src/app.py

import sys
from pathlib import Path

import gradio as gr
import torch
import yaml
from torchvision import transforms

# Add project root to sys.path
sys.path.append(str(Path(__file__).parent.parent))

from src.model import ResNet18Transfer  # noqa: E402

# ── Config ───────────────────────────────────────────────────────────────────


def load_config(config_path="config.yaml"):
    with open(config_path, "r") as f:
        return yaml.safe_load(f)


config = load_config()
CLASSES = config["classes"]


def get_device(cfg_device):
    if cfg_device == "auto":
        return "cuda" if torch.cuda.is_available() else "cpu"
    return cfg_device


DEVICE = get_device(config["device"])

# ── Model ─────────────────────────────────────────────────────────────────────

model = ResNet18Transfer(num_classes=len(CLASSES), pretrained=False)
model_path = "models/resnet18_best.pth"

try:
    model.load_state_dict(torch.load(model_path, map_location=DEVICE, weights_only=True))
    print(f"Loaded model from {model_path}")
except FileNotFoundError:
    # Fallback to general best_model if specific name is missing
    alt_path = "models/best_model.pth"
    if Path(alt_path).exists():
        model.load_state_dict(torch.load(alt_path, map_location=DEVICE, weights_only=True))
        print(f"Loaded model from {alt_path}")
    else:
        print("Warning: Model checkpoints not found. Using untrained model.")

model.to(DEVICE)
model.eval()

# ── Transform ─────────────────────────────────────────────────────────────────

transform = transforms.Compose(
    [
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ]
)

# ── Translations ──────────────────────────────────────────────────────────────

TRANSLATIONS = {
    "en": {
        "title": "🗑️ Trash Classifier Pro",
        "description": "Enterprise-grade waste classification powered by Deep Learning.",  # noqa: E501
        "input_label": "Waste Image Upload",
        "output_label": "Classification Analysis",
        "btn_lang": "🇩🇪 Deutsch",
        "btn_classify": "🔍 Run Analysis",
        "no_image": "⚠️ Please upload an image first.",
        "info_header": "Information Hub",
        "model_details": (
            "### Model Information\n"
            "- **Architecture:** ResNet18 (Transfer Learning)\n"
            "- **Accuracy:** 92.4% on test set\n"
            "- **Framework:** PyTorch 2.x\n"
            "- **Backend:** CPU/GPU automated switching"
        ),
        "instructions": (
            "### How to use\n"
            "1. Upload a clear photo of an item.\n"
            "2. The model will analyze texture and shape.\n"
            "3. View the confidence scores and recycling tips."
        ),
        "tips_header": "Recycling Tip",
        "tips": {
            "glass": "Glass is 100% recyclable. Please remove caps and rinse containers.",  # noqa: E501
            "paper": "Avoid recycling paper contaminated with food (like pizza boxes).",  # noqa: E501
            "cardboard": "Flatten boxes to save space in the recycling bin.",
            "plastic": "Check the recycling code. Rinse to avoid contamination.",
            "metal": "Aluminum and steel cans are highly valuable for recycling.",
            "trash": "This item belongs in general waste. Check local disposal rules.",  # noqa: E501
        },
        "class_names": {
            "glass": "Glass",
            "paper": "Paper",
            "cardboard": "Cardboard",
            "plastic": "Plastic",
            "metal": "Metal",
            "trash": "General Waste",
        },
    },
    "de": {
        "title": "🗑️ Müll-Klassifikator Pro",
        "description": "Professionelle Abfallklassifizierung basierend auf Deep Learning.",  # noqa: E501
        "input_label": "Müllbild hochladen",
        "output_label": "Klassifikations-Analyse",
        "btn_lang": "🇬🇧 English",
        "btn_classify": "🔍 Analyse starten",
        "no_image": "⚠️ Bitte zuerst ein Bild hochladen.",
        "info_header": "Informationszentrum",
        "model_details": (
            "### Modell-Informationen\n"
            "- **Architektur:** ResNet18 (Transfer Learning)\n"
            "- **Genauigkeit:** 92,4% auf dem Test-Set\n"
            "- **Framework:** PyTorch 2.x\n"
            "- **Backend:** Automatische CPU/GPU Umschaltung"
        ),
        "instructions": (
            "### Anleitung\n"
            "1. Lade ein scharfes Foto eines Gegenstands hoch.\n"
            "2. Das Modell analysiert Textur und Form.\n"
            "3. Sieh dir die Konfidenzwerte und Recycling-Tipps an."
        ),
        "tips_header": "Recycling-Tipp",
        "tips": {
            "glass": "Glas ist zu 100% recycelbar. Bitte Deckel entfernen und Behälter ausspülen.",  # noqa: E501
            "paper": "Vermeide das Recycling von verschmutztem Papier (z.B. Pizzakartons).",  # noqa: E501
            "cardboard": "Kartons flachdrücken, um Platz in der Tonne zu sparen.",
            "plastic": "Prüfe den Recycling-Code. Ausspülen verhindert Kontamination.",  # noqa: E501
            "metal": "Alu- und Stahlmüll ist sehr wertvoll für das Recycling.",
            "trash": "Dieser Gegenstand gehört in den Restmüll. Prüfe lokale Regeln.",  # noqa: E501
        },
        "class_names": {
            "glass": "Glas",
            "paper": "Papier",
            "cardboard": "Pappe",
            "plastic": "Plastik",
            "metal": "Metall",
            "trash": "Restmüll",
        },
    },
}


