app.py

import gradio as gr
from ultralytics import YOLO
from PIL import Image
import os
from huggingface_hub import hf_hub_download
import numpy as np
import tempfile

# --- 1. SETUP & MODEL LOADING ---
MODEL_REPO = "youkii-xr/hieroglyphic-detection"
MODEL_FILENAME = "best.pt"
# Fix for the Ultralytics config warning in containers
os.environ["YOLO_CONFIG_DIR"] = "/tmp/Ultralytics"

try:
    print("System: Initializing Rosetta Decoder Core...")
    model_path = hf_hub_download(
        repo_id=MODEL_REPO,
        filename=MODEL_FILENAME,
        token=os.environ.get("HF_TOKEN")
    )
    model = YOLO(model_path)
    print("System: Model loaded successfully.")
except Exception as e:
    print(f"Error: {e}")
    model = None

# --- 2. LOGIC: DECODING & REPORTING ---
def generate_human_report(detections, counts):
    """Generates a natural language summary of the findings."""
    if not detections:
        return "Rosetta Decoder detects no identifiable Gardiner codes in this image."
    
    total = len(detections)
    sorted_counts = sorted(counts.items(), key=lambda item: item[1], reverse=True)
    
    report = f"🔓 DECODING SEQUENCE COMPLETE\n"
    report += f"===================================\n"
    report += f"Glyph Density: {total} Symbols Identified\n\n"
    report += "🔣 GARDINER CODE INVENTORY:\n"
    
    for code, count in sorted_counts:
        report += f"• Code [{code}]: {count} instance(s)\n"
        
    report += f"\n===================================\n"
    report += f"CONFIDENCE: High\n"
    report += f"STATUS: Digitized & Ready for Translation."
    return report

def detect_hieroglyphs(image: Image.Image, conf_threshold: float = 0.25):
    if image is None: 
        return None, None, "Input source required.", None
    if model is None: 
        return None, {"error": "Model failed"}, "Critical Error: Weights not loaded.", None

    try:
        results = model.predict(source=image, conf=conf_threshold, iou=0.45, imgsz=640, verbose=False, device='cpu', max_det=300)
        
        # Visual
        annotated_array = results[0].plot() 
        annotated_image = Image.fromarray(annotated_array[..., ::-1])
        
        # Save for Download Button
        # We create a temp file so the download button can find it
        temp_dir = tempfile.gettempdir()
        save_path = os.path.join(temp_dir, "rosetta_decoded_result.jpg")
        annotated_image.save(save_path)
        
        # Data
        detections = []
        gardiner_counts = {}
        
        for box in results[0].boxes:
            if box.cls.numel() > 0:
                cls_id = int(box.cls[0])
                if 0 <= cls_id < len(model.names):
                    code = model.names[cls_id]
                    conf = float(box.conf[0])
                    if code not in gardiner_counts: gardiner_counts[code] = 0
                    gardiner_counts[code] += 1
                    detections.append({"code": code, "confidence": round(conf, 2)})

        summary_json = {
            "status": "success",
            "total_found": len(detections),
            "counts": gardiner_counts
        }
        
        text_report = generate_human_report(detections, gardiner_counts)
        
        # Return: Image, JSON, Text, FilePath (for download)
        return annotated_image, summary_json, text_report, save_path

    except Exception as e:
        return None, {"error": str(e)}, f"System Failure: {str(e)}", None

# --- 3. UI STYLING (HTML/CSS INJECTION) ---

# SVG Cursor: A stylized "Lens"
cursor_url = "url('data:image/svg+xml;base64,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')"

custom_css = f"""
@import url('https://fonts.googleapis.com/css2?family=Cinzel:wght@400;700&display=swap');
@import url('https://fonts.googleapis.com/css2?family=Space+Mono:ital,wght@0,400;0,700;1,400&display=swap');

/* --- THEME VARIABLES --- */
:root {{
    /* DAY MODE */
    --bg-gradient: linear-gradient(135deg, #f0e6d2 0%, #e6dcc3 100%);
    --card-bg: rgba(255, 255, 255, 0.6);
    --text-primary: #3d342b;
    --text-accent: #8b4513;   /* Saddle Brown */
    --border-color: #8b4513;
    --btn-grad: linear-gradient(135deg, #cd853f 0%, #8b4513 100%);
    --btn-text: #fff;
    --glow-color: rgba(139, 69, 19, 0.3);
    /* Guide Box Colors (Golden/Info) */
    --info-bg: rgba(212, 175, 55, 0.1); 
    --info-border: #d4af37;
}}

.dark {{
    /* DARK MODE */
    --bg-gradient: radial-gradient(circle at 50% 0%, #1c1c1c 0%, #0a0a0a 100%);
    --card-bg: rgba(30, 30, 30, 0.7);
    --text-primary: #dcdcdc; 
    --text-accent: #d4af37;  /* Gold */
    --border-color: #d4af37;
    --btn-grad: linear-gradient(135deg, #b8860b 0%, #d4af37 100%);
    --btn-text: #000;
    --glow-color: rgba(212, 175, 55, 0.4);
    /* Guide Box Colors (Golden/Info) */
    --info-bg: rgba(212, 175, 55, 0.1);
    --info-border: #d4af37;
}}

