# -*- coding: utf-8 -*-
"""AdAnalyst_Mar30_logo
#0. Install Libraries (Restart runtime at the end of the download and start running from step1)
"""

"""#1. Import Libraries"""

# Download the Dutch language corpus for textblob-nl
import nltk
nltk.download('alpino')
import gradio as gr
import torch
import torchvision.transforms as transforms
from PIL import Image, ImageDraw
import timm
import numpy as np
import cv2
import pandas as pd
import re
from textblob import TextBlob
from textblob_nl import PatternTagger, PatternAnalyzer
from sklearn.cluster import KMeans
import math
from ultralytics import YOLO
import easyocr
import sys
import os
import zipfile
from deepface import DeepFace

"""#2. Dictionaries"""

# Define regex pattern for URLs
url_pattern = re.compile(
    r'(http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|'
    r'(?:%[0-9a-fA-F][0-9a-fA-F]))+|www\.(\w+\.)+\w+|(\w+\.)+(nl|com))'
)

# Define lists for price indications, promotions, calls to action, and car brands
price_indications = [r'\u20AC', '€', 'EUR', 'euro', 'euros']


promotion_words = ['korting', 'aanbieding', 'uitverkoop', 'prijsverlaging', 'afprijzing', 'voordeel',
          'prijsbewust', 'goedkoop', 'besparing', 'tegen een lage prijs', 'speciale aanbieding',
          'prijsvermindering', 'kortingbon', 'promotiecode', 'actie', 'actieprijs', 'tijdelijke aanbieding',
          'nu met korting', 'extra voordelig', 'beste deal', 'flash sale', 'superaanbieding', 'seizoensuitverkoop',
          'nu extra voordelig', 'nu met voordeel', 'exclusieve korting', 'laatste kans', 'tweede gratis', 'koopje',
          'knalprijs', 'megakorting', 'laagste prijs']

call_to_action_phrases = ['bezoek', 'ga naar', 'dealer', 'showroom', 'garage', 'proefrit', 'testrit',
              'probeer', 'bestel nu', 'koop nu', 'reserveren', 'vraag een offerte aan', 'configureren',
              'ontdek meer', 'bekijk de aanbieding', 'registreer voor updates', 'neem contact op voor meer informatie',
              'kom langs', 'start hier', 'doe een aanbetaling', 'ontvang een brochure', 'financieringsmogelijkheden',
              'vraag om een demo', 'bestel vandaag nog', 'nu kopen', 'nu reserveren', 'meld je aan', 'schrijf je in',
              'maak een afspraak', 'bel ons', 'neem contact op', 'plan je testrit', 'configureer je auto', 'klik hier',
              'vraag een proefrit aan', 'vraag een offerte', 'doe mee', 'check het aanbod', 'registreer nu', 'nu ontdekken',
              'bekijk onze modellen', 'bezoek onze website', 'vraag informatie aan', 'aanbetaling', 'aanvraag', 'aanvragen', 'afspraak',
              'bekijk', 'bel', 'bestel', 'contact', 'configureer', 'demo', 'doe', 'download', 'financieringsmogelijkheden', 'info',
              'informatie', 'kom', 'koop', 'laat je gegevens achter', 'maak', 'meld', 'neem', 'nu', 'offerte', 'ontdek', 'ontvang',
              'plan', 'probeer', 'registreer', 'reserveren', 'schrijf', 'start', 'vraag', 'website']

