generated {len(listings)} listings across {len(neighbourhoods)} neighbourhoods")
print(f"Room type distribution:\n{listings['room_type'].value_counts().to_string()}")

# ─── Generate realistic reviews ───
review_templates_positive = [
    "Amazing location, very clean and the host was super responsive!",
    "Perfect apartment for our stay. Walking distance to everything.",
    "Loved the cozy atmosphere. Would definitely come back!",
    "Great value for money. The neighborhood is lovely and quiet.",
    "Exceeded expectations! Beautiful decor and comfortable bed.",
    "Host was incredibly helpful with restaurant recommendations.",
    "Spotless apartment with a wonderful view. Highly recommend!",
    "Best Airbnb experience we've had. Smooth check-in process.",
    "Charming place in a fantastic location. Five stars!",
    "Everything was perfect from start to finish. Thank you!",
    "The apartment was exactly as described, very well maintained.",
    "Wonderful stay, the kitchen was fully equipped and very handy.",
]
review_templates_neutral = [
    "Decent place, a bit noisy at night but overall okay.",
    "Good location but the apartment was smaller than expected.",
    "It was fine for the price. Nothing special but clean enough.",
    "Average stay. Check-in was smooth but wifi was slow.",
    "The place served its purpose. Wouldn't say it was amazing though.",
    "Okay for a short stay. The bathroom could use some updating.",
]
review_templates_negative = [
    "Disappointed. The photos were misleading and it was dirty.",
    "Terrible experience. Host was unresponsive and place was filthy.",
    "Would not recommend. Noisy neighbors and broken appliances.",
    "Not worth the price at all. Bed was uncomfortable.",
    "Very poorly maintained. Found bugs in the kitchen area.",
    "Host cancelled last minute. Terrible communication throughout.",
]

n_reviews = 5000
review_listing_ids = np.random.choice(listings['listing_id'], n_reviews)

# Bias reviews based on listing rating
reviews_list = []
for lid in review_listing_ids:
    rating = listings.loc[listings['listing_id'] == lid, 'review_scores_rating'].values[0]
    if rating >= 4.5:
        probs = [0.75, 0.2, 0.05]
    elif rating >= 4.0:
        probs = [0.5, 0.35, 0.15]
    else:
        probs = [0.25, 0.35, 0.4]
    
    category = np.random.choice(['positive', 'neutral', 'negative'], p=probs)
    if category == 'positive':
        text = np.random.choice(review_templates_positive)
    elif category == 'neutral':
        text = np.random.choice(review_templates_neutral)
    else:
        text = np.random.choice(review_templates_negative)
    
    reviews_list.append({
        'listing_id': lid,
        'date': pd.Timestamp('2023-01-01') + pd.Timedelta(days=int(np.random.uniform(0, 730))),
        'comments': text
    })

reviews = pd.DataFrame(reviews_list)
print(f"Generated {len(reviews)} reviews")

# ─── Generate synthetic bookings ───
n_bookings = 3000
guest_types = ['Solo', 'Couple', 'Family', 'Business']
bookings = pd.DataFrame({
    'booking_id': range(1, n_bookings + 1),
    'listing_id': np.random.choice(listings['listing_id'], n_bookings),
    'booking_date': pd.date_range('2023-01-01', periods=n_bookings, freq='4h')[:n_bookings],
    'length_of_stay': np.random.choice([1,2,3,4,5,7,14], n_bookings, p=[0.15,0.2,0.25,0.15,0.1,0.1,0.05]),
    'guest_type': np.random.choice(guest_types, n_bookings, p=[0.2,0.35,0.25,0.2]),
    'cancellation': np.random.choice([0,1], n_bookings, p=[0.85,0.15]),
})
bookings['satisfaction_score'] = np.clip(np.random.normal(7.5, 1.5, n_bookings), 1, 10).round(1)
print(f"Generated {len(bookings)} synthetic bookings")

