app.py

import gradio as gr
import pandas as pd
import joblib
from huggingface_hub import hf_hub_download
from datetime import datetime

# --- 1. Download and Load Models ---
print("Downloading models from Hugging Face Hub...")
REPO_ID = "matanzig/flight-price-prediction"

reg_model = joblib.load(hf_hub_download(repo_id=REPO_ID, filename="flight_price_rf_model.pkl"))
cls_model = joblib.load(hf_hub_download(repo_id=REPO_ID, filename="flight_price_classifier_rf.pkl"))


COLUMNS = [
    'startingAirport', 'destinationAirport', 'isBasicEconomy', 'isRefundable', 
    'isNonStop', 'seatsRemaining', 'totalTravelDistance', 'month', 
    'days_until_flight', 'travelDuration_mins', 'primary_airline', 
    'primary_cabin', 'departure_hour', 'day_of_week_Monday', 
    'day_of_week_Saturday', 'day_of_week_Sunday', 'day_of_week_Thursday', 
    'day_of_week_Tuesday', 'day_of_week_Wednesday', 'is_weekend', 
    'days_until_flight_squared', 'cluster_group_1', 'cluster_group_2', 
    'cluster_group_3', 'cluster_group_1', 'cluster_group_2', 'cluster_group_3'
]


AIRLINE_MAPPING = {
    "Alaska Airlines": 0, "American Airlines": 1, "Boutique Air": 2, "Cape Air": 3,
    "Contour Airlines": 4, "Delta": 5, "Frontier Airlines": 6, "Hawaiian Airlines": 7,
    "JetBlue Airways": 8, "Key Lime Air": 9, "Southern Airways Express": 10,
    "Spirit Airlines": 11, "Sun Country Airlines": 12, "United": 13
}

# --- 2. Prediction Engine ---
def predict_flight_price(flight_date, distance, duration, days_until, seats, airline_name, seen_price, is_nonstop, is_basic_economy):
  
    try:
        dt = pd.to_datetime(flight_date, format="%Y-%m-%d")
        month_val = dt.month
        day_name = dt.day_name()
        is_weekend = 1 if day_name in ['Saturday', 'Sunday'] else 0
    except ValueError:
        raise gr.Error("❌ Invalid Date Format! Please use exactly YYYY-MM-DD (e.g., 2026-07-15).")

    
    scaled_month = (month_val - 7.0) / 2.0
    
   
    scaled_days = (days_until - 30) / 15.0
    scaled_days_sq = scaled_days ** 2
    
    
    airline_id = AIRLINE_MAPPING.get(airline_name, 5) 

    
    row_data = [
        4, 5, 
        int(is_basic_economy), 0, int(is_nonstop), int(seats), float(distance),
        float(scaled_month), float(scaled_days), float(duration), int(airline_id), 1, -0.32,
        day_name == 'Monday', day_name == 'Saturday', day_name == 'Sunday',
        day_name == 'Thursday', day_name == 'Tuesday', day_name == 'Wednesday',
        is_weekend, float(scaled_days_sq),
        False, False, False, False, False, False 
    ]
    
    
    input_df = pd.DataFrame([row_data], columns=COLUMNS)
    
    
    reg_prediction = reg_model.predict(input_df)[0]
    cls_prediction = cls_model.predict(input_df)[0]
    
    
    tier_mapping = {0: "Budget Expected 🟢", 1: "Standard Expected 🟡", 2: "Premium Expected 🔴"}
    expected_tier = tier_mapping.get(cls_prediction, "Unknown")
    
    
    if seen_price > 0:
        diff = seen_price - reg_prediction
        if diff < -25:
            deal_analysis = f"🔥 Amazing Deal! This ticket is ${abs(diff):.0f} CHEAPER than the AI average. Book it now!"
        elif diff > 25:
            deal_analysis = f"⚠️ Overpriced! This ticket is ${diff:.0f} MORE EXPENSIVE than it should be. Wait or find another flight."
        else:
            deal_analysis = f"⚖️ Fair Market Price. The price you found perfectly matches our algorithm's baseline."
    else:
        deal_analysis = "Enter a 'Seen Price' above to get an instant deal analysis."
        
    return f"${reg_prediction:.2f}", expected_tier, deal_analysis


# --- 3. Gradio Interface (UI) ---
DESCRIPTION = """
**Welcome to the US Flight Price Predictor AI!** 
Enter your flight details below to get an AI-powered baseline fare and deal analysis.

⚠️ **Model Limitations:** This algorithm was trained exclusively on Expedia data spanning from **April to October 2022**. It is highly optimized for summer and early fall travel dynamics, but does *not* account for major winter holiday price surges (e.g., Thanksgiving, Christmas) or extreme macroeconomic inflation events beyond that window.
"""

interface = gr.Interface(
    fn=predict_flight_price,
    inputs=[
        gr.Textbox(value="2026-07-15", label="Flight Date (YYYY-MM-DD)", placeholder="e.g., 2026-08-01"),
        gr.Slider(minimum=100, maximum=3000, step=50, value=1300, label="Travel Distance (Miles)"),
        gr.Slider(minimum=60, maximum=800, step=10, value=400, label="Travel Duration (Minutes)"),
        gr.Slider(minimum=0, maximum=90, step=1, value=45, label="How many days in advance are you booking?"),
        gr.Slider(minimum=0, maximum=10, step=1, value=5, label="Seats Remaining"),
        gr.Dropdown(choices=list(AIRLINE_MAPPING.keys()), value="Delta", label="Airline"),
        gr.Number(value=0, label="Price you found online ($) - Optional"), 
        gr.Checkbox(label="Is Non-Stop (Direct Flight)?"),
        gr.Checkbox(label="Is Basic Economy?")
    ],
    outputs=[
        gr.Textbox(label="🤖 AI Predicted Fair Price"),
        gr.Textbox(label="📊 Expected Market Tier (Classifier)"),
        gr.Textbox(label="💡 Personal Deal Analysis")
    ],
    title="✈️ Smart Flight Price Predictor",
    description=DESCRIPTION,
    theme="default"
)

if __name__ == "__main__":
    interface.launch()