📡 VISION STATEMENT
Bridging the Ancient and the Digital.
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.
"""
guide_instructions_html = """
🤖 CLAUDE DESKTOP SETUP GUIDE
STEP 1: PREPARE WORKSPACE
Claude is sandboxed. It cannot see your Desktop. You must create a bridge.
1. Create this EXACT folder on your PC: C:\\Claude_Work
2. Move your hieroglyph images INSIDE this folder.
STEP 2: VERIFY PYTHON
The code below assumes Python is at: C:\\Python313\\python.exe
Check your path: Open CMD and type where python.
Note: If your path is different, replace the path in the JSON code block below before copying.
STEP 3: CONFIGURE CLAUDE
1. Open Config: %APPDATA%\\Claude\\claude_desktop_config.json
2. Paste the JSON below into the "mcpServers" section.
3. IMPORTANT: Close Claude from the System Tray (near the clock) and restart it.
STEP 4: USAGE
Prompt Claude: "Analyze the image at C:\\Claude_Work\\my_tablet.jpg"
"""
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"")
# Top Section
gr.HTML(header_html)
gr.HTML(mission_html)
# Workspace
with gr.Row():
# INPUT COLUMN
with gr.Column(scale=1):
gr.HTML('
INPUT IMAGE
')
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('
')
# OUTPUT COLUMN
with gr.Column(scale=1):
gr.HTML('
AI ANALYSIS RESULTS
')
out_image = gr.Image(label="Decoded Result", interactive=False)
# Download Button
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('
')
# 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("
")
# Event Wiring
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"])