app.py

import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("hotel_final_dataset.csv")

hotels = sorted(df["hotel_name"].dropna().unique().tolist())

def analyze(hotel):
    data = df[df["hotel_name"] == hotel].copy()

    avg_rating = round(data["avg_rating"].mean(), 2)
    sentiment = round(data["sentiment_score"].mean(), 2)
    occupancy = round(data["occupancy_rate"].mean(), 2)
    price = round(data["price_per_night"].mean(), 2)
    demand = round(data["demand_index"].mean(), 2)

    base_rec = data["pricing_recommendation"].mode().iloc[0]

    if sentiment < 0:
        recommendation = f"{base_rec} — but improve customer satisfaction first"
    elif occupancy > 0.95:
        recommendation = f"{base_rec} — strong demand supports price increase"
    else:
        recommendation = base_rec

    fig, ax = plt.subplots(figsize=(8, 4))
    fig.patch.set_facecolor('#111111')
    ax.set_facecolor('#111111')

    ax.plot(data["month"], data["booking_count"], marker="o")
    ax.set_title("Booking Trend", color='white')
    ax.set_xlabel("Month", color='white')
    ax.set_ylabel("Booking Count", color='white')
    ax.tick_params(colors='white')
    plt.xticks(rotation=45)
    plt.tight_layout()

    return avg_rating, sentiment, occupancy, price, demand, recommendation, fig

with gr.Blocks() as demo:
    gr.Markdown("# AI-Driven Hotel Pricing Dashboard")
    gr.Markdown("Analyze hotel performance using sentiment, demand, pricing, and booking trends.")

    with gr.Row():
        hotel_input = gr.Dropdown(choices=hotels, label="Select Hotel", value=hotels[0])

    with gr.Row():
        avg_rating_output = gr.Textbox(label="Average Rating")
        sentiment_output = gr.Textbox(label="Customer Sentiment Score")
        occupancy_output = gr.Textbox(label="Occupancy Rate")

    with gr.Row():
        price_output = gr.Textbox(label="Average Price per Night")
        demand_output = gr.Textbox(label="Demand Level Index")
        recommendation_output = gr.Textbox(label="Pricing Recommendation")

    plot_output = gr.Plot(label="Booking Trend")

    submit_btn = gr.Button("Run Analysis")

    submit_btn.click(
        fn=analyze,
        inputs=hotel_input,
        outputs=[
            avg_rating_output,
            sentiment_output,
            occupancy_output,
            price_output,
            demand_output,
            recommendation_output,
            plot_output
        ]
    )

demo.launch()