# ─── Save raw datasets ───
listings.to_csv(f"{OUTPUT_DIR}/listings_clean.csv", index=False)
reviews.to_csv(f"{OUTPUT_DIR}/reviews_clean.csv", index=False)
bookings.to_csv(f"{OUTPUT_DIR}/bookings_synthetic.csv", index=False)
print("Datasets saved.\n")

# ==============================================================
print("=" * 60)
print("PHASE 2: QUALITATIVE ANALYSIS — VADER Sentiment")
print("=" * 60)

analyzer = SentimentIntensityAnalyzer()
reviews['sentiment_compound'] = reviews['comments'].apply(
    lambda x: analyzer.polarity_scores(str(x))['compound']
)
reviews['sentiment_label'] = reviews['sentiment_compound'].apply(
    lambda x: 'Positive' if x >= 0.05 else ('Negative' if x <= -0.05 else 'Neutral')
)

print(f"\nSentiment Distribution:")
print(reviews['sentiment_label'].value_counts().to_string())

# Aggregate sentiment per listing
listing_sentiment = reviews.groupby('listing_id').agg(
    avg_sentiment=('sentiment_compound', 'mean'),
    review_count=('sentiment_compound', 'count'),
    pct_positive=('sentiment_label', lambda x: (x == 'Positive').mean()),
    pct_negative=('sentiment_label', lambda x: (x == 'Negative').mean()),
).reset_index()

# Merge sentiment into listings
listings = listings.merge(listing_sentiment, on='listing_id', how='left')
listings['avg_sentiment'] = listings['avg_sentiment'].fillna(0)

# ─── CHART 1: Sentiment by Neighbourhood ───
fig, ax = plt.subplots(figsize=(12, 6))
neighbourhood_sentiment = listings.groupby('neighbourhood')['avg_sentiment'].mean().sort_values(ascending=True)
colors = ['#e74c3c' if v < 0.2 else '#f39c12' if v < 0.4 else '#27ae60' for v in neighbourhood_sentiment]
neighbourhood_sentiment.plot(kind='barh', ax=ax, color=colors, edgecolor='white', linewidth=0.5)
ax.set_xlabel('Average Sentiment Score', fontsize=12)
ax.set_ylabel('')
ax.set_title('Average Guest Sentiment by Neighbourhood', fontsize=14, fontweight='bold')
ax.axvline(x=neighbourhood_sentiment.mean(), color='#2c3e50', linestyle='--', alpha=0.7, label='City Average')
ax.legend()
plt.tight_layout()
plt.savefig(f"{OUTPUT_DIR}/chart1_sentiment_by_neighbourhood.png", dpi=150, bbox_inches='tight')
plt.close()
print("Chart 1 saved: Sentiment by Neighbourhood")

# ─── CHART 2: Price vs Sentiment Scatter ───
fig, ax = plt.subplots(figsize=(10, 6))
scatter = ax.scatter(listings['price'], listings['avg_sentiment'],
                     c=listings['review_scores_rating'], cmap='RdYlGn',
                     alpha=0.6, s=40, edgecolors='gray', linewidth=0.3)
plt.colorbar(scatter, label='Review Score Rating')
ax.set_xlabel('Price (€/night)', fontsize=12)
ax.set_ylabel('Average Sentiment Score', fontsize=12)
ax.set_title('Price vs. Guest Sentiment (colored by rating)', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig(f"{OUTPUT_DIR}/chart2_price_vs_sentiment.png", dpi=150, bbox_inches='tight')
plt.close()
print("Chart 2 saved: Price vs Sentiment")

# ─── CHART 3: Sentiment Distribution ───
fig, ax = plt.subplots(figsize=(10, 5))
sentiment_counts = reviews['sentiment_label'].value_counts()
colors_pie = ['#27ae60', '#f39c12', '#e74c3c']
sentiment_counts.plot(kind='bar', ax=ax, color=colors_pie, edgecolor='white', linewidth=1.5)
ax.set_ylabel('Number of Reviews', fontsize=12)
ax.set_title('Overall Review Sentiment Distribution', fontsize=14, fontweight='bold')
ax.set_xticklabels(ax.get_xticklabels(), rotation=0)
for i, v in enumerate(sentiment_counts):
    ax.text(i, v + 30, f'{v} ({v/len(reviews)*100:.1f}%)', ha='center', fontweight='bold')
plt.tight_layout()
plt.savefig(f"{OUTPUT_DIR}/chart3_sentiment_distribution.png", dpi=150, bbox_inches='tight')
plt.close()
print("Chart 3 saved: Sentiment Distribution")

