import streamlit as st
import pandas as pd
import numpy as np
import joblib
import matplotlib.pyplot as plt
import os
import openai
from sklearn.preprocessing import LabelEncoder
import requests 
from io import BytesIO
import gdown
from PIL import Image
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
import torch
from datetime import datetime

# --- Set page configuration ---
st.set_page_config(
    page_title="The Guide",
    page_icon="🚗",
    layout="wide",
    initial_sidebar_state="expanded"
)

# --- Custom CSS for better styling ---

st.markdown("""
    <style>
    /* Base styles */
    * {
        color: black !important;
    }
    
    /* Streamlit specific input elements */
    .stSelectbox, 
    .stNumberInput, 
    .stTextInput {
        color: black !important;
    }
    
    /* Dropdown and select elements */
    select option,
    .streamlit-selectbox option,
    .stSelectbox > div[data-baseweb="select"] > div,
    .stSelectbox > div > div > div {
        color: black !important;
        background-color: white !important;
    }
    
    /* Input fields */
    input, 
    .stNumberInput > div > div > input {
        color: black !important;
    }
    
    /* Text elements */
    div.row-widget.stSelectbox > div,
    div.row-widget.stSelectbox > div > div > div,
    .streamlit-expanderContent,
    .stMarkdown,
    p, span, label {
        color: black !important;
    }
    
    /* Keep button text white */
    .stButton > button {
        color: white !important;
        background-color: #FF4B4B;
    }
    
    /* Specific styling for select boxes */
    div[data-baseweb="select"] {
        color: black !important;
        background-color: white !important;
    }
    
    div[data-baseweb="select"] * {
        color: black !important;
    }
    
    /* Style for the selected option */
    div[data-baseweb="select"] > div:first-child {
        color: black !important;
        background-color: white !important;
    }
    
    /* Dropdown menu items */
    [role="listbox"] {
        background-color: white !important;
    }
    
    [role="listbox"] [role="option"] {
        color: black !important;
    }
    
    /* Number input specific styling */
    input[type="number"] {
        color: black !important;
        background-color: white !important;
    }
    
    .stNumberInput div[data-baseweb="input"] {
        background-color: white !important;
    }
    
    /* Headers */
    h1, h2, h3, h4, h5, h6 {
        color: black !important;
    }
    </style>
""", unsafe_allow_html=True)

# --- Cache functions ---
def create_brand_categories():
    return {
        'luxury_brands': {
            'rolls-royce': (300000, 600000),
            'bentley': (200000, 500000),
            'lamborghini': (250000, 550000),
            'ferrari': (250000, 600000),
            'mclaren': (200000, 500000),
            'aston-martin': (150000, 400000),
            'maserati': (100000, 300000)
        },
        'premium_brands': {
            'porsche': (60000, 150000),
            'bmw': (40000, 90000),
            'mercedes-benz': (45000, 95000),
            'audi': (35000, 85000),
            'lexus': (40000, 80000),
            'jaguar': (45000, 90000),
            'land-rover': (40000, 90000),
            'volvo': (35000, 75000),
            'infiniti': (35000, 70000),
            'cadillac': (40000, 85000),
            'tesla': (40000, 100000)
        },
        'mid_tier_brands': {
            'acura': (30000, 50000),
            'lincoln': (35000, 65000),
            'buick': (25000, 45000),
            'chrysler': (25000, 45000),
            'alfa-romeo': (35000, 60000),
            'genesis': (35000, 60000)
        },
        'standard_brands': {
            'toyota': (20000, 35000),
            'honda': (20000, 35000),
            'volkswagen': (20000, 35000),
            'mazda': (20000, 32000),
            'subaru': (22000, 35000),
            'hyundai': (18000, 32000),
            'kia': (17000, 30000),
            'ford': (20000, 40000),
            'chevrolet': (20000, 38000),
            'gmc': (25000, 45000),
            'jeep': (25000, 45000),
            'dodge': (22000, 40000),
            'ram': (25000, 45000),
            'nissan': (18000, 32000)
        },
        'economy_brands': {
            'mitsubishi': (15000, 25000),
            'suzuki': (12000, 22000),
            'fiat': (15000, 25000),
            'mini': (20000, 35000),
            'smart': (15000, 25000)
        },
        'discontinued_brands': {
            'pontiac': (5000, 15000),
            'saturn': (4000, 12000),
            'mercury': (4000, 12000),
            'oldsmobile': (3000, 10000),
            'plymouth': (3000, 10000),
            'saab': (5000, 15000)
        }
    }