# Expanded list of major car brands
car_brands = [
    'ARCFOX', 'Acura', 'Aion', 'Alfa Romeo', 'Apollo', 'Artega', 'Aston Martin', 'Audi',
    'BAC', 'BAIC', 'BMW', 'BYD', 'Baojun', 'Beijing', 'Bentley', 'Bestune', 'Bugatti',
    'Buick', 'Cadillac', 'Caterham', 'Changan', 'Chery', 'Chevrolet', 'Chrysler', 'Citroën',
    'Cupra', 'Daewoo', 'Dacia', 'Dodge', 'Dongfeng', 'DS Automobiles', 'Ferrari', 'Fiat',
    'Fisker', 'Ford', 'GAC', 'GAZ', 'GMC', 'Geely', 'Genesis', 'Great Wall', 'Gumpert',
    'Haval', 'Holden', 'Honda', 'Hongqi', 'Hozon Auto', 'Hummer', 'Hyundai', 'Infiniti',
    'Isuzu', 'JAC', 'Jaguar', 'Jeep', 'Kia', 'Koenigsegg', 'LEVC', 'LINCOLN', 'Lamborghini',
    'Land Rover', 'Leapmotor', 'Lexus', 'Li Auto', 'Lincoln', 'Lucid', 'Luxgen', 'Lynk & Co',
    'MG', 'MINI', 'Mahindra', 'Maserati', 'Mazda', 'McLaren', 'Mercedes', 'Mercury',
    'Mini', 'Mitsubishi', 'Morgan', 'NIO', 'Nissan', 'Noble', 'Oldsmobile', 'Opel',
    'ORA', 'Pagani', 'Peugeot', 'Perodua', 'Polestar', 'Pontiac', 'Porsche', 'Proton',
    'Ram', 'Reno', 'Rezvani', 'Rimac', 'Rivian', 'Rolls-Royce', 'Roewe', 'Saab', 'Saturn',
    'SAIC', 'SEAT', 'SSC North America', 'Skoda', 'Smart', 'Spyker', 'SsangYong',
    'Subaru', 'Suzuki', 'Tank', 'Tata', 'Tesla', 'Toyota', 'Trumpchi', 'VinFast', 'Volkswagen',
    'Volvo', 'WEY', 'W Motors', 'Wiesmann', 'Xpeng', 'Zeekr'
]

# COCO class names
coco_classes = {
    0: "person",
    1: "bicycle",
    2: "car",
    3: "motorcycle",
    4: "airplane",
    5: "bus",
    6: "train",
    7: "truck",
    8: "boat",
    9: "traffic light",
    10: "fire hydrant",
    11: "stop sign",
    12: "parking meter",
    13: "bench",
    14: "bird",
    15: "cat",
    16: "dog",
    17: "horse",
    18: "sheep",
    19: "cow",
    20: "elephant",
    21: "bear",
    22: "zebra",
    23: "giraffe",
    24: "backpack",
    25: "umbrella",
    26: "handbag",
    27: "tie",
    28: "suitcase",
    29: "frisbee",
    30: "skis",
    31: "snowboard",
    32: "sports ball",
    33: "kite",
    34: "baseball bat",
    35: "baseball glove",
    36: "skateboard",
    37: "surfboard",
    38: "tennis racket",
    39: "bottle",
    40: "wine glass",
    41: "cup",
    42: "fork",
    43: "knife",
    44: "spoon",
    45: "bowl",
    46: "banana",
    47: "apple",
    48: "sandwich",
    49: "orange",
    50: "broccoli",
    51: "carrot",
    52: "hot dog",
    53: "pizza",
    54: "donut",
    55: "cake",
    56: "chair",
    57: "couch",
    58: "potted plant",
    59: "bed",
    60: "dining table",
    61: 'toilet',
    62: 'tv',
    63: 'laptop',
    64: 'mouse',
    65: 'remote',
    66: 'keyboard',
    67: 'cell phone',
    68: 'microwave',
    69: 'oven',
    70: 'toaster',
    71: 'sink',
    72: 'refrigerator',
    73: 'book',
    74: 'clock',
    75: 'vase',
    76: 'scissors',
    77: 'teddy bear',
    78: 'hair drier',
    79: 'toothbrush'
}

# Object categories as per your request
animal_classes = [14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
transportation_classes = [1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
sports_classes = [29, 30, 31, 32, 33, 34, 35, 36, 37, 38]
food_classes = [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 60, 68, 69, 70, 71, 72]

"""#3. Function Modules

## Uniqueness and Consistency
"""

# 1. Compute image embedding
# Load the pre-trained ViT model
def load_vit_model():
    model = timm.create_model('vit_base_patch16_224', pretrained=True, num_classes=0)
    model.eval()
    return model

vit_model = load_vit_model()

# Define image preprocessing steps
def get_preprocess_transforms():
    return transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(
            mean=(0.5, 0.5, 0.5),
            std=(0.5, 0.5, 0.5)
        )
    ])

preprocess = get_preprocess_transforms()

def compute_image_embedding(image, model, preprocess):
    input_tensor = preprocess(image).unsqueeze(0)
    with torch.no_grad():
        embedding = model(input_tensor)
    return embedding.squeeze()