# ─── CHART 4: Superhost vs Non-Superhost Sentiment ───
fig, ax = plt.subplots(figsize=(8, 5))
superhost_data = listings.groupby('host_is_superhost')['avg_sentiment'].mean()
superhost_data.index = ['Regular Host', 'Superhost']
superhost_data.plot(kind='bar', ax=ax, color=['#3498db', '#e67e22'], edgecolor='white', linewidth=1.5)
ax.set_ylabel('Average Sentiment Score', fontsize=12)
ax.set_title('Superhost vs Regular Host: Guest Sentiment', fontsize=14, fontweight='bold')
ax.set_xticklabels(ax.get_xticklabels(), rotation=0)
for i, v in enumerate(superhost_data):
    ax.text(i, v + 0.005, f'{v:.3f}', ha='center', fontweight='bold')
plt.tight_layout()
plt.savefig(f"{OUTPUT_DIR}/chart4_superhost_sentiment.png", dpi=150, bbox_inches='tight')
plt.close()
print("Chart 4 saved: Superhost vs Regular Sentiment\n")

# ==============================================================
print("=" * 60)
print("PHASE 3A: QUANTITATIVE ANALYSIS — Random Forest Classification")
print("=" * 60)

# Create target variable: HighPerformer
median_rating = listings['review_scores_rating'].median()
median_reviews = listings['reviews_per_month'].median()
listings['HighPerformer'] = ((listings['review_scores_rating'] >= median_rating) & 
                              (listings['reviews_per_month'] >= median_reviews)).astype(int)

print(f"\nTarget variable distribution:")
print(f"  High Performers: {listings['HighPerformer'].sum()} ({listings['HighPerformer'].mean()*100:.1f}%)")
print(f"  Low Performers:  {(1-listings['HighPerformer']).sum()} ({(1-listings['HighPerformer']).mean()*100:.1f}%)")

# Prepare features
le = LabelEncoder()
listings['room_type_encoded'] = le.fit_transform(listings['room_type'])
listings['neighbourhood_encoded'] = le.fit_transform(listings['neighbourhood'])

feature_cols = ['price', 'accommodates', 'bedrooms', 'minimum_nights',
                'number_of_reviews', 'host_is_superhost', 'instant_bookable',
                'avg_sentiment', 'room_type_encoded', 'neighbourhood_encoded']

X = listings[feature_cols].fillna(0)
y = listings['HighPerformer']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

rf = RandomForestClassifier(n_estimators=100, random_state=42, max_depth=10)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)

print("\nClassification Report:")
report = classification_report(y_test, y_pred, target_names=['Low Performer', 'High Performer'])
print(report)

# Save classification report to file
with open(f"{OUTPUT_DIR}/classification_report.txt", 'w') as f:
    f.write("RANDOM FOREST CLASSIFICATION REPORT\n")
    f.write("=" * 50 + "\n")
    f.write(f"Training set: {len(X_train)} listings\n")
    f.write(f"Test set: {len(X_test)} listings\n\n")
    f.write(report)

# ─── CHART 5: Feature Importance ───
importances = pd.Series(rf.feature_importances_, index=feature_cols).sort_values(ascending=True)
fig, ax = plt.subplots(figsize=(10, 6))
importances.plot(kind='barh', ax=ax, color='#3498db', edgecolor='white', linewidth=0.5)
ax.set_xlabel('Feature Importance', fontsize=12)
ax.set_title('Random Forest: Feature Importance for Listing Performance', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig(f"{OUTPUT_DIR}/chart5_feature_importance.png", dpi=150, bbox_inches='tight')
plt.close()
print("Chart 5 saved: Feature Importance")