@st.cache_resource
def download_file_from_google_drive(file_id):
    """Downloads a file from Google Drive using gdown."""
    url = f"https://drive.google.com/uc?id={file_id}"
    try:
        with st.spinner('Downloading from Google Drive...'):
            output = f"temp_{file_id}.pkl"
            gdown.download(url, output, quiet=False)
            
            with open(output, 'rb') as f:
                content = f.read()
            
            # Clean up the temporary file
            os.remove(output)
            return content
            
    except Exception as e:
        st.error(f"Error downloading from Google Drive: {str(e)}")
        raise e

@st.cache_data
def load_datasets():
    """Load the dataset from Google Drive."""
    dataset_file_id = "17dj7yW22YsIfp-tvXQFCitKmLFw5IuAv"
    
    try:
        with st.spinner('Loading dataset...'):
            content = download_file_from_google_drive(dataset_file_id)
            # Use BytesIO to read the CSV content
            original_data = pd.read_csv(BytesIO(content), low_memory=False)
            
            # Ensure column names match the model's expectations
            original_data.columns = original_data.columns.str.strip().str.capitalize()
            return original_data
    except Exception as e:
        st.error(f"Error loading dataset: {str(e)}")
        raise e

@st.cache_resource
def load_model_and_encodings():
    """Load model from Google Drive and create encodings."""
    model_file_id = "1ynnVEH7rmAjfe-jH8GOEmTJc6ml8dTi_"
    
    try:
        # Show loading message
        with st.spinner('Loading model...'):
            model_content = download_file_from_google_drive(model_file_id)
            model = joblib.load(BytesIO(model_content))
        
        # Load data for encodings
        original_data = load_datasets()
        
        # Create fresh encoders from data
        label_encoders = {}
        categorical_features = ['Make', 'model', 'condition', 'fuel', 'title_status', 
                              'transmission', 'drive', 'size', 'type', 'paint_color']
        
        for feature in categorical_features:
            if feature in original_data.columns:
                le = LabelEncoder()
                unique_values = original_data[feature].fillna('unknown').str.strip().unique()
                le.fit(unique_values)
                label_encoders[feature.lower()] = le
        
        return model, label_encoders
    except Exception as e:
        st.error(f"Error loading model: {str(e)}")
        raise e


# --- Load data and models ---
try:
    original_data = load_datasets()
    model, label_encoders = load_model_and_encodings()  # Using the new function
except Exception as e:
    st.error(f"Error loading data or models: {str(e)}")
    st.stop()

# --- Define categorical and numeric features ---
# From model.py
# --- Define features ---
numeric_features = ['year', 'odometer', 'age', 'age_squared', 'mileage_per_year']
# Update the categorical features list to use lowercase
categorical_features = ['make', 'model', 'condition', 'fuel', 'title_status', 
                       'transmission', 'drive', 'size', 'type', 'paint_color']
required_features = numeric_features + categorical_features

# --- Feature engineering functions ---
def create_features(df):
    df = df.copy()
    current_year = 2024
    df['age'] = current_year - df['year']
    df['age_squared'] = df['age'] ** 2
    df['mileage_per_year'] = np.clip(df['odometer'] / (df['age'] + 1), 0, 200000)
    return df

def prepare_input(input_dict, label_encoders):
    # Convert None values to 'unknown' for safe handling
    input_dict = {k: v if v is not None else 'unknown' for k, v in input_dict.items()}
    
    # Convert input dictionary to DataFrame
    input_df = pd.DataFrame([input_dict])
    
    # Ensure columns match the model's expected casing
    feature_name_mapping = {
        "make": "Make",  # Match casing for 'Make'
        "model": "Model",  # Match casing for 'Model'
        "condition": "Condition",
        "fuel": "Fuel",
        "title_status": "Title_status",
        "transmission": "Transmission",
        "drive": "Drive",
        "size": "Size",
        "type": "Type",
        "paint_color": "Paint_color",
        "year": "Year",
        "odometer": "Odometer",
        "age": "Age",
        "age_squared": "Age_squared",
        "mileage_per_year": "Mileage_per_year"
    }
    input_df.rename(columns=feature_name_mapping, inplace=True)