/* --- GLOBAL SETTINGS --- */
body, .gradio-container {{
    background: var(--bg-gradient) !important;
    font-family: 'Cinzel', serif !important;
    color: var(--text-primary) !important;
    cursor: {cursor_url} 16 16, auto !important; 
}}

button, a, .cursor-pointer {{
    cursor: {cursor_url} 16 16, pointer !important;
}}

/* --- ANIMATED CARDS --- */
.card {{
    background: var(--card-bg) !important;
    border: 1px solid rgba(128, 128, 128, 0.2) !important;
    border-radius: 12px;
    padding: 24px;
    box-shadow: 0 4px 20px rgba(0, 0, 0, 0.2);
    backdrop-filter: blur(12px);
    margin-bottom: 24px;
    transition: all 0.3s cubic-bezier(0.25, 0.8, 0.25, 1);
}}

.card:hover {{
    transform: translateY(-4px);
    border-color: var(--border-color) !important;
    box-shadow: 0 10px 30px rgba(0,0,0,0.3), 0 0 15px var(--glow-color);
}}

/* --- BUTTONS --- */
button.primary-btn {{
    background: var(--btn-grad) !important;
    border: 1px solid var(--border-color) !important;
    color: var(--btn-text) !important;
    font-weight: 700 !important;
    font-family: 'Space Mono', monospace !important;
    text-transform: uppercase;
    letter-spacing: 2px;
    transition: all 0.3s ease;
}}
button.primary-btn:hover {{
    transform: scale(1.02);
    box-shadow: 0 0 20px var(--glow-color);
}}

/* --- TYPOGRAPHY --- */
.card-title {{
    font-family: 'Space Mono', monospace;
    font-size: 14px;
    font-weight: 700;
    color: var(--text-accent);
    text-transform: uppercase;
    letter-spacing: 3px;
    border-bottom: 1px solid rgba(128,128,128, 0.2);
    padding-bottom: 12px;
    margin-bottom: 18px;
    display: flex;
    align-items: center;
    gap: 8px;
}}

/* --- GOLDEN GUIDE BOXES --- */
.guide-step {{
    background-color: var(--info-bg);
    border-left: 4px solid var(--info-border);
    padding: 16px;
    margin: 12px 0;
    border-radius: 0 8px 8px 0;
    font-family: 'Space Mono', monospace;
    font-size: 13px;
    line-height: 1.6;
}}

.step-number {{
    color: var(--text-accent);
    font-weight: bold;
    text-transform: uppercase;
    display: block;
    margin-bottom: 6px;
    font-size: 11px;
    letter-spacing: 1px;
}}

.path-highlight {{
    background: rgba(128,128,128,0.2);
    padding: 2px 6px;
    border-radius: 4px;
    color: var(--text-accent);
    font-weight: bold;
}}

/* UI CLEANUP */
.gradio-image, .gradio-json {{ background: transparent !important; border: none !important; }}
.report-box textarea {{
    background-color: rgba(0,0,0,0.2) !important;
    border: 1px solid var(--border-color) !important;
    font-family: 'Space Mono', monospace !important;
    color: var(--text-accent) !important;
    font-size: 13px !important;
}}
"""

header_html = """
<div style="padding: 20px 0; border-bottom: 1px solid rgba(128,128,128,0.1); margin-bottom: 20px;">
    <div style="display: flex; align-items: center; gap: 15px;">
        <svg width="40" height="40" viewBox="0 0 24 24" fill="none" stroke="var(--text-accent)" stroke-width="2">
            <rect x="3" y="3" width="18" height="18" rx="2" />
            <path d="M7 7h10" />
            <path d="M7 12h10" />
            <path d="M7 17h10" />
            <circle cx="12" cy="12" r="3" stroke="var(--text-accent)" fill="none"/>
        </svg>
        <div>
            <h1 style="margin: 0; font-size: 28px; font-family: 'Cinzel', serif; letter-spacing: 1px; color: var(--text-primary);">ROSETTA DECODER</h1>
            <p style="margin: 0; font-family: 'Space Mono', monospace; font-size: 11px; color: var(--text-accent); letter-spacing: 3px;">AI HIEROGLYPHIC TRANSLATION SYSTEM</p>
        </div>
    </div>
</div>
"""

mission_html = """
<div class="card">
    <div class="card-title">📡 VISION STATEMENT</div>
    <p style="opacity: 0.9; font-size: 15px; line-height: 1.8;">
        <b>Bridging the Ancient and the Digital.</b><br>
        The Rosetta Decoder project utilizes advanced computer vision to identify and catalog Ancient Egyptian hieroglyphs. 
        By automating the detection of Gardiner codes, we are creating a digital bridge that will eventually allow for instant, 
        context-aware translation of Pharaonic wisdom, making the voices of the past accessible to everyone.
    </p>
</div>
"""

guide_instructions_html = """
<div class="card" style="border-color: var(--info-border) !important;">
    <div class="card-title" style="color: var(--info-border) !important;">🤖 CLAUDE DESKTOP SETUP GUIDE</div>
    
