app.py

import streamlit as st
import pandas as pd
import openai
import joblib
from PIL import Image
import requests
from io import BytesIO
import matplotlib.pyplot as plt
import numpy as np
from sklearn.preprocessing import LabelEncoder
from huggingface_hub import hf_hub_download
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
import torch
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import re
from datetime import datetime

# Dataset loading function with caching
@st.cache_data
def load_datasets():
    try:
        with st.spinner('Loading dataset...'):
            original_data = pd.read_csv('CTP_Model1.csv', low_memory=False)
            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

def load_image(image_file):
    return Image.open(image_file)

def classify_image(image):
    try:
        # Load the model and feature extractor
        model_name = "dima806/car_models_image_detection"
        feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
        model = AutoModelForImageClassification.from_pretrained(model_name)

        # Preprocess the image
        inputs = feature_extractor(images=image, return_tensors="pt")

        # Perform inference
        with torch.no_grad():
            outputs = model(**inputs)

        # Get the predicted class
        logits = outputs.logits
        predicted_class_idx = logits.argmax(-1).item()
        
        # Get the class label and score
        predicted_class_label = model.config.id2label[predicted_class_idx]
        score = torch.nn.functional.softmax(logits, dim=-1)[0, predicted_class_idx].item()

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

def find_closest_match(df, brand, model):
    # Combine brand and model names from the dataset
    df['full_name'] = df['Make'] + ' ' + df['Model']
    
    # Create a list of all car names
    car_names = df['full_name'].tolist()
    
    # Add the query car name
    query_car = f"{brand} {model}"
    car_names.append(query_car)
    
    # Create TF-IDF vectorizer
    vectorizer = TfidfVectorizer()
    tfidf_matrix = vectorizer.fit_transform(car_names)
    
    # Compute cosine similarity
    cosine_similarities = cosine_similarity(tfidf_matrix[-1], tfidf_matrix[:-1]).flatten()
    
    # Get the index of the most similar car
    most_similar_index = cosine_similarities.argmax()
    
    # Return the most similar car's data
    return df.iloc[most_similar_index]

def get_car_overview(car_data):
    prompt = f"Provide an overview of the following car:\nYear: {car_data['Year']}\nMake: {car_data['Make']}\nModel: {car_data['Model']}\nTrim: {car_data['Trim']}\nPrice: ${car_data['Price']}\nCondition: {car_data['Condition']}\n"
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message['content']

def load_model_and_encodings():
    try:
        with st.spinner('Loading model...'):
            model_content = hf_hub_download(repo_id="EdBoy2202/car_prediction_model", filename="car_price_modelv3.pkl")
            model = joblib.load(model_content)

        original_data = load_datasets()
        
        label_encoders = {}
        categorical_features = original_data.select_dtypes(include=['object']).columns.tolist()
        
        for feature in categorical_features:
            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, categorical_features
    except Exception as e:
        st.error(f"Error loading model: {str(e)}")
        raise e

def calculate_age(year):
    current_year = datetime.now().year
    return current_year - year

def predict_price(model, encoders, categorical_features, user_input):
    encoded_features = {}
    current_year = datetime.now().year
    
    for feature, value in user_input.items():
        if feature.lower() in encoders:
            encoded_features[feature.capitalize()] = encoders[feature.lower()].transform([value])[0]
        elif feature in categorical_features:
            # If it's a categorical feature but not in encoders, set to 0 (unknown)
            encoded_features[feature.capitalize()] = 0
        else:
            # For numerical features, use the value as is
            encoded_features[feature.capitalize()] = value
    
    # Calculate additional features
    encoded_features['Age'] = calculate_age(encoded_features['Year'])
    encoded_features['Age_squared'] = encoded_features['Age'] ** 2
    
    # Assume average mileage per year (you may want to adjust this)
    avg_mileage_per_year = 12000
    encoded_features['Mileage_per_year'] = avg_mileage_per_year
    
    # Assume odometer reading (you may want to adjust this)
    encoded_features['Odometer'] = encoded_features['Age'] * avg_mileage_per_year
    
    input_data = pd.DataFrame([encoded_features])
    