# 2. Get embeddings from files
def get_embeddings_from_files(file_list, model, preprocess):
    embeddings = []
    for file_obj in file_list:
        image = Image.open(file_obj.name).convert('RGB')
        embedding = compute_image_embedding(image, model, preprocess)
        embeddings.append(embedding)
    return embeddings

# 3. Calculate similarity scores
def calculate_similarity_scores(focus_embedding, embeddings_list):
    from torch.nn.functional import cosine_similarity
    similarities = []
    for emb in embeddings_list:
        sim = cosine_similarity(focus_embedding.unsqueeze(0), emb.unsqueeze(0)).item()
        similarities.append(sim)
    average_score = sum(similarities) / len(similarities) if similarities else None
    return round(average_score, 4) if average_score is not None else 'None'

"""## OCR"""

# Extract textual features

# load EasyOCTR model
reader = easyocr.Reader(['nl','en'], gpu=False)

def extract_textual_features(image):

    width, height = image.size
    image_area = width * height

    image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)

    #reader = easyocr.Reader(['nl', 'en'], gpu=False)

    results = reader.readtext(image_cv)

    extracted_text_list = []
    total_text_area = 0

    mask = np.zeros((height, width), dtype=np.uint8)

    for (bbox, text, prob) in results:
        if text.strip():
            extracted_text_list.append(text)

            points = np.array(bbox, dtype=np.int32)
            polygon = []
            for point in points:
                polygon.append((int(point[0]), int(point[1])))

            temp_mask = Image.new('L', (width, height), 0)
            ImageDraw.Draw(temp_mask).polygon(polygon, outline=1, fill=1)
            temp_array = np.array(temp_mask)
            mask = np.logical_or(mask, temp_array).astype(np.uint8) * 255

    extracted_text = ' '.join(extracted_text_list).strip()

    num_char = len(extracted_text.replace(" ", ""))

    text_area = np.sum(mask > 0)
    text_area_ratio = text_area / image_area*100 if image_area > 0 else 0
    text_area_ratio = round(text_area_ratio, 4)

    return extracted_text, num_char, text_area_ratio

# 5. Perform sentiment analysis
def perform_sentiment_analysis(text):
    if text.strip():
        blob = TextBlob(text, pos_tagger=PatternTagger(), analyzer=PatternAnalyzer())
        sentiment = blob.sentiment
        sentiment_polarity = round(sentiment[0], 4)
        sentiment_subjectivity = round(sentiment[1], 4)
    else:
        sentiment_polarity = 0.0
        sentiment_subjectivity = 0.0
    return sentiment_polarity, sentiment_subjectivity

# 6. Analyze additional textual features
def analyze_additional_text_features(text):
    # URL Count
    urls = re.findall(url_pattern, text)
    url_count = len(urls)

    # Price Indication Count
    price_count = sum(text.lower().count(word.lower()) for word in price_indications)

    # Promotion Indication Count
    promotion_count = sum(text.lower().count(word.lower()) for word in promotion_words)

    # Call to Action Count
    call_to_action_count = sum(text.lower().count(phrase.lower()) for phrase in call_to_action_phrases)

    # Brand Salience Count
    brand_count = sum(text.lower().count(brand.lower()) for brand in car_brands)

    return url_count, price_count, promotion_count, call_to_action_count, brand_count

"""## Basci Visual Features"""

# Function to convert RGB to Hex
def rgb_to_hex(color):
    return '#%02x%02x%02x' % tuple(color)

#  Get dominant color
def get_dominant_color(image, k=4):
    # Resize image to reduce computation time
    image = image.resize((150, 150))
    # Convert image to numpy array
    np_image = np.array(image)
    np_image = np_image.reshape((np_image.shape[0]*np_image.shape[1], 3))
    # Use KMeans clustering
    kmeans = KMeans(n_clusters=k)
    kmeans.fit(np_image)
    # Get the cluster centers
    colors = kmeans.cluster_centers_
    # Get counts of pixels in each cluster
    labels, counts = np.unique(kmeans.labels_, return_counts=True)
    # Find the most frequent color
    dominant_color = colors[np.argmax(counts)]
    # Convert to int and to hex
    dominant_color = dominant_color.astype(int)
    dominant_color_hex = rgb_to_hex(dominant_color)
    return dominant_color_hex, tuple(dominant_color)