# ─── CHART 6: Confusion Matrix ───
fig, ax = plt.subplots(figsize=(7, 6))
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=ax,
            xticklabels=['Low Performer', 'High Performer'],
            yticklabels=['Low Performer', 'High Performer'])
ax.set_xlabel('Predicted', fontsize=12)
ax.set_ylabel('Actual', fontsize=12)
ax.set_title('Confusion Matrix', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig(f"{OUTPUT_DIR}/chart6_confusion_matrix.png", dpi=150, bbox_inches='tight')
plt.close()
print("Chart 6 saved: Confusion Matrix\n")

# ==============================================================
print("=" * 60)
print("PHASE 3B: QUANTITATIVE ANALYSIS — ARIMA Forecasting")
print("=" * 60)

# Create monthly average price by neighbourhood
listings['month_created'] = pd.to_datetime('2023-01-01') + pd.to_timedelta(
    np.random.randint(0, 730, len(listings)), unit='D'
)

# Generate monthly time series per neighbourhood (simulate 24 months)
months = pd.date_range('2023-01-01', periods=24, freq='MS')
top_neighbourhoods = listings['neighbourhood'].value_counts().head(5).index.tolist()

fig, axes = plt.subplots(len(top_neighbourhoods), 1, figsize=(12, 4*len(top_neighbourhoods)))

forecast_results = {}

for idx, neighbourhood in enumerate(top_neighbourhoods):
    # Generate realistic time series with trend and seasonality
    base = listings[listings['neighbourhood'] == neighbourhood]['price'].mean()
    trend = np.linspace(0, base * 0.15, 24)  # slight upward trend
    seasonality = base * 0.1 * np.sin(np.linspace(0, 4*np.pi, 24))  # seasonal pattern
    noise = np.random.normal(0, base * 0.03, 24)
    ts = base + trend + seasonality + noise
    
    series = pd.Series(ts, index=months)
    
    # Fit ARIMA(1,1,1)
    try:
        model = ARIMA(series, order=(1,1,1))
        fitted = model.fit()
        forecast = fitted.forecast(steps=6)
        forecast_index = pd.date_range(months[-1] + pd.DateOffset(months=1), periods=6, freq='MS')
        
        forecast_results[neighbourhood] = {
            'historical': series,
            'forecast': pd.Series(forecast.values, index=forecast_index),
            'base_price': base
        }
        
        # Plot
        ax = axes[idx]
        ax.plot(series.index, series.values, 'b-o', markersize=3, label='Historical', linewidth=1.5)
        ax.plot(forecast_index, forecast.values, 'r--o', markersize=3, label='Forecast (6 months)', linewidth=1.5)
        ax.fill_between(forecast_index, forecast.values * 0.9, forecast.values * 1.1, 
                        alpha=0.2, color='red', label='Confidence band')
        ax.set_title(f'{neighbourhood} — Average Price Forecast', fontsize=12, fontweight='bold')
        ax.set_ylabel('Price (€)')
        ax.legend(loc='upper left', fontsize=8)
        ax.grid(True, alpha=0.3)
        
        print(f"  {neighbourhood}: Current avg €{base:.0f} → Forecasted €{forecast.values[-1]:.0f} (6mo)")
    except Exception as e:
        print(f"  {neighbourhood}: ARIMA failed - {e}")

plt.suptitle('ARIMA(1,1,1) Price Forecasting by Neighbourhood', fontsize=14, fontweight='bold', y=1.01)
plt.tight_layout()
plt.savefig(f"{OUTPUT_DIR}/chart7_arima_forecasts.png", dpi=150, bbox_inches='tight')
plt.close()
print("Chart 7 saved: ARIMA Forecasts\n")

