Initial commit: Apple Health Landing Zone

Create a Hugging Face Space that allows users to:
- Upload Apple Health export.xml files
- Create private datasets for health data storage
- Automatically generate MCP server spaces for data querying

Features OAuth login, private data handling, and MCP integration.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

.gitignore

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+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv

.python-version

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+3.12

README.md

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+---
+title: Apple Health Landing Zone
+emoji: 🏥
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 5.34.0
+app_file: app.py
+pinned: false
+hf_oauth: true
+hf_oauth_scopes:
+  - read-repos
+  - write-repos
+  - manage-repos
+---
+
+# Apple Health Landing Zone
+
+Upload your Apple Health export.xml file to create a private data ecosystem on Hugging Face.
+
+## What This Does
+
+This space helps you create:
+
+1. **Private Dataset**: Securely stores your Apple Health export.xml file
+2. **MCP Server Space**: A private Gradio space that acts as an MCP (Model Context Protocol) server for querying your health data
+
+## Features
+
+- 🔐 OAuth login with your Hugging Face account
+- 📊 Private dataset creation for your health data
+- 🖥️ Automatic MCP server setup
+- 🔍 Query interface for your health data
+- 🤖 Integration with Claude Desktop via MCP
+
+## How to Use
+
+1. Click "Sign in with Hugging Face"
+2. Upload your Apple Health export.xml file
+3. Choose a project name
+4. Click "Create Landing Zone"
+
+## Getting Your Apple Health Data
+
+1. Open the Health app on your iPhone
+2. Tap your profile picture in the top right
+3. Scroll down and tap "Export All Health Data"
+4. Choose to export and save the file
+5. Upload the export.xml file here
+
+## Privacy
+
+- All created repositories are **private** by default
+- Your health data is only accessible by you
+- The MCP server runs in your own private space
+
+## MCP Integration
+
+After creating your landing zone, you can add the MCP server to Claude Desktop by adding the configuration shown in your created space to your Claude Desktop settings.