# Extract visual features
def extract_visual_features(image):
    # Image resolution
    width, height = image.size

    # Average RGB values
    np_image = np.array(image)
    avg_r = np.mean(np_image[:, :, 0])
    avg_g = np.mean(np_image[:, :, 1])
    avg_b = np.mean(np_image[:, :, 2])

    # Dominant color
    dominant_color_hex, dominant_color_rgb = get_dominant_color(image)

    # Warm/cold hue
    # Convert image to HSV
    hsv_image = image.convert('HSV')
    np_hsv = np.array(hsv_image)
    avg_hue = np.mean(np_hsv[:, :, 0])
    # Convert hue from 0-255 to degrees
    avg_hue_deg = avg_hue * 360 / 255
    # Determine warm or cold
    # Warm colors are from 0-60 and 300-360 degrees
    if (0 <= avg_hue_deg <= 60) or (300 <= avg_hue_deg <= 360):
        hue_category = 'Warm'
    else:
        hue_category = 'Cool'

    # Visual complexity (Shannon entropy)
    # Convert image to grayscale
    gray_image = image.convert('L')
    histogram = gray_image.histogram()
    histogram_length = sum(histogram)
    samples_probability = [float(h) / histogram_length for h in histogram if h != 0]
    entropy = -sum([p * math.log(p, 2) for p in samples_probability])
    entropy =  round(entropy / 8, 4)

    # Return the computed features
    return {
        'Resolution': f'{width}x{height}',
        'Dominant Color': dominant_color_hex,
        'Dominant Color RGB': dominant_color_rgb,
        'Hue Category': hue_category,
        'Average Red': round(avg_r, 2),
        'Average Green': round(avg_g, 2),
        'Average Blue': round(avg_b, 2),
        'Visual Complexity': round(entropy, 4)
    }

"""## YOLO: Perform object detection"""

# Load YOLO model
def load_yolo_model():
    model = YOLO('yolo11s.pt')  # Using YOLO medium model
    return model

yolo_model = load_yolo_model()

# QR code
detector = cv2.QRCodeDetector()

def perform_object_detection(image_pil):
    img_width, img_height = image_pil.size
    image_area = img_width * img_height

    np_image = np.array(image_pil)
    np_image_bgr = cv2.cvtColor(np_image, cv2.COLOR_RGB2BGR)

    retval, decoded_info, points, _ = detector.detectAndDecodeMulti(np_image_bgr)
    qr_code_count = len(decoded_info)

    # Run YOLO model
    results = yolo_model(np_image_bgr)

    detections = results[0]
    boxes = detections.boxes  # Bounding boxes
    class_ids = boxes.cls.cpu().numpy().astype(int)
    confidences = boxes.conf.cpu().numpy()
    xyxy = boxes.xyxy.cpu().numpy()  # Bounding box coordinates

    # Initialize counts and areas
    car_count = 0
    car_coverage_area = 0
    car_positions = []

    person_count = 0
    animal_count = 0
    transportation_count = 0
    sports_item_count = 0
    food_item_count = 0



    for cls_id, conf, bbox in zip(class_ids, confidences, xyxy):
        class_name = coco_classes.get(cls_id, 'Unknown')
        bbox_area = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])

        if cls_id == 2:  # Car class
            car_count += 1
            car_coverage_area += bbox_area
            # Determine position
            center_x = (bbox[0] + bbox[2]) / 2
            center_y = (bbox[1] + bbox[3]) / 2
            
            if center_x <  2 * img_width  / 5:
                horiz = 'Left'
            elif center_x > 3 * img_width / 5:
                horiz = 'Right'
            else:
                horiz = 'Center'

            if center_y < 2 * img_height / 5:
                vert = 'Top'
            elif center_y > 3 * img_height / 5:
                vert = 'Bottom'
            else:
                vert = 'Middle'
            car_positions.append(f"{vert}-{horiz}")

        elif cls_id == 0:
            person_count += 1
        elif cls_id in animal_classes:
            animal_count += 1
        elif cls_id in transportation_classes:
            transportation_count += 1
        elif cls_id in sports_classes:
            sports_item_count += 1
        elif cls_id in food_classes:
            food_item_count += 1

    # Calculate coverage area ratio
    car_coverage_ratio = car_coverage_area / image_area *100 if image_area > 0 else 0
    car_coverage_ratio = round(car_coverage_ratio, 2)

