Untitled

# Step 1: Data Collection (example using a mock dataset)
import pandas as pd

# Load dataset of top-selling patterns from Amazon
patterns_df = pd.read_csv('top_selling_dinnerware_patterns.csv')

# Step 2: Feature Extraction
def extract_pattern_features(df):
    # Extract common color schemes
    color_schemes = df['color'].apply(lambda x: extract_colors(x))
    
    # Extract design elements like motifs (floral, geometric)
    design_elements = df['design'].apply(lambda x: extract_motifs(x))
    
    return color_schemes, design_elements

# Step 3: Generate New Pattern
def generate_new_pattern(color_schemes, design_elements):
    # Randomly combine popular design elements with a new twist
    new_color = random.choice(color_schemes)
    new_motif = random.choice(design_elements)
    return create_pattern(new_color, new_motif)

# Step 4: Evaluate Pattern
def evaluate_pattern(pattern):
    score = evaluate_aesthetics(pattern)
    if score > threshold:
        return pattern
    else:
        return regenerate_pattern()

# Main function
def main():
    color_schemes, design_elements = extract_pattern_features(patterns_df)
    new_pattern = generate_new_pattern(color_schemes, design_elements)
    final_pattern = evaluate_pattern(new_pattern)
    return final_pattern