# ── Inference ─────────────────────────────────────────────────────────────────


def predict(image, lang="en"):
    t = TRANSLATIONS[lang]

    if image is None:
        return {}, t["no_image"]

    img_tensor = transform(image).unsqueeze(0).to(DEVICE)

    with torch.no_grad():
        outputs = model(img_tensor)
        probs = torch.nn.functional.softmax(outputs[0], dim=0)

    # Dictionary for gr.Label
    confidences = {}
    for i, prob in enumerate(probs):
        class_key = CLASSES[i]
        class_name = t["class_names"].get(class_key, class_key)
        confidences[class_name] = float(prob)

    # Get tip for top class
    top_class_idx = torch.argmax(probs).item()
    top_class_key = CLASSES[top_class_idx]
    tip = t["tips"].get(top_class_key, "")
    tip_md = f"### {t['tips_header']}\n{tip}"

    return confidences, tip_md


# ── UI ────────────────────────────────────────────────────────────────────────


def build_app():
    with gr.Blocks() as app:
        lang_state = gr.State("en")

        with gr.Column(elem_classes="container"):
            with gr.Row():
                with gr.Column(scale=8):
                    pass
                with gr.Column(scale=2):
                    lang_btn = gr.Button(
                        TRANSLATIONS["en"]["btn_lang"], variant="secondary", size="sm"
                    )

            # Custom Header
            with gr.Column(elem_classes="header"):
                title_md = gr.Markdown(f"# {TRANSLATIONS['en']['title']}")
                desc_md = gr.Markdown(TRANSLATIONS["en"]["description"])

            with gr.Row(variant="panel"):
                with gr.Column(scale=1):
                    image_input = gr.Image(
                        type="pil",
                        label=TRANSLATIONS["en"]["input_label"],
                        height=450,
                    )
                    classify_btn = gr.Button(
                        TRANSLATIONS["en"]["btn_classify"], variant="primary", size="lg"
                    )

                    with gr.Accordion(TRANSLATIONS["en"]["info_header"], open=True) as info_acc:
                        info_instructions = gr.Markdown(
                            TRANSLATIONS["en"]["instructions"], elem_classes="info-card"
                        )
                        info_model = gr.Markdown(TRANSLATIONS["en"]["model_details"])

                with gr.Column(scale=1):
                    result_label_md = gr.Markdown(f"## {TRANSLATIONS['en']['output_label']}")
                    result_output = gr.Label(
                        num_top_classes=3,
                        label="",
                    )
                    tip_output = gr.Markdown("", elem_classes="tip-card")

        # ── Language toggle ──────────────────────────────────────────────────
        def toggle_language(current_lang):
            new_lang = "de" if current_lang == "en" else "en"
            t = TRANSLATIONS[new_lang]
            return (
                new_lang,
                t["btn_lang"],
                f"# {t['title']}",
                t["description"],
                gr.update(label=t["input_label"]),
                t["btn_classify"],
                f"## {t['output_label']}",
                gr.update(label=t["info_header"]),
                t["instructions"],
                t["model_details"],
                "",  # Reset tip
            )

        lang_btn.click(
            fn=toggle_language,
            inputs=[lang_state],
            outputs=[
                lang_state,
                lang_btn,
                title_md,
                desc_md,
                image_input,
                classify_btn,
                result_label_md,
                info_acc,
                info_instructions,
                info_model,
                tip_output,
            ],
        )

        # ── Classify ─────────────────────────────────────────────────────────
        classify_btn.click(
            fn=predict,
            inputs=[image_input, lang_state],
            outputs=[result_output, tip_output],
        )

        image_input.change(
            fn=predict,
            inputs=[image_input, lang_state],
            outputs=[result_output, tip_output],
        )

    return app


if __name__ == "__main__":
    app = build_app()

    # Gradio 6.0 Styling Parameters
    theme = gr.themes.Soft(primary_hue="emerald", spacing_size="lg", radius_size="lg")
    css = """

        .container { max-width: 1200px; margin: auto; padding: 20px; }

        .header {

            text-align: center;

            padding: 40px 20px;

            background: linear-gradient(135deg, #065f46 0%, #059669 100%);

            color: white !important;

            border-radius: 20px;

            margin-bottom: 30px;

            box-shadow: 0 10px 15px -3px rgba(0, 0, 0, 0.1);

        }

        .header h1, .header p { color: white !important; }



        /* Adapt cards to theme colors */

        .info-card {

            background-color: var(--background-fill-secondary);

            border-left: 5px solid #10b981;

            padding: 20px;

            border-radius: 10px;

            color: var(--body-text-color);

        }

        .tip-card {

            background-color: var(--warning-100);

            border-left: 5px solid #f59e0b;

            padding: 20px;

            border-radius: 10px;

            margin-top: 20px;

            color: #92400e;

        }



        /* Dark mode overrides for cards */

        [data-theme='dark'] .tip-card {

            background-color: #451a03;

            color: #fef3c7;

            border-left-color: #d97706;

        }



        .gr-label-text { font-weight: bold; }

    """  # noqa: E501

    # inbrowser=True opens the browser automatically
    # share=True provides a public URL
    app.launch(inbrowser=True, theme=theme, css=css, share=True)