    # Get unique positions
    unique_positions = list(set(car_positions))

    return {
        'Car Count': car_count,
        'Car Coverage Ratio': car_coverage_ratio,
        'Car Positions': ', '.join(unique_positions) if unique_positions else 'None',
        'Person Count': person_count,
        'Animal Object Count': animal_count,
        'Transportation Object Count': transportation_count,
        'Sports Item Count': sports_item_count,
        'Food Item Count': food_item_count,
        'QR Code Count': qr_code_count
    }

"""## Logo"""

# Commented out IPython magic to ensure Python compatibility.
if not os.path.exists("yolov7"):
    with zipfile.ZipFile("yolov7.zip", 'r') as zip_ref:
        zip_ref.extractall(".")
print("files", os.listdir("."))
sys.path.append("./yolov7")
logo_model = torch.hub.load('./yolov7', 'custom', './logo_detection.pt', source='local')
logo_model.conf = 0.25

def detect_logos(pil_image):

    image_np = np.array(pil_image)
    results = logo_model(image_np)

    # results.xyxy[0]  [x1, y1, x2, y2, conf, cls]
    detections = results.xyxy[0].cpu().numpy() if results.xyxy[0] is not None else np.empty((0, 6))

    logo_count = detections.shape[0]
    total_logo_area = 0
    positions = []

    img_height, img_width = image_np.shape[0], image_np.shape[1]
    image_area = img_height * img_width


    for det in detections:
        x1, y1, x2, y2, conf, cls = det
        box_area = (x2 - x1) * (y2 - y1)
        total_logo_area += box_area

        center_x = (x1 + x2) / 2
        center_y = (y1 + y2) / 2

        if center_x < img_width / 3:
            horiz = 'Left'
        elif center_x < 2 * img_width / 3:
            horiz = 'Center'
        else:
            horiz = 'Right'

        if center_y < img_height / 3:
            vert = 'Top'
        elif center_y < 2 * img_height / 3:
            vert = 'Middle'
        else:
            vert = 'Bottom'
 

        positions.append(f"{vert}-{horiz}")

    logo_area_ratio = total_logo_area / image_area*100 if image_area > 0 else 0
    logo_area_ratio = round(logo_area_ratio,2)

    unique_positions = list(dict.fromkeys(positions))
    positions_str = ", ".join(unique_positions)

    return {
        'Logo Count': logo_count,
        'Logo Coverage Ratio': logo_area_ratio,
        'Logo Positions': positions_str}


def analyze_face_features(image):
    #GDPR -- exclude gender and race inference

    # BGR format for deepface
    image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)

    results = DeepFace.analyze(
        img_path=image_cv,
        actions=['emotion'],
        detector_backend='mtcnn',
        enforce_detection=False
    )

    # list format
    if not isinstance(results, list):
        results = [results]

    # return none if no face detected
    if not results:
        return {
            "angry": 0,
            "disgust": 0,
            "fear": 0,
            "happy": 0,
            "sad": 0,
            "surprise": 0,
            "neutral": 0}

    # If no faces, return zeros
    if not results:
        return {
            "angry": 0,
            "disgust": 0,
            "fear": 0,
            "happy": 0,
            "sad": 0,
            "surprise": 0,
            "neutral": 0}

    # initiation
    emotions_sum = {
        "angry": 0,
        "disgust": 0,
        "fear": 0,
        "happy": 0,
        "sad": 0,
        "surprise": 0,
        "neutral": 0
    }

    # process faces
    for face in results:
        for emotion, value in face['emotion'].items():
            emotion_lower = emotion.lower()
            if emotion_lower in emotions_sum:
                emotions_sum[emotion_lower] += value

    # average emtions
    num_faces = len(results)
    avg_emotions = {emotion: round(value / num_faces, 2) for emotion, value in emotions_sum.items()}

    result = {
        "angry": avg_emotions["angry"],
        "disgust": avg_emotions["disgust"],
        "fear": avg_emotions["fear"],
        "happy": avg_emotions["happy"],
        "sad": avg_emotions["sad"],
        "surprise": avg_emotions["surprise"],
        "neutral": avg_emotions["neutral"],
    }


    return result

"""# 4. Run all analysis"""

# Process image and compute all features
def process_image(focal_image, same_brand_files, competitive_brand_files):
    # Input validation
    if focal_image is None:
        return ["Please upload the focal ad."] + [""] * 23  # Adjusted for total outputs

