app_original.py

import glob
import os

import gradio as gr
import numpy as np
import torch
from diffusers import AutoPipelineForText2Image
from simple_train import simple_train
from sklearn.cluster import KMeans
from transformers import (
    AutoImageProcessor,
    AutoModelForImageClassification,
    ConvNextForImageClassification,
    ConvNextImageProcessor,
    pipeline,
)

MODEL_ID = os.getenv("MODEL_ID", "stabilityai/sdxl-turbo")

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32

pipe = AutoPipelineForText2Image.from_pretrained(MODEL_ID, torch_dtype=dtype).to(device)
if device == "cuda":
    try:
        pipe.enable_xformers_memory_efficient_attention()
    except Exception:
        pipe.enable_attention_slicing()
else:
    pipe.enable_attention_slicing()


def generate(prompt, steps, width, height, seed):
    if seed is None or int(seed) < 0:
        generator = None
    else:
        generator = torch.Generator(device=device).manual_seed(int(seed))

    result = pipe(
        prompt=prompt,
        num_inference_steps=int(steps),
        guidance_scale=0.0,  # SDXL-Turbo works best at 0.0
        width=int(width // 8) * 8,
        height=int(height // 8) * 8,
        generator=generator,
    )
    return result.images[0]


# ---------- Flower identification (zero-shot) ----------
# Curated label set; edit/extend as you like
FLOWER_LABELS = [
    "rose",
    "tulip",
    "lily",
    "peony",
    "sunflower",
    "chrysanthemum",
    "carnation",
    "orchid",
    "hydrangea",
    "daisy",
    "dahlia",
    "ranunculus",
    "anemone",
    "marigold",
    "lavender",
    "magnolia",
    "gardenia",
    "camellia",
    "jasmine",
    "iris",
    "gerbera",
    "zinnia",
    "hibiscus",
    "lotus",
    "poppy",
    "sweet pea",
    "freesia",
    "lisianthus",
    "calla lily",
    "cherry blossom",
    "plumeria",
    "cosmos",
]

# Initialize classifier - will be updated when trained model is loaded
clf_device = 0 if torch.cuda.is_available() else -1
zs_classifier = None
convnext_model = None
convnext_processor = None
current_model_path = "facebook/convnext-base-224-22k"


def load_classifier(model_path="facebook/convnext-base-224-22k"):
    global zs_classifier, convnext_model, convnext_processor, current_model_path
    try:
        if os.path.exists(model_path):
            # Load custom trained model
            convnext_model = AutoModelForImageClassification.from_pretrained(model_path)
            convnext_processor = AutoImageProcessor.from_pretrained(model_path)
            current_model_path = model_path
            # Also keep zero-shot classifier for fallback
            zs_classifier = pipeline(
                task="zero-shot-image-classification",
                model="openai/clip-vit-base-patch32",
                device=clf_device,
            )
            return f"✅ Loaded custom ConvNeXt model from: {model_path}"
        else:
            # Load default ConvNeXt model for feature extraction and fallback to CLIP for zero-shot
            convnext_model = ConvNextForImageClassification.from_pretrained(
                "facebook/convnext-base-224-22k"
            )
            convnext_processor = ConvNextImageProcessor.from_pretrained(
                "facebook/convnext-base-224-22k"
            )
            zs_classifier = pipeline(
                task="zero-shot-image-classification",
                model="openai/clip-vit-base-patch32",
                device=clf_device,
            )
            current_model_path = "facebook/convnext-base-224-22k"
            return "✅ Loaded default ConvNeXt model: facebook/convnext-base-224-22k"
    except Exception as e:
        return f"❌ Error loading model: {e!s}"


# Initialize with default model
load_classifier()


def identify_flowers(image, candidate_labels, top_k, min_score):
    if image is None:
        return [], "Please provide an image (upload or generate first)."

    labels = candidate_labels if candidate_labels else FLOWER_LABELS

    # Use ConvNeXt for feature extraction if we have a trained model, otherwise fallback to CLIP
    if (
        convnext_model is not None
        and os.path.exists(current_model_path)
        and current_model_path != "facebook/convnext-base-224-22k"
    ):
        try:
            # Use trained ConvNeXt model
            inputs = convnext_processor(images=image, return_tensors="pt")
            with torch.no_grad():
                outputs = convnext_model(**inputs)
                predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

            # Convert predictions to results format
            results = []
            for i, score in enumerate(predictions[0]):
                if i < len(labels):
                    results.append({"label": labels[i], "score": float(score)})