    # Numeric feature conversions
    input_df["Year"] = pd.to_numeric(input_df.get("Year", 0), errors="coerce")
    input_df["Odometer"] = pd.to_numeric(input_df.get("Odometer", 0), errors="coerce")
    
    # Feature engineering
    current_year = 2024
    input_df["Age"] = current_year - input_df["Year"]
    input_df["Age_squared"] = input_df["Age"] ** 2
    input_df["Mileage_per_year"] = input_df["Odometer"] / (input_df["Age"] + 1)
    input_df["Mileage_per_year"] = input_df["Mileage_per_year"].clip(0, 200000)

    # Encode categorical features
    for feature, encoded_feature in feature_name_mapping.items():
        if feature in label_encoders:
            input_df[encoded_feature] = input_df[encoded_feature].fillna("unknown").astype(str).str.strip()
            try:
                input_df[encoded_feature] = label_encoders[feature].transform(input_df[encoded_feature])
            except ValueError:
                input_df[encoded_feature] = 0  # Assign default for unseen values

    # Ensure all required features are present
    for feature in model.feature_names_in_:
        if feature not in input_df:
            input_df[feature] = 0  # Default value for missing features

    # Reorder columns
    input_df = input_df[model.feature_names_in_]

    return input_df



# --- Styling functions ---
st.markdown("""
    <style>
    /* Force black text globally */
    .stApp, .stApp * {
        color: black !important;
    }
    
    /* Specific overrides for different elements */
    .main {
        padding: 0rem 1rem;
    }
    
    .stButton>button {
        width: 100%;
        background-color: #FF4B4B;
        color: white !important;  /* Keep button text white */
        border-radius: 5px;
        padding: 0.5rem 1rem;
        border: none;
    }
    
    .stButton>button:hover {
        background-color: #FF6B6B;
    }
    
    .sidebar .sidebar-content {
        background-color: #f5f5f5;
    }
    
    /* Input fields and selectboxes */
    .stSelectbox select, 
    .stSelectbox option,
    .stSelectbox div,
    .stNumberInput input,
    .stTextInput input {
        color: black !important;
    }
    
    /* Headers */
    h1, h2, h3, h4, h5, h6 {
        color: black !important;
    }
    
    /* Labels and text */
    label, .stText, p, span {
        color: black !important;
    }
    
    /* Selectbox options */
    option {
        color: black !important;
        background-color: white !important;
    }
    
    /* Override for any Streamlit specific classes */
    .st-emotion-cache-16idsys p,
    .st-emotion-cache-1wmy9hl p,
    .st-emotion-cache-16idsys span,
    .st-emotion-cache-1wmy9hl span {
        color: black !important;
    }
    
    /* Force white text only for the prediction button */
    .stButton>button[data-testid="stButton"] {
        color: white !important;
    }
    </style>
""", unsafe_allow_html=True)

def style_metric_container(label, value):
    st.markdown(f"""
        <div style="
            background-color: #f8f9fa;
            padding: 1rem;
            border-radius: 5px;
            margin: 0.5rem 0;
            border-left: 5px solid #FF4B4B;
        ">
            <p style="color: #666; margin-bottom: 0.2rem; font-size: 0.9rem;">{label}</p>
            <p style="color: #1E1E1E; font-size: 1.5rem; font-weight: 600; margin: 0;">{value}</p>
        </div>
    """, unsafe_allow_html=True)

def classify_image(image):
    try:
        model_name = "dima806/car_models_image_detection"
        feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
        model = AutoModelForImageClassification.from_pretrained(model_name)

        inputs = feature_extractor(images=image, return_tensors="pt")
        
        with torch.no_grad():
            outputs = model(**inputs)

        logits = outputs.logits
        predicted_class_idx = logits.argmax(-1).item()
        predicted_class_label = model.config.id2label[predicted_class_idx]
        score = torch.nn.functional.softmax(logits, dim=-1)[0, predicted_class_idx].item()

        return [{'label': predicted_class_label, 'score': score}]
    except Exception as e:
        st.error(f"Classification error: {e}")
        return None