    # Compute embeddings
    focus_embedding = compute_image_embedding(focal_image, vit_model, preprocess)

    # Calculate scores
    if same_brand_files:
        same_brand_embeddings = get_embeddings_from_files(same_brand_files, vit_model, preprocess)
        consistency_score = calculate_similarity_scores(focus_embedding, same_brand_embeddings)
        consistency_score = round(consistency_score, 4)
    else:
        consistency_score = 'None'

    if competitive_brand_files:
        competitive_brand_embeddings = get_embeddings_from_files(competitive_brand_files, vit_model, preprocess)
        uniqueness_score = 1- calculate_similarity_scores(focus_embedding, competitive_brand_embeddings)
        uniqueness_score = round(uniqueness_score, 4)
    else:
        uniqueness_score = 'None'

    # Calculate ad_elasticity
    if consistency_score != 'None' and uniqueness_score != 'None':
        ad_elasticity = round(0.021 + 0.097*consistency_score + 0.110*uniqueness_score, 4)
    else:
        ad_elasticity = 'None'  # Handle missing values gracefully    

    # Extract textual features
    extracted_text, num_char, text_area_ratio = extract_textual_features(focal_image)

    # Sentiment analysis
    sentiment_polarity, sentiment_subjectivity = perform_sentiment_analysis(extracted_text)

    # Analyze additional textual features
    url_count, price_count, promotion_count, call_to_action_count, brand_salience_count = analyze_additional_text_features(extracted_text)

    # Extract visual features
    visual_features = extract_visual_features(focal_image)
    # Unpack visual features
    resolution = visual_features['Resolution']
    dominant_color = visual_features['Dominant Color']
    dominant_color_rgb = visual_features['Dominant Color RGB']
    hue_category = visual_features['Hue Category']
    avg_r = visual_features['Average Red']
    avg_g = visual_features['Average Green']
    avg_b = visual_features['Average Blue']
    visual_complexity = visual_features['Visual Complexity']

    # Perform object detection
    object_detection_results = perform_object_detection(focal_image)
    # Unpack object detection results
    car_count = object_detection_results['Car Count']
    car_coverage_ratio = object_detection_results['Car Coverage Ratio']
    car_positions = object_detection_results['Car Positions']
    person_count = object_detection_results['Person Count']
    animal_count = object_detection_results['Animal Object Count']
    transportation_count = object_detection_results['Transportation Object Count']
    sports_item_count = object_detection_results['Sports Item Count']
    food_item_count = object_detection_results['Food Item Count']
    qr_code_count = object_detection_results['QR Code Count']

    # Perform logo detection
    logo_detection_results = detect_logos(focal_image)
    # Unpack logo detection results
    logo_count = logo_detection_results['Logo Count']
    logo_area_ratio = logo_detection_results['Logo Coverage Ratio']
    logo_positions = logo_detection_results['Logo Positions']

    #emtion
    emotion_results = analyze_face_features(focal_image)
    emo_angry = emotion_results["angry"]
    emo_disgust = emotion_results["disgust"]
    emo_fear =  emotion_results["fear"]
    emo_happy = emotion_results["happy"]
    emo_sad = emotion_results["sad"]
    emo_surprise = emotion_results["surprise"]
    emo_neutral = emotion_results["neutral"]
    

    # Return all outputs
    return [
        uniqueness_score,
        consistency_score,
        ad_elasticity,
        resolution,
        dominant_color,
        hue_category,
        avg_r,
        avg_g,
        avg_b,
        visual_complexity,
        car_count,
        car_coverage_ratio,
        car_positions,
        logo_count,
        logo_area_ratio,
        logo_positions,
        person_count,
        animal_count,
        transportation_count,
        sports_item_count,
        food_item_count,
        qr_code_count,
        num_char,
        text_area_ratio,
        sentiment_polarity,
        sentiment_subjectivity,
        url_count,
        price_count,
        promotion_count,
        call_to_action_count,
        brand_salience_count,
        emo_angry,
        emo_disgust,
        emo_fear,
        emo_happy,
        emo_sad,
        emo_surprise,
        emo_neutral
    ]

import cv2, requests
image = cv2.imdecode(np.frombuffer(requests.get("https://github.com/JaidedAI/EasyOCR/raw/master/examples/english.png").content, np.uint8), cv2.IMREAD_COLOR)

image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
image = image.convert('RGB')
image

process_image(image, None, None)