    # Ensure all expected columns are present
    expected_columns = ['Make', 'Model', 'Year', 'Condition', 'Fuel', 'Odometer', 'Title_status', 'Transmission', 'Drive', 'Size', 'Type', 'Paint_color', 'Age', 'Age_squared', 'Mileage_per_year']
    for col in expected_columns:
        if col not in input_data.columns:
            input_data[col] = 0  # or some default value
    
    predicted_price = model.predict(input_data)
    return predicted_price[0]

# Streamlit App
st.title("Auto Appraise")
st.write("Upload a car image or take a picture to get its brand, model, overview, and expected price!")

# Load model and encoders
model, label_encoders, categorical_features = load_model_and_encodings()

# Initialize OpenAI API key
openai.api_key = st.secrets["GPT_TOKEN"]

# File uploader for image
uploaded_file = st.file_uploader("Choose a car image", type=["jpg", "jpeg", "png"])

# Camera input as an alternative (optional)
camera_image = st.camera_input("Or take a picture of the car")

# Process the image (either uploaded or from camera)
image = None
if uploaded_file is not None:
    image = Image.open(uploaded_file)
    st.write("Image uploaded successfully.")
elif camera_image is not None:
    image = Image.open(camera_image)
    st.write("Image captured successfully.")

if image is not None:
    st.image(image, caption='Processed Image', use_container_width=True)

    # Classify the car image
    with st.spinner('Analyzing image...'):
        car_classifications = classify_image(image)
    
    if car_classifications:
        st.write("Image classification successful.")
        st.subheader("Car Classification Results:")
        for classification in car_classifications:
            st.write(f"Model: {classification['label']}")
            st.write(f"Confidence: {classification['score']*100:.2f}%")
        
        # Use the top prediction for further processing
        top_prediction = car_classifications[0]['label']
        brand, model_name = top_prediction.split(' ', 1)
        
        st.write(f"Identified Car: {brand} {model_name}")

        # Find the closest match in the CSV
        df = load_datasets()
        match = find_closest_match(df, brand, model_name)
        if match is not None:
            st.write("Closest Match Found:")
            st.write(f"Make: {match['Make']}")
            st.write(f"Model: {match['Model']}")
            st.write(f"Year: {match['Year']}")
            st.write(f"Price: ${match['Price']}")

            # Get additional information using GPT-3.5-turbo
            overview = get_car_overview(match)
            st.write("Car Overview:")
            st.write(overview)

            # Interactive Price Prediction
            st.subheader("Price Prediction Over Time")
            selected_years = st.slider("Select range of years for price prediction", 
                                         min_value=2000, max_value=2023, value=(2010, 2023))

            years = np.arange(selected_years[0], selected_years[1] + 1)
            predicted_prices = []

            for year in years:
                user_input = {
                    'make': match['Make'],
                    'model': match['Model'],
                    'year': year,
                    'condition': match.get('Condition', 'unknown'),
                    'fuel': match.get('Fuel', 'unknown'),
                    'title_status': match.get('Title_status', 'unknown'),
                    'transmission': match.get('Transmission', 'unknown'),
                    'drive': match.get('Drive', 'unknown'),
                    'size': match.get('Size', 'unknown'),
                    'type': match.get('Type', 'unknown'),
                    'paint_color': match.get('Paint_color', 'unknown'),
                }
                
                price = predict_price(model, label_encoders, categorical_features, user_input)
                predicted_prices.append(price)

            # Plotting the results
            plt.figure(figsize=(10, 5))
            plt.plot(years, predicted_prices, marker='o')
            plt.title(f"Predicted Price of {match['Make']} {match['Model']} Over Time")
            plt.xlabel("Year")
            plt.ylabel("Predicted Price ($)")
            plt.grid()
            st.pyplot(plt)

        else:
            st.write("No match found in the database.")
    else:
        st.error("Could not classify the image. Please try again with a different image.")
else:
    st.write("Please upload an image or take a picture to proceed.")