    <div class="guide-step">
        <span class="step-number">STEP 1: PREPARE WORKSPACE</span>
        Claude is sandboxed. It cannot see your Desktop. You must create a bridge.<br>
        1. Create this EXACT folder on your PC: <span class="path-highlight">C:\\Claude_Work</span><br>
        2. Move your hieroglyph images <b>INSIDE</b> this folder.
    </div>

    <div class="guide-step">
        <span class="step-number">STEP 2: VERIFY PYTHON</span>
        The code below assumes Python is at: <code>C:\\Python313\\python.exe</code><br>
        <b>Check your path:</b> Open CMD and type <code>where python</code>.<br>
        <i>Note: If your path is different, replace the path in the JSON code block below before copying.</i>
    </div>

    <div class="guide-step">
        <span class="step-number">STEP 3: CONFIGURE CLAUDE</span>
        1. Open Config: <code>%APPDATA%\\Claude\\claude_desktop_config.json</code><br>
        2. Paste the JSON below into the <code>"mcpServers"</code> section.<br>
        3. <b>IMPORTANT:</b> Close Claude from the System Tray (near the clock) and restart it.
    </div>

    <div class="guide-step">
        <span class="step-number">STEP 4: USAGE</span>
        Prompt Claude: <i>"Analyze the image at C:\\Claude_Work\\my_tablet.jpg"</i>
    </div>
"""

claude_json_content = """{
  "mcpServers": {
    "gradio": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://youkii-xr-hieroglyph-mcp-server.hf.space/gradio_api/mcp/",
        "--transport",
        "streamable-http"
      ]
    },
    "upload_helper": {
      "command": "C:\\\\Python313\\\\python.exe",
      "args": [
        "-m",
        "gradio",
        "upload-mcp",
        "https://youkii-xr-hieroglyph-mcp-server.hf.space/",
        "C:\\\\Claude_Work"
      ]
    }
  }
}"""

# --- 4. MAIN APP ASSEMBLY ---
with gr.Blocks(title="Rosetta Decoder") as demo:
    # Inject Styles
    gr.HTML(f"<style>{custom_css}</style>")
    
    # Top Section
    gr.HTML(header_html)
    gr.HTML(mission_html)
    
    # Workspace
    with gr.Row():
        # INPUT COLUMN
        with gr.Column(scale=1):
            gr.HTML('<div class="card"><div class="card-title">INPUT IMAGE</div>')
            with gr.Tabs():
                with gr.TabItem("📜 Upload File"):
                    img_upload = gr.Image(type="pil", sources=["upload", "clipboard"], label="Upload", height=320)
                    slider_upload = gr.Slider(0.1, 1.0, 0.25, label="Scan Sensitivity")
                    btn_upload = gr.Button("🔍 START DECODING", elem_classes="primary-btn")
                
                with gr.TabItem("🎥 Live Camera"):
                    img_cam = gr.Image(type="pil", sources=["webcam"], label="Camera", height=320)
                    slider_cam = gr.Slider(0.1, 1.0, 0.25, label="Scan Sensitivity")
                    btn_cam = gr.Button("🔍 LIVE DECODE", elem_classes="primary-btn")
            gr.HTML('</div>')

        # OUTPUT COLUMN
        with gr.Column(scale=1):
            gr.HTML('<div class="card"><div class="card-title">AI ANALYSIS RESULTS</div>')
            out_image = gr.Image(label="Decoded Result", interactive=False)
            
            # Download Button (Standard Gradio Component)
            btn_download = gr.DownloadButton("💾 DOWNLOAD RESULT", visible=True)
            
            out_report = gr.Textbox(label="Analysis Log", lines=6, elem_classes="report-box", placeholder="Waiting for data stream...")
            with gr.Accordion("Raw Glyph Data (JSON)", open=False):
                out_json = gr.JSON(label="JSON Data")
            gr.HTML('</div>')

    # Footer Section
    gr.HTML(guide_instructions_html)
    gr.Code(value=claude_json_content, language="json", label="claude_desktop_config.json", interactive=False, lines=15)
    gr.HTML("</div>")

    # Event Wiring
    # Note: detect_hieroglyphs now returns 4 values: Image, JSON, Text, FilePath
    
    btn_upload.click(
        fn=detect_hieroglyphs, 
        inputs=[img_upload, slider_upload], 
        outputs=[out_image, out_json, out_report, btn_download]
    )
    
    btn_cam.click(
        fn=detect_hieroglyphs, 
        inputs=[img_cam, slider_cam], 
        outputs=[out_image, out_json, out_report, btn_download]
    )

if __name__ == "__main__":
    demo.launch(mcp_server=True, ssr_mode=False, allowed_paths=["/tmp"])