"""#5. Gradio Interface"""

def get_score_card(value, title, thresholds, colors, labels):
    try:
        value = float(value)
        position = 10  # default
        for i, th in enumerate(thresholds):
            if value < th:
                position = 10 + i * 20
                break
        else:
            position = 90
    except (ValueError, TypeError):
        return f"<div style='color:gray;'>{title}: N/A</div>"

    color_bars = "".join([f"<div style='width:{100/len(colors)}%; background-color:{c};'></div>" for c in colors])
    
    return f"""
    <div style='border-radius:15px; padding:20px; background:#F9F9F9; box-shadow:0 4px 10px rgba(0,0,0,0.1);'>
        <div style='font-size:18px; font-weight:bold; margin-bottom:10px;'>{title}: {value:.2f}</div>
        <div style='position:relative; height:40px;'>
            <div style='display:flex; height:16px; border-radius:8px; overflow:hidden;'>
                {color_bars}
            </div>
            <div style='position:absolute; top:18px; left:{position}%; transform:translateX(-50%);'>
                <div style='width:0;height:0;border-left:8px solid transparent;border-right:8px solid transparent;border-top:12px solid black;'></div>
            </div>
        </div>
        <div style='display:flex; justify-content:space-between; margin-top:8px; font-size:14px;'>
            {"".join([f"<span>{l}</span>" for l in labels])}
        </div>
    </div>
    """


def get_consistency_card(value):
    return get_score_card(
        value=value,
        title="Within-Brand Ad Consistency",
        thresholds=[0.1736, 0.3003, 0.5538, 0.6806],  # +- 1 or 2 SD
        colors=["#FF4C4C", "#FFA500", "#FFD700", "#90EE90", "#008000"],
        labels=["Poor", "Low", "Avg", "Good", "Exc"]
    )


def get_distinctiveness_card(value):
    return get_score_card(
        value=value,
        title="Cross-Brand Ad Uniqueness",
        thresholds=[0.3937, 0.5000, 0.7125, 0.8187],
        colors=["#FF4C4C", "#FFA500", "#FFD700", "#90EE90", "#008000"],
        labels=["Poor", "Low", "Avg", "Good", "Exc"]
    )


def get_elasticity_card(value):
    return get_score_card(
        value=value,
        title="Ad Elasticity",
        thresholds=[0.08, 0.12, 0.16, 0.20],
        colors=["#FF4C4C", "#FFA500", "#FFD700", "#90EE90", "#008000"],
        labels=["Poor", "Low", "Avg", "Good", "Exc"],
    )

with gr.Blocks() as demo:
    gr.Markdown("# Ad Analyst")

    # Input Components
    with gr.Row():
        focal_ad = gr.Image(type='pil', label='Focal Ad', height=200)
        same_brand_ads = gr.File(file_types=['image'], label='Same Brand Ads', file_count='multiple')
        competitive_brand_ads = gr.File(file_types=['image'], label='Competitive Brand Ads', file_count='multiple')
    run_button = gr.Button('Run Analysis')

    # Output Components
    gr.Markdown("## Comprehensive Indexes")
    with gr.Row():
        distinctiveness_score = gr.HTML(label='Cross-Brand Ad Uniqueness')
        consistency_score = gr.HTML(label='Within-Brand Ad Consistency')
        ad_elasticity = gr.HTML(label='Ad Elasticity')

    gr.Markdown("## Visual Features")
    with gr.Row():
        resolution_output = gr.Textbox(label='Resolution')
        hue_category_output = gr.Textbox(label='Hue Category')
        dominant_color_output = gr.Textbox(label='Dominant Color')
        dominant_color_indicator = gr.ColorPicker(label='Color Indicator', value='#FFFFFF')