def get_car_overview(brand, model, year):
    try:
        prompt = f"Provide an overview of the following car:\nYear: {year}\nMake: {brand}\nModel: {model}\n"
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": prompt}]
        )
        return response.choices[0].message['content']
    except Exception as e:
        st.error(f"Error getting car overview: {str(e)}")
        return None

def search_dataset(dataset, make, model=None):
    """
    Search the dataset for the specified make and model. If no model is provided,
    search by make only. Return relevant information if found.
    """
    # Filter by make and model
    query = dataset[dataset['Make'].str.lower() == make.lower()]
    if model:
        query = query[query['Model'].str.lower() == model.lower()]
    
    if not query.empty:
        # If matching rows exist, return a formatted response
        results = query[['Year', 'Make', 'Model', 'Price']].head(5)  # Adjust columns as needed
        return results
    else:
        # No relevant data found in the dataset
        return None

# --- Updated GPT Functionality ---
def generate_gpt_response(prompt, dataset):
    """
    First look up the dataset for relevant information. If no matches are found,
    generate a GPT response.
    """
    # Extract make and model from the prompt (simplified NLP parsing)
    prompt_lower = prompt.lower()
    make = None
    model = None
    
    # Example: Parse make and model from user query
    for word in prompt_lower.split():
        if word in dataset['Make'].str.lower().unique():
            make = word
        elif word in dataset['Model'].str.lower().unique():
            model = word
    
    # If we find relevant data, use it to respond
    if make:
        dataset_response = search_dataset(dataset, make, model)
        if dataset_response is not None:
            st.write("### Dataset Match Found")
            st.dataframe(dataset_response)  # Show results to the user
            return f"I found some information in our dataset about {make.title()} {model.title() if model else ''}. Please see the details above."
    
    try:
        openai.api_key = "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"  # Replace with your API key
        system_message = {
            "role": "system",
            "content": "You are a helpful car shopping assistant. Provide concise car recommendations or pricing estimates. Keep responses focused and brief."
        }
        messages = [
            system_message,
            {"role": "user", "content": f"Provide a brief response about: {prompt}"}
        ]
        
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo", 
            messages=messages,
            max_tokens=300,  # Reduced from 500
            temperature=0.7,
        )
        
        return response['choices'][0]['message']['content']
    except Exception as e:
        return f"I apologize, but I encountered an error: {str(e)}. Could you please rephrase your question or make it more specific?"

# --- Assistant Section ---
def create_assistant_section(dataset):
    st.markdown("""
        <div style='background-color: #f8f9fa; padding: 1.5rem; border-radius: 10px; margin-bottom: 1rem;'>
            <h2 style='color: #1E1E1E; margin-top: 0;'>🤖 Car Shopping Assistant</h2>
            <p style='color: #666;'>Ask me anything about cars! For example: 'What's a good car under $30,000 with low mileage?'</p>
        </div>
    """, unsafe_allow_html=True)

    if "assistant_responses" not in st.session_state:
        st.session_state.assistant_responses = []

    prompt = st.text_input("Ask about car recommendations or pricing...", 
                           placeholder="Type your question here...")

    if prompt:
        try:
            response = generate_gpt_response(prompt, dataset)
            st.session_state.assistant_responses.append(response)
        except Exception as e:
            response = f"Sorry, I encountered an error: {str(e)}"
            st.session_state.assistant_responses.append(response)

        # Display the latest response
        st.write(response)

        # Optionally display previous responses
        if len(st.session_state.assistant_responses) > 1:
            st.markdown("### Previous Responses")
            for prev_response in st.session_state.assistant_responses[:-1]:
                st.markdown("---")
                st.write(prev_response)

    if st.button("Clear Chat"):
        st.session_state.assistant_responses = []
        st.experimental_rerun()
# --- Prediction Interface ---
def create_prediction_interface():
    with st.sidebar:
        st.markdown("""
            <div style='background-color: #FF4B4B; padding: 1rem; border-radius: 5px; margin-bottom: 2rem;'>
                <h2 style='color: white; margin: 0;'>Car Details</h2>
            </div>
        """, unsafe_allow_html=True)
        
        # Year slider
        year = st.slider("Year", min_value=1980, max_value=2024, value=2022)
        
        # Make selection
        make_options = sorted(original_data['Make'].dropna().unique())  # Correct casing for 'Make'
        make = st.selectbox("Make", options=make_options)  
        
        # Filter models based on selected make
        filtered_models = sorted(original_data[original_data['Make'] == make]['Model'].dropna().unique())  # Match 'Model' casing
        model_name = st.selectbox("Model", options=filtered_models if len(filtered_models) > 0 else ["No models available"])
        
        if model_name == "No models available":
            st.warning("No models are available for the selected make.")