            # Sort by score
            results = sorted(results, key=lambda r: r["score"], reverse=True)
        except Exception:
            # Fallback to CLIP zero-shot
            results = zs_classifier(
                image, candidate_labels=labels, hypothesis_template="a photo of a {}"
            )
    else:
        # Use CLIP zero-shot classification
        results = zs_classifier(
            image, candidate_labels=labels, hypothesis_template="a photo of a {}"
        )

    # Filter and format results
    results = [r for r in results if r["score"] >= float(min_score)]
    results = sorted(results, key=lambda r: r["score"], reverse=True)[: int(top_k)]
    table = [[r["label"], round(float(r["score"]), 4)] for r in results]
    model_type = (
        "ConvNeXt"
        if (
            convnext_model is not None
            and os.path.exists(current_model_path)
            and current_model_path != "facebook/convnext-base-224-22k"
        )
        else "CLIP zero-shot"
    )
    msg = f"Detected flowers using {model_type}."
    return table, msg


# simple passthrough so the generated image appears in the Identify tab automatically
def passthrough(img):
    return img


# Training functions
def get_available_models():
    models_dir = "training_data/trained_models"
    if not os.path.exists(models_dir):
        return ["facebook/convnext-base-224-22k (default)"]

    models = ["facebook/convnext-base-224-22k (default)"]
    for item in os.listdir(models_dir):
        model_path = os.path.join(models_dir, item)
        if os.path.isdir(model_path) and os.path.exists(
            os.path.join(model_path, "config.json")
        ):
            models.append(f"Custom: {item}")
    return models


def count_training_images():
    images_dir = "training_data/images"
    if not os.path.exists(images_dir):
        return "Training directory not found"

    total_images = 0
    flower_counts = {}

    for flower_type in os.listdir(images_dir):
        flower_path = os.path.join(images_dir, flower_type)
        if os.path.isdir(flower_path):
            image_files = (
                glob.glob(os.path.join(flower_path, "*.jpg"))
                + glob.glob(os.path.join(flower_path, "*.jpeg"))
                + glob.glob(os.path.join(flower_path, "*.png"))
                + glob.glob(os.path.join(flower_path, "*.webp"))
            )
            count = len(image_files)
            if count > 0:
                flower_counts[flower_type] = count
                total_images += count

    if total_images == 0:
        return "No training images found. Add images to subdirectories in training_data/images/"

    result = f"**Total images: {total_images}**\n\n"
    for flower_type, count in sorted(flower_counts.items()):
        result += f"- {flower_type}: {count} images\n"

    return result


def start_training(epochs=None, batch_size=None, learning_rate=None):
    try:
        # Check if training data exists
        images_dir = "training_data/images"
        if not os.path.exists(images_dir):
            return "❌ Training directory not found. Please create training_data/images/ and add your data."

        # Count images
        total_images = 0
        for flower_type in os.listdir(images_dir):
            flower_path = os.path.join(images_dir, flower_type)
            if os.path.isdir(flower_path):
                image_files = (
                    glob.glob(os.path.join(flower_path, "*.jpg"))
                    + glob.glob(os.path.join(flower_path, "*.jpeg"))
                    + glob.glob(os.path.join(flower_path, "*.png"))
                    + glob.glob(os.path.join(flower_path, "*.webp"))
                )
                total_images += len(image_files)

        if total_images < 10:
            return f"❌ Need at least 10 training images. Found {total_images}. Add more images to training_data/images/"

        # Start training
        model_path = simple_train()

        if model_path:
            return f"✅ Training completed! Model saved to: {model_path}"
        else:
            return "❌ Training failed. Check the console for details."

    except Exception as e:
        return f"❌ Training error: {e!s}"


def load_trained_model(model_selection):
    if model_selection.startswith("Custom:"):
        model_name = model_selection.replace("Custom: ", "")
        model_path = os.path.join("training_data/trained_models", model_name)
        return load_classifier(model_path)
    else:
        return load_classifier("facebook/convnext-base-224-22k")


# French-style arrangement functions
def extract_dominant_colors(image, num_colors=5):
    """Extract dominant colors from an image using k-means clustering"""
    if image is None:
        return [], "No image provided"

    # Convert PIL image to numpy array
    img_array = np.array(image)

    # Reshape image to be a list of pixels
    pixels = img_array.reshape(-1, 3)

    # Use k-means to find dominant colors
    kmeans = KMeans(n_clusters=num_colors, random_state=42, n_init=10)
    kmeans.fit(pixels)