    with gr.Row():
        avg_r_output = gr.Textbox(label='Average Red')
        avg_g_output = gr.Textbox(label='Average Green')
        avg_b_output = gr.Textbox(label='Average Blue')
        visual_complexity_output = gr.Textbox(label='Visual Complexity (0 to 1)')
    gr.Markdown("## Object Detection")
    with gr.Row():
        car_count_output = gr.Textbox(label='Car Count')
        car_coverage_ratio_output = gr.Textbox(label='Car Coverage Ratio (%)')
        car_positions_output = gr.Textbox(label='Car Positions')
    with gr.Row():
        logo_count_output = gr.Textbox(label='Logo Count')
        logo_coverage_ratio_output = gr.Textbox(label='Logo Coverage Ratio (%)')
        logo_positions_output = gr.Textbox(label='Logo Positions')
    with gr.Row():
        person_count_output = gr.Textbox(label='Person Count')
        animal_count_output = gr.Textbox(label='Animal Count')
        transportation_count_output = gr.Textbox(label='Other Transportation Object Count')
    with gr.Row():
        sports_item_count_output = gr.Textbox(label='Sports Equipment Count')
        food_item_count_output = gr.Textbox(label='Food and Dining Item Count')
        qr_code_count_output = gr.Textbox(label='QR Code Count')  # QR code count output
        
    gr.Markdown("## Textual Features")
    with gr.Row():
        num_char = gr.Textbox(label='Character Count')
        text_area_ratio = gr.Textbox(label='Text Area Ratio (%)')
        sentiment_polarity_output = gr.Textbox(label='Sentiment Polarity (-1 to 1)')

    with gr.Row():
        subjectivity_output = gr.Textbox(label='Subjectivity (0 to 1)')
        url_count_output = gr.Textbox(label='URL Count')
        price_indication_count_output = gr.Textbox(label='Price Indication Count')

    with gr.Row():
        promotion_indication_count_output = gr.Textbox(label='Promotion Indication Count')
        call_to_action_count_output = gr.Textbox(label='Call-to-Action Count')
        brand_salience_output = gr.Textbox(label='Brand Salience')

    gr.Markdown("## Facial Emotions")
    with gr.Row():
        emo_angry_output = gr.Textbox(label='Angry')
        emo_disgust_output = gr.Textbox(label='Disgust')
        emo_fear_output = gr.Textbox(label='Fear')
    with gr.Row():
        emo_happy_output = gr.Textbox(label='Happy')
        emo_sad_output = gr.Textbox(label='Sad')
        emo_surprise_output = gr.Textbox(label='Surprise')
        emo_neutral_output = gr.Textbox(label='Neutral')

    
    # Define the function to be called when the button is clicked
    def process_and_display(focal_image, same_brand_files, competitive_brand_files):
        # Call the process_image function and get the outputs
        outputs = process_image(focal_image, same_brand_files, competitive_brand_files)
        # Set the dominant color indicator
        dominant_color_hex = outputs[4]
        dominant_color_indicator = dominant_color_hex if dominant_color_hex else '#FFFFFF'
        outputs.insert(5, dominant_color_indicator)  # Insert after dominant color output

        ad_elasticity_value = outputs[2]  
        ad_elasticity_card_html = get_elasticity_card(ad_elasticity_value)
        outputs[2]=ad_elasticity_card_html

        consistency_score_value = outputs[1]
        consistency_score_card_html = get_consistency_card(consistency_score_value)
        outputs[1]=consistency_score_card_html

        distinctiveness_score_value = outputs[0]
        distinctiveness_score_card_html = get_distinctiveness_card(distinctiveness_score_value)
        outputs[0]=distinctiveness_score_card_html

        return outputs

    # Set up the event handler
    run_button.click(
        fn=process_and_display,
        inputs=[focal_ad, same_brand_ads, competitive_brand_ads],
        outputs=[
            distinctiveness_score,
            consistency_score,
            ad_elasticity,
            resolution_output,
            dominant_color_output,
            dominant_color_indicator,
            hue_category_output,
            avg_r_output,
            avg_g_output,
            avg_b_output,
            visual_complexity_output,
            car_count_output,
            car_coverage_ratio_output,
            car_positions_output,
            logo_count_output,
            logo_coverage_ratio_output,
            logo_positions_output,
            person_count_output,
            animal_count_output,
            transportation_count_output,
            sports_item_count_output,
            food_item_count_output,
            qr_code_count_output,
            num_char,
            text_area_ratio,
            sentiment_polarity_output,
            subjectivity_output,
            url_count_output,
            price_indication_count_output,
            promotion_indication_count_output,
            call_to_action_count_output,
            brand_salience_output,
            emo_angry_output,
            emo_disgust_output,
            emo_fear_output,
            emo_happy_output,
            emo_sad_output,
            emo_surprise_output,
            emo_neutral_output
        ]
    )

# Launch the app
demo.launch()