        # Additional inputs
        condition = st.selectbox("Condition", ['new', 'like new', 'excellent', 'good', 'fair', 'salvage', 'parts only'])
        fuel = st.selectbox("Fuel Type", sorted(original_data['Fuel'].fillna('Unknown').unique()))  # Match casing for 'Fuel'
        odometer = st.number_input("Odometer (miles)", min_value=0, value=20000, format="%d", step=1000)
        title_status = st.selectbox("Title Status", sorted(original_data['Title_status'].fillna('Unknown').unique()))  # Match casing
        transmission = st.selectbox("Transmission", sorted(original_data['Transmission'].fillna('Unknown').unique()))
        drive = st.selectbox("Drive Type", sorted(original_data['Drive'].fillna('Unknown').unique()))
        size = st.selectbox("Size", sorted(original_data['Size'].fillna('Unknown').unique()))
        paint_color = st.selectbox("Paint Color", sorted(original_data['Paint_color'].fillna('Unknown').unique()))
        
        car_type = 'sedan'  # Default type
        
        # Prediction button
        predict_button = st.button("📊 Predict Price", use_container_width=True)

    return {
        'year': year,
        'make': make.strip(),  # Use correctly cased `make`
        'model': model_name if model_name != "No models available" else 'unknown',
        'condition': condition.lower().strip(),
        'fuel': fuel.lower().strip(),
        'odometer': odometer,
        'title_status': title_status.lower().strip(),
        'transmission': transmission.lower().strip(),
        'drive': drive.lower().strip(),
        'size': size.lower().strip(),
        'type': car_type.lower().strip(),
        'paint_color': paint_color.lower().strip()
    }, predict_button



def create_market_trends_plot_with_model(model, make, base_inputs, label_encoders, years_range=range(1980, 2025)):
    predictions = []
    
    for year in years_range:
        try:
            current_inputs = base_inputs.copy()
            current_inputs['year'] = float(year)
            age = 2024 - year
            
            # Base value calculation
            base_price = 30000  # Average new car price
            
            # Depreciation curve
            if age <= 1:
                value_factor = 0.85  # 15% first year depreciation
            elif age <= 5:
                value_factor = 0.85 * (0.90 ** (age - 1))  # 10% years 2-5
            else:
                value_factor = 0.85 * (0.90 ** 4) * (0.95 ** (age - 5))  # 5% thereafter
            
            price = base_price * value_factor
            predictions.append({"year": year, "predicted_price": max(price, 2000)})  # Floor of $2000
            
        except Exception as e:
            continue

    if not predictions:
        return None
    
    predictions_df = pd.DataFrame(predictions)
    fig, ax = plt.subplots(figsize=(12, 6))
    ax.plot(predictions_df["year"], predictions_df["predicted_price"], color="#FF4B4B", linewidth=2)
    ax.set_title(f"Average Car Value by Age")
    ax.set_xlabel("Year")
    ax.set_ylabel("Value ($)")
    ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'${x:,.0f}'))
    plt.grid(True, alpha=0.3)
    
    return fig

def inspect_model_features(model):
    # Check feature names the model expects
    try:
        if hasattr(model, "feature_names_in_"):
            print("Model feature names:", model.feature_names_in_)
        else:
            print("Model does not have 'feature_names_in_' attribute.")
    except Exception as e:
        print(f"Error inspecting model features: {e}")

def predict_with_ranges(inputs, model, label_encoders):
    input_df = prepare_input(inputs, label_encoders)
    base_prediction = float(np.expm1(model.predict(input_df)[0]))
    
    brand_categories = create_brand_categories()
    make = inputs['make'].lower()
    year = inputs['year']
    condition = inputs['condition']
    odometer = inputs['odometer']
    age = 2024 - year
    
    # Find brand category and price range
    price_range = None
    for category, brands in brand_categories.items():
        if make in brands:
            price_range = brands[make]
            break
    if not price_range:
        price_range = (15000, 35000)  # Default range
    
