import gradio as gr
import random
import time
from PIL import Image
import numpy as np

# Simulated reviewer responses
REVIEWER_NAMES = ["Sophia", "Emma", "Olivia", "Ava", "Isabella", "Mia", "Charlotte", "Amelia"]
ADJECTIVES = ["interesting", "unexpected", "unique", "curious", "unconventional", "distinctive"]
FEEDBACK_TEMPLATES = [
    "The composition is {adjective}, and I appreciate how {lighting} the lighting is. The {aspect} aspect stands out to me because {reason}.",
    "What strikes me first is the {texture} texture. It's {comparison} compared to others I've seen. I'd rate this {rating} because {justification}.",
    "The {color} tones create a {mood} atmosphere. Personally, I think {improvement} would enhance the overall presentation. The uniqueness makes it {rating}.",
    "From an artistic perspective, the {perspective} perspective is {adjective}. It makes me feel {emotion} because {personal_connection}.",
    "The technical quality is {quality}, especially considering {technical_aspect}. If I had to suggest something, {suggestion}. Overall {rating}."
]

def generate_detailed_review():
    """Generate a realistic, detailed review from a simulated reviewer"""
    name = random.choice(REVIEWER_NAMES)
    rating = random.randint(1, 10)
    template = random.choice(FEEDBACK_TEMPLATES)
    
    # Fill template with random details
    review = template.format(
        adjective=random.choice(ADJECTIVES),
        lighting=random.choice(["soft", "dramatic", "natural", "artificial"]),
        aspect=random.choice(["shape", "form", "proportion", "contour"]),
        reason=random.choice(["it challenges conventional beauty standards", "it shows authentic vulnerability", "it's refreshingly imperfect"]),
        texture=random.choice(["smooth", "rough", "veiny", "wrinkled"]),
        comparison=random.choice(["more organic", "less symmetrical", "more expressive"]),
        justification=random.choice(["it represents raw human form", "it defies unrealistic expectations", "it tells a story"]),
        color=random.choice(["flesh-toned", "pinkish", "rosy", "coral"]),
        mood=random.choice(["intimate", "vulnerable", "private", "personal"]),
        improvement=random.choice(["better background contrast", "more creative angles", "softer shadows"]),
        perspective=random.choice(["foreshortened", "close-up", "macro"]),
        emotion=random.choice(["intrigued", "curious", "amused", "surprised"]),
        personal_connection=random.choice(["it reminds me of classical sculptures", "it feels authentically human"]),
        quality=random.choice(["decent", "acceptable", "reasonable"]),
        technical_aspect=random.choice(["the focus", "the exposure", "the depth of field"]),
        suggestion=random.choice(["experiment with black and white", "try different lighting setups", "use props for context"]),
        rating=f"{rating}/10"
    )
    
    return f"{name} ({rating}/10): {review}"

def process_image(image):
    """Process the uploaded image and generate reviews"""
    # Convert to PIL Image and process
    pil_image = Image.fromarray(image.astype('uint8'), 'RGB')
    width, height = pil_image.size
    
    # Simulate processing time
    time.sleep(random.uniform(1.0, 3.0))
    
    # Generate multiple detailed reviews
    num_reviews = random.randint(5, 15)
    reviews = [generate_detailed_review() for _ in range(num_reviews)]
    
    # Create results dictionary
    return {
        "dimensions": f"{width}x{height} pixels",
        "reviews": "\n\n".join(reviews),
        "average_rating": f"{random.uniform(4.0, 9.5):.1f}/10"
    }

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# 🖼️ Personalized Image Review Portal")
    gr.Markdown("Upload an image for detailed, thoughtful feedback from our review panel")
    
    with gr.Row():
        with gr.Column(scale=1):
            image_input = gr.Image(label="Upload Image", type="numpy")
            submit_btn = gr.Button("Get Reviews", variant="primary")
            
            with gr.Accordion("⚙️ Settings", open=False):
                gr.Markdown("### Review Preferences")
                num_reviewers = gr.Slider(5, 20, value=10, label="Number of Reviewers")
                detail_level = gr.Radio(["Brief", "Balanced", "Detailed"], value="Detailed", label="Feedback Detail")
                gr.Markdown("*Note: All reviews are generated locally*")
        
        with gr.Column(scale=2):
            with gr.Tab("📝 Reviews"):
                reviews_output = gr.Textbox(label="Detailed Feedback", lines=15, max_lines=20)
            
            with gr.Tab("📊 Summary"):
                gr.Markdown("### Review Summary")
                dimensions_output = gr.Textbox(label="Image Dimensions", interactive=False)
                avg_rating_output = gr.Textbox(label="Average Rating", interactive=False)
                gr.Markdown("---")
                gr.Markdown("#### Rating Distribution")
                gr.BarPlot(value=[(f"{i}-{i+1}", random.randint(2, 8)) for i in range(1, 10, 2)], 
                           x="Rating Range", y="Reviewers", title="Rating Distribution")
    
    gr.Markdown("---")
    gr.Markdown("🔒 This is a local application - your images remain on your device")
    gr.Markdown("Built with [AnyCoder](https://huggingface.co/spaces/akhaliq/anycoder)")
    
    # Event handling
    submit_btn.click(
        fn=process_image,
        inputs=image_input,
        outputs={
            "dimensions": dimensions_output,
            "reviews": reviews_output,
            "average_rating": avg_rating_output
        }
    )

# Launch with modern theme
demo.launch(
    theme=gr.themes.Soft(
        primary_hue="blue",
        secondary_hue="purple",
        font=[gr.themes.GoogleFont("Montserrat"), "sans-serif"]
    ),
    css="footer {visibility: hidden}",
    head="<style>.gradio-container {max-width: 1200px !important;}</style>"
)