    # Get the colors and convert to RGB values
    colors = kmeans.cluster_centers_.astype(int)

    # Convert to color names/descriptions
    color_names = []
    for color in colors:
        r, g, b = color
        if r > 200 and g > 200 and b > 200:
            color_names.append("white")
        elif r < 50 and g < 50 and b < 50:
            color_names.append("black")
        elif r > g and r > b:
            if r > 150 and g < 100:
                color_names.append("red" if g < 50 else "pink")
            else:
                color_names.append("coral")
        elif g > r and g > b:
            if b < 100:
                color_names.append("yellow" if g > 200 and r > 150 else "green")
            else:
                color_names.append("teal")
        elif b > r and b > g:
            if r < 100:
                color_names.append("blue" if b > 150 else "navy")
            else:
                color_names.append("purple" if r > g else "lavender")
        elif r > 150 and g > 100 and b < 100:
            color_names.append("orange")
        else:
            color_names.append("cream")

    return color_names, colors


def analyze_and_generate_french_style(image):
    """Analyze uploaded flower image and generate French-style arrangement"""
    if image is None:
        return None, "Please upload an image", ""

    # Identify the flower type
    if zs_classifier is None:
        return None, "Model not loaded", ""

    try:
        progress_log = "🔄 **Step 1/4:** Starting flower analysis...\n\n"

        # Identify flower
        progress_log += "🔍 Identifying flower type using AI model...\n"
        results = zs_classifier(
            image, candidate_labels=FLOWER_LABELS, hypothesis_template="a photo of a {}"
        )

        top_flower = results[0]["label"] if results else "flower"
        confidence = results[0]["score"] if results else 0
        progress_log += (
            f"✅ Identified: **{top_flower}** (confidence: {confidence:.2%})\n\n"
        )

        # Extract dominant colors
        progress_log += "🔄 **Step 2/4:** Analyzing color palette...\n\n"
        progress_log += "🎨 Extracting dominant colors from image...\n"
        color_names, color_rgb = extract_dominant_colors(image, num_colors=3)

        # Create color description
        main_colors = color_names[:3]  # Top 3 colors
        color_desc = ", ".join(main_colors)
        progress_log += f"✅ Color palette: **{color_desc}**\n\n"

        # Generate French-style prompt
        progress_log += (
            "🔄 **Step 3/4:** Creating French-style arrangement prompt...\n\n"
        )
        prompt = f"elegant French-style floral arrangement featuring {top_flower}s in {color_desc} colors, displayed in a clear crystal vase on a marble kitchen countertop, soft natural lighting, minimalist French country kitchen background, professional photography, sophisticated composition"
        progress_log += f"✅ Prompt created: *{prompt[:100]}...*\n\n"

        # Generate the image
        progress_log += (
            "🔄 **Step 4/4:** Generating French-style arrangement image...\n\n"
        )
        progress_log += "🖼️ Using AI image generation (SDXL-Turbo)...\n"
        generated_image = generate(prompt, steps=4, width=1024, height=1024, seed=-1)
        progress_log += "✅ French-style arrangement generated successfully!\n\n"

        # Create analysis summary
        analysis = f"""
**🌸 Flower Analysis:**
- **Type:** {top_flower} (confidence: {confidence:.2%})
- **Dominant Colors:** {color_desc}

**🇫🇷 Generated Prompt:**
"{prompt}"

---

**📋 Process Log:**
{progress_log}
"""

        return (
            generated_image,
            "✅ Analysis complete! French-style arrangement generated.",
            analysis,
        )

    except Exception as e:
        error_log = f"❌ **Error occurred during processing:**\n\n{e!s}\n\n"
        if "progress_log" in locals():
            error_log += f"**Progress before error:**\n{progress_log}"
        return None, f"❌ Error: {e!s}", error_log


# ---------- UI ----------
with gr.Blocks() as demo:
    gr.Markdown("# 🌸 SDXL-Turbo — Text → Image + Flower Identifier")

    with gr.Tabs():
        with gr.TabItem("Generate"):
            with gr.Row():
                with gr.Column():
                    prompt = gr.Textbox(
                        value="ikebana-style flower arrangement, soft natural light, minimalist",
                        label="Prompt",
                    )
                    steps = gr.Slider(1, 8, value=4, step=1, label="Steps")
                    width = gr.Slider(512, 1536, value=1024, step=8, label="Width")
                    height = gr.Slider(512, 1536, value=1024, step=8, label="Height")
                    seed = gr.Number(value=-1, precision=0, label="Seed (-1 = random)")
                    go = gr.Button("Generate", variant="primary")
                out = gr.Image(label="Result", type="pil")

