"""
Netra AI - Public Interface
Calls protected backend space for classification
"""

import gradio as gr
from gradio_client import Client
from huggingface_hub import login
import os

# Global backend client
backend = None

def get_backend():
    """Lazy initialization of backend client"""
    global backend
    if backend is None:
        HF_TOKEN = os.getenv("HF_TOKEN")
        if HF_TOKEN:
            login(token=HF_TOKEN)
        backend = Client("rg321/Construction_Material_Classification")
    return backend


def classify_material(image):
    """Proxy function that calls protected backend"""
    if image is None:
        return "Please upload an image", "", ""

    try:
        import tempfile
        from PIL import Image as PILImage
        from gradio_client import handle_file

        print(f"📤 Calling backend API with image type: {type(image)}")

        # Convert PIL Image to temp file for gradio_client
        if isinstance(image, PILImage.Image):
            with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg', mode='wb') as tmp:
                image.save(tmp, 'JPEG')
                image_path = tmp.name
        else:
            image_path = image

        print(f"📤 Sending file: {image_path}")

        # Call protected space API with properly formatted file
        client = get_backend()
        result = client.predict(
            image=handle_file(image_path),
            api_name="/classify_material"
        )

        print(f"📥 Backend response: {result}")

        # result is tuple: (classification, confidence, reasoning)
        return result[0], result[1], result[2]

    except Exception as e:
        import traceback
        error_msg = str(e)
        full_error = traceback.format_exc()
        print(f"❌ Error: {full_error}")
        return f"❌ Classification Error", "", f"Error: {error_msg}"


# Custom CSS (same as original)
custom_css = """
#header {
    text-align: center;
    background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
    padding: 20px;
    border-radius: 10px;
    color: white;
}
#result {
    font-size: 72px;
    font-weight: 900;
    text-align: center;
    padding: 50px 30px;
    border-radius: 20px;
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    color: white;
    margin-bottom: 25px;
    box-shadow: 0 8px 16px rgba(0,0,0,0.2);
    text-transform: uppercase;
    letter-spacing: 2px;
    line-height: 1.2;
}
#confidence {
    font-size: 24px;
    font-weight: 600;
    text-align: center;
    padding: 15px;
    background: #f0f9ff;
    border-radius: 10px;
    margin-bottom: 20px;
}
#reasoning {
    font-size: 14px;
    text-align: left;
    padding: 15px;
    background: #f9fafb;
    border-radius: 8px;
    margin-bottom: 20px;
    color: #4b5563;
}
#about {
    font-size: 13px;
    color: #6b7280;
    padding-top: 20px;
    border-top: 1px solid #e5e7eb;
}
"""

# Build Gradio interface
with gr.Blocks(css=custom_css, title="Netra AI - Material Classifier") as demo:

    gr.Markdown(
        """
        <div id="header">
        <h1>🏗️ Netra AI - Construction Material Classifier</h1>
        <p>AI-powered material identification for construction sites</p>
        </div>
        """,
        elem_id="header"
    )

    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### 📸 Upload Material Image")
            image_input = gr.Image(
                type="pil",
                label="Construction Material Photo",
                height=400
            )

            classify_btn = gr.Button("🚀 Classify Material", variant="primary", size="lg")

            gr.Markdown(
                """
                **Supported Materials:**
                - 🏖️ Reet (Sand)
                - 🔷 12mm VSI (Uniform Aggregate)
                - 🪨 Stone (Large Boulders)
                - 🏗️ GSB (Mixed Graded Base)
                """
            )

            gr.Markdown("### 📷 Try Sample Images")
            gr.Examples(
                examples=[
                    ["sanity_test_dataset/12mm_VSI_01.jpeg"],
                    ["sanity_test_dataset/Reet_01_fine.jpeg"],
                    ["sanity_test_dataset/Stone_01.jpeg"],
                    ["sanity_test_dataset/GSB_01.jpeg"],
                ],
                inputs=image_input,
                label=""
            )

        with gr.Column(scale=1):
            result_output = gr.Markdown("", elem_id="result")
            confidence_output = gr.Markdown("", elem_id="confidence")
            reasoning_output = gr.Markdown("", elem_id="reasoning")

            gr.Markdown(
                """
                <div id="about">

                ### 📊 About This Demo

                This is a proof-of-concept for **Netra AI's** automated material classification system.

                **Technology:**
                - Vision AI for real-time material identification
                - Prevents grade fraud at construction sites
                - Automates dispatch logging

                **Use Cases:**
                - Gate monitoring at crusher plants
                - Dispatch verification
                - Quality control

                ---
                **Netra AI** - *Making every tonne count.*

                For commercial inquiries: [Contact Us](mailto:guptaraghu321@gmail.com)

                </div>
                """
            )


    # Connect button
    classify_btn.click(
        fn=classify_material,
        inputs=[image_input],
        outputs=[result_output, confidence_output, reasoning_output]
    )

# Launch
if __name__ == "__main__":
    demo.launch(show_error=True)