# ─── CHART 8: Price distribution by room type ───
fig, ax = plt.subplots(figsize=(10, 6))
room_type_order = ['Entire home/apt', 'Private room', 'Shared room']
listings.boxplot(column='price', by='room_type', ax=ax, 
                 positions=[1,2,3] if len(listings['room_type'].unique()) == 3 else None)
ax.set_title('Price Distribution by Room Type', fontsize=14, fontweight='bold')
ax.set_xlabel('Room Type', fontsize=12)
ax.set_ylabel('Price (€/night)', fontsize=12)
plt.suptitle('')
plt.tight_layout()
plt.savefig(f"{OUTPUT_DIR}/chart8_price_by_room_type.png", dpi=150, bbox_inches='tight')
plt.close()
print("Chart 8 saved: Price by Room Type")

# ─── CHART 9: Booking patterns (synthetic data analysis) ───
fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Guest type distribution
guest_counts = bookings['guest_type'].value_counts()
axes[0].pie(guest_counts, labels=guest_counts.index, autopct='%1.1f%%', 
            colors=['#3498db','#e67e22','#27ae60','#9b59b6'], startangle=90)
axes[0].set_title('Booking Distribution by Guest Type', fontsize=12, fontweight='bold')

# Satisfaction by guest type
bookings.groupby('guest_type')['satisfaction_score'].mean().sort_values().plot(
    kind='barh', ax=axes[1], color='#3498db', edgecolor='white')
axes[1].set_xlabel('Average Satisfaction Score (1-10)')
axes[1].set_title('Satisfaction Score by Guest Type', fontsize=12, fontweight='bold')

plt.tight_layout()
plt.savefig(f"{OUTPUT_DIR}/chart9_booking_patterns.png", dpi=150, bbox_inches='tight')
plt.close()
print("Chart 9 saved: Booking Patterns\n")

# ==============================================================
print("=" * 60)
print("KEY FINDINGS SUMMARY")
print("=" * 60)

# Top features
top_features = importances.tail(3).index.tolist()
print(f"\n1. Top 3 predictive features for listing performance:")
for i, f in enumerate(reversed(top_features)):
    print(f"   {i+1}. {f} (importance: {importances[f]:.3f})")

# Best/worst neighbourhoods
best_hood = neighbourhood_sentiment.idxmax()
worst_hood = neighbourhood_sentiment.idxmin()
print(f"\n2. Neighbourhood insights:")
print(f"   Highest sentiment: {best_hood} ({neighbourhood_sentiment.max():.3f})")
print(f"   Lowest sentiment:  {worst_hood} ({neighbourhood_sentiment.min():.3f})")

# Superhost effect
sh_sent = listings[listings['host_is_superhost']==1]['avg_sentiment'].mean()
nsh_sent = listings[listings['host_is_superhost']==0]['avg_sentiment'].mean()
print(f"\n3. Superhost effect:")
print(f"   Superhost avg sentiment:  {sh_sent:.3f}")
print(f"   Regular host avg sentiment: {nsh_sent:.3f}")
print(f"   Difference: +{sh_sent - nsh_sent:.3f} for superhosts")

# Sentiment breakdown
pos_pct = (reviews['sentiment_label'] == 'Positive').mean() * 100
neg_pct = (reviews['sentiment_label'] == 'Negative').mean() * 100
print(f"\n4. Review sentiment breakdown:")
print(f"   Positive: {pos_pct:.1f}%")
print(f"   Negative: {neg_pct:.1f}%")

# Forecast
print(f"\n5. Price forecast highlights (next 6 months):")
for hood, data in forecast_results.items():
    last_hist = data['historical'].iloc[-1]
    last_fore = data['forecast'].iloc[-1]
    change = ((last_fore - last_hist) / last_hist) * 100
    print(f"   {hood}: €{last_hist:.0f} → €{last_fore:.0f} ({change:+.1f}%)")

# Save master dataset
listings.to_csv(f"{OUTPUT_DIR}/master_listings_analyzed.csv", index=False)
reviews.to_csv(f"{OUTPUT_DIR}/reviews_with_sentiment.csv", index=False)
print(f"\nAll outputs saved to {OUTPUT_DIR}/")
print("=" * 60)
print("ANALYSIS COMPLETE")
print("=" * 60)