        with gr.TabItem("Identify"):
            with gr.Row():
                with gr.Column():
                    img_in = gr.Image(
                        label="Image (upload or auto-filled from 'Generate')",
                        type="pil",
                        interactive=True,
                    )
                    labels_box = gr.CheckboxGroup(
                        choices=FLOWER_LABELS,
                        value=[
                            "rose",
                            "tulip",
                            "lily",
                            "peony",
                            "hydrangea",
                            "orchid",
                            "sunflower",
                        ],
                        label="Candidate labels (edit as needed)",
                    )
                    topk = gr.Slider(1, 15, value=7, step=1, label="Top-K")
                    min_score = gr.Slider(
                        0.0, 1.0, value=0.12, step=0.01, label="Min confidence"
                    )
                    detect_btn = gr.Button("Identify Flowers", variant="primary")
                with gr.Column():
                    results_tbl = gr.Dataframe(
                        headers=["Flower", "Confidence"],
                        datatype=["str", "number"],
                        interactive=False,
                    )
                    status = gr.Markdown()

        with gr.TabItem("Train Model"):
            gr.Markdown("## 🎯 Fine-tune the flower identification model")
            gr.Markdown(
                "Organize your training images in subdirectories by flower type in `training_data/images/`"
            )
            gr.Markdown(
                "Example: `training_data/images/roses/`, `training_data/images/tulips/`, etc."
            )

            with gr.Row():
                with gr.Column():
                    gr.Markdown("### Training Data")
                    refresh_btn = gr.Button("🔄 Refresh Data Count", size="sm")
                    data_status = gr.Markdown()

                    gr.Markdown("### Training Parameters")
                    epochs = gr.Slider(1, 20, value=5, step=1, label="Training Epochs")
                    batch_size = gr.Slider(1, 16, value=8, step=1, label="Batch Size")
                    learning_rate = gr.Number(
                        value=1e-5, label="Learning Rate", precision=6
                    )

                    train_btn = gr.Button("🚀 Start Training", variant="primary")

                with gr.Column():
                    gr.Markdown("### Model Management")
                    model_dropdown = gr.Dropdown(
                        choices=get_available_models(),
                        value="facebook/convnext-base-224-22k (default)",
                        label="Select Model",
                    )
                    refresh_models_btn = gr.Button("🔄 Refresh Models", size="sm")
                    load_model_btn = gr.Button(
                        "📥 Load Selected Model", variant="secondary"
                    )

                    model_status = gr.Markdown(
                        f"**Current model:** {current_model_path}"
                    )

                    gr.Markdown("### Training Status")
                    training_output = gr.Markdown()

        with gr.TabItem("French Style arrangement"):
            gr.Markdown("## 🇫🇷 French-Style Flower Arrangements")
            gr.Markdown(
                "Upload a flower image and generate an elegant French-style arrangement with matching colors!"
            )

            with gr.Row():
                with gr.Column():
                    upload_img = gr.Image(label="Upload Flower Image", type="pil")
                    analyze_btn = gr.Button(
                        "🎨 Analyze & Generate French Style",
                        variant="primary",
                        size="lg",
                    )

                with gr.Column():
                    french_result = gr.Image(
                        label="Generated French-Style Arrangement", type="pil"
                    )
                    french_status = gr.Markdown()
                    analysis_details = gr.Markdown()

    # Wire events
    go.click(generate, [prompt, steps, width, height, seed], [out])
    # Auto-send generated image to Identify tab
    out.change(passthrough, inputs=out, outputs=img_in)
    # Run identification
    detect_btn.click(
        identify_flowers, [img_in, labels_box, topk, min_score], [results_tbl, status]
    )

    # Training tab events
    refresh_btn.click(count_training_images, outputs=[data_status])
    refresh_models_btn.click(
        lambda: gr.Dropdown(choices=get_available_models()), outputs=[model_dropdown]
    )
    load_model_btn.click(
        load_trained_model, inputs=[model_dropdown], outputs=[model_status]
    )
    train_btn.click(
        start_training,
        inputs=[epochs, batch_size, learning_rate],
        outputs=[training_output],
    )

    # French Style tab events - update status during processing
    def update_french_status():
        return "🔄 Processing... Please wait while we analyze your flower image...", ""

    analyze_btn.click(
        update_french_status, outputs=[french_status, analysis_details]
    ).then(
        analyze_and_generate_french_style,
        inputs=[upload_img],
        outputs=[french_result, french_status, analysis_details],
    )

    # Initialize data count on load
    demo.load(count_training_images, outputs=[data_status])

demo.queue().launch()