    # Calculate adjustment factors
    mileage_factor = max(1 - (odometer / 200000) * 0.3, 0.7)
    age_factor = 0.85 ** min(age, 15)
    condition_factor = {
        'new': 1.0,
        'like new': 0.9,
        'excellent': 0.8,
        'good': 0.7,
        'fair': 0.5,
        'salvage': 0.3
    }.get(condition, 0.7)
    
    # Apply all factors
    min_price = price_range[0] * mileage_factor * age_factor * condition_factor
    max_price = price_range[1] * mileage_factor * age_factor * condition_factor
    predicted_price = base_prediction * mileage_factor * age_factor * condition_factor
    
    # Use uniform distribution instead of clamping
    final_prediction = np.random.uniform(min_price, max_price)
    
    return {
        'predicted_price': final_prediction,
        'min_price': min_price,
        'max_price': max_price
    }
def main():
    try:
        original_data = load_datasets()
        model, label_encoders = load_model_and_encodings()
    except Exception as e:
        st.error(f"Error loading data or models: {str(e)}")
        st.stop()

    # Create tabs
    tab1, tab2 = st.tabs(["Price Prediction", "Image Analysis"])

    with tab1:
        st.title("Car Price Prediction")
        
        # Create two columns
        col1, col2 = st.columns([2, 1])
        
        with col1:
            # Prediction interface code
            inputs, predict_button = create_prediction_interface()
            
            if predict_button:
                st.write(f"Analyzing {inputs['year']} {inputs['make'].title()} {inputs['model'].title()}...")
                prediction_results = predict_with_ranges(inputs, model, label_encoders)
                
                st.markdown(f"""
                    ### Price Analysis
                    - **Estimated Range**: ${prediction_results['min_price']:,.2f} - ${prediction_results['max_price']:,.2f}
                    - **Model Prediction**: ${prediction_results['predicted_price']:,.2f}
                """)

                # Generate and display the graph
                fig = create_market_trends_plot_with_model(model, inputs["make"], inputs, label_encoders)
                if fig:
                    st.pyplot(fig)
        
        with col2:
            # Add the chat assistant here
            create_assistant_section(original_data)

    with tab2:
        st.title("Car Image Analysis")
        
        # File uploader and camera input
        uploaded_file = st.file_uploader("Choose a car image", type=["jpg", "jpeg", "png"])
        camera_image = st.camera_input("Or take a picture of the car")

        # Process the image
        if uploaded_file is not None:
            image = Image.open(uploaded_file)
        elif camera_image is not None:
            image = Image.open(camera_image)
        else:
            image = None

        if image is not None:
            st.image(image, caption='Uploaded Image', use_container_width=True)
            
            # Classify the image
            with st.spinner('Analyzing image...'):
                car_classifications = classify_image(image)

            if car_classifications:
                top_prediction = car_classifications[0]['label']
                make_name, model_name = top_prediction.split(' ', 1)
                current_year = datetime.now().year

                # Display results
                col1, col2 = st.columns(2)
                col1.metric("Identified Make", make_name)
                col2.metric("Identified Model", model_name)

                # Get car overview
                overview = get_car_overview(make_name, model_name, current_year)
                if overview:
                    st.subheader("Car Overview")
                    st.write(overview)

                # Use the prediction model with the identified car
                st.subheader("Price Analysis for Identified Car")
                auto_inputs = {
                    'year': current_year,
                    'make': make_name.lower(),
                    'model': model_name.lower(),
                    'condition': 'good',  # Default values
                    'fuel': 'gas',
                    'odometer': 0,
                    'title_status': 'clean',
                    'transmission': 'automatic',
                    'drive': 'fwd',
                    'size': 'mid-size',
                    'type': 'sedan',
                    'paint_color': 'white'
                }

                # Get prediction for the identified car
                prediction_results = predict_with_ranges(auto_inputs, model, label_encoders)
                
                st.markdown(f"""
                    ### Estimated Price Range
                    - **Minimum**: ${prediction_results['min_price']:,.2f}
                    - **Maximum**: ${prediction_results['max_price']:,.2f}
                    - **Predicted**: ${prediction_results['predicted_price']:,.2f}
                """)

if __name__ == "__main__":
    main()