app.py

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+import gradio as gr
+from huggingface_hub import HfApi, create_repo
+from huggingface_hub.utils import RepositoryNotFoundError
+import os
+
+
+def create_interface():
+    """Create the Gradio interface with OAuth login for Apple Health Landing Zone."""
+    
+    with gr.Blocks(title="Apple Health Landing Zone") as demo:
+        gr.Markdown("# Apple Health Landing Zone")
+        gr.Markdown("Login with your Hugging Face account to create a private dataset for your Apple Health export and an MCP server space.")
+        
+        # OAuth login
+        gr.LoginButton()
+        
+        # User info display
+        user_info = gr.Markdown("")
+        
+        # Upload section (initially hidden)
+        with gr.Column(visible=False) as upload_section:
+            gr.Markdown("### Upload Apple Health Export")
+            gr.Markdown("Upload your export.xml file from Apple Health. This will create:")
+            gr.Markdown("1. A private dataset to store your health data")
+            gr.Markdown("2. A private space with an MCP server to query your data")
+            
+            file_input = gr.File(
+                label="Apple Health export.xml",
+                file_types=[".xml"],
+                type="filepath"
+            )
+            
+            space_name_input = gr.Textbox(
+                label="Project Name",
+                placeholder="my-health-data",
+                info="Enter a name for your health data project (lowercase, no spaces)"
+            )
+            
+            create_btn = gr.Button("Create Landing Zone", variant="primary")
+            create_status = gr.Markdown("")
+        
+        def create_health_landing_zone(file_path: str, project_name: str, oauth_token: gr.OAuthToken | None) -> str:
+            """Create private dataset and MCP server space for Apple Health data."""
+            if not oauth_token:
+                return "❌ Please login first!"
+            
+            if not file_path:
+                return "❌ Please upload your export.xml file!"
+            
+            if not project_name:
+                return "❌ Please enter a project name!"
+            
+            try:
+                # Use the OAuth token
+                token = oauth_token.token
+                if not token:
+                    return "❌ No access token found. Please login again."
+                    
+                api = HfApi(token=token)
+                
+                # Get the current user's username
+                user_info = api.whoami()
+                username = user_info["name"]
+                
+                # Create dataset repository
+                dataset_repo_id = f"{username}/{project_name}-data"
+                space_repo_id = f"{username}/{project_name}-mcp"
+                
+                # Check if repositories already exist
+                try:
+                    api.repo_info(dataset_repo_id, repo_type="dataset")
+                    return f"❌ Dataset '{dataset_repo_id}' already exists!"
+                except RepositoryNotFoundError:
+                    pass
+                
+                try:
+                    api.repo_info(space_repo_id, repo_type="space")
+                    return f"❌ Space '{space_repo_id}' already exists!"
+                except RepositoryNotFoundError:
+                    pass
+                
+                # Create the private dataset
+                dataset_url = create_repo(
+                    repo_id=dataset_repo_id,
+                    repo_type="dataset",
+                    private=True,
+                    token=token
+                )
+                
+                # Upload the export.xml file
+                api.upload_file(
+                    path_or_fileobj=file_path,
+                    path_in_repo="export.xml",
+                    repo_id=dataset_repo_id,
+                    repo_type="dataset",
+                    token=token
+                )
+                
+                # Create README for dataset
+                dataset_readme = f"""# Apple Health Data
+
+This is a private dataset containing Apple Health export data for {username}.
+
+## Files
+- `export.xml`: The Apple Health export file
+
+## Associated MCP Server
+- Space: [{space_repo_id}](https://huggingface.co/spaces/{space_repo_id})
+
+## Privacy
+This dataset is private and contains personal health information. Do not share access with others.
+"""
+                
+                api.upload_file(
+                    path_or_fileobj=dataset_readme.encode(),
+                    path_in_repo="README.md",
+                    repo_id=dataset_repo_id,
+                    repo_type="dataset",
+                    token=token
+                )
+                
+                # Create the MCP server space
+                space_url = create_repo(
+                    repo_id=space_repo_id,
+                    repo_type="space",
+                    space_sdk="gradio",
+                    private=True,
+                    token=token
+                )
+                
+                # Create MCP server app.py
+                mcp_app_content = f'''import gradio as gr
+from huggingface_hub import hf_hub_download
+import xml.etree.ElementTree as ET
+import pandas as pd
+from datetime import datetime
+import json
+
+# Download the health data
+DATA_REPO = "{dataset_repo_id}"
+
+def load_health_data():
+    """Load and parse the Apple Health export.xml file."""
+    try:
+        # Download the export.xml file from the dataset
+        file_path = hf_hub_download(
+            repo_id=DATA_REPO,
+            filename="export.xml",
+            repo_type="dataset",
+            use_auth_token=True
+        )
+        
+        # Parse the XML file
+        tree = ET.parse(file_path)
+        root = tree.getroot()
+        
+        # Extract records
+        records = []
+        for record in root.findall('.//Record'):
+            records.append(record.attrib)
+        
+        return pd.DataFrame(records)
+    except Exception as e:
+        return None
+
+# Load data on startup
+health_df = load_health_data()
+
+def query_health_data(query_type, start_date=None, end_date=None):
+    """Query the health data based on user input."""
+    if health_df is None:
+        return "Error: Could not load health data."
+    
+    df = health_df.copy()
+    
+    # Filter by date if provided
+    if start_date:
+        df = df[df['startDate'] >= start_date]
+    if end_date:
+        df = df[df['endDate'] <= end_date]
+    
+    if query_type == "summary":
+        # Get summary statistics
+        summary = {{
+            "Total Records": len(df),
+            "Record Types": df['type'].value_counts().to_dict() if 'type' in df.columns else {{}},
+            "Date Range": f"{{df['startDate'].min()}} to {{df['endDate'].max()}}" if 'startDate' in df.columns else "N/A"
+        }}
+        return json.dumps(summary, indent=2)
+    
+    elif query_type == "recent":
+        # Get recent records
+        if 'startDate' in df.columns:
+            recent = df.nlargest(10, 'startDate')[['type', 'value', 'startDate', 'unit']].to_dict('records')
+            return json.dumps(recent, indent=2)
+        return "No date information available"
+    
+    else:
+        return "Invalid query type"
+
+# MCP Server Interface
+with gr.Blocks(title="Apple Health MCP Server") as demo:
+    gr.Markdown("# Apple Health MCP Server")
+    gr.Markdown(f"This is an MCP server for querying Apple Health data from dataset: `{{DATA_REPO}}`")
+    
+    with gr.Tab("Query Interface"):
+        query_type = gr.Dropdown(
+            choices=["summary", "recent"],
+            value="summary",
+            label="Query Type"
+        )
+        
+        with gr.Row():
+            start_date = gr.Textbox(label="Start Date (YYYY-MM-DD)", placeholder="2024-01-01")
+            end_date = gr.Textbox(label="End Date (YYYY-MM-DD)", placeholder="2024-12-31")
+        
+        query_btn = gr.Button("Run Query", variant="primary")
+        output = gr.Code(language="json", label="Query Results")
+        
+        query_btn.click(
+            fn=query_health_data,
+            inputs=[query_type, start_date, end_date],
+            outputs=output
+        )
+    
+    with gr.Tab("MCP Endpoint"):
+        gr.Markdown("""
+        ## MCP Server Endpoint
+        
+        This space can be used as an MCP server with the following configuration:
+        
+        ```json
+        {{
+            "mcpServers": {{
+                "apple-health": {{
+                    "command": "npx",
+                    "args": [
+                        "-y",
+                        "@modelcontextprotocol/server-huggingface",
+                        "{space_repo_id}",
+                        "--use-auth"
+                    ]
+                }}
+            }}
+        }}
+        ```
+        
+        Add this to your Claude Desktop configuration to query your Apple Health data through Claude.
+        """)
+
+if __name__ == "__main__":
+    demo.launch()
+'''
+                
+                api.upload_file(
+                    path_or_fileobj=mcp_app_content.encode(),
+                    path_in_repo="app.py",
+                    repo_id=space_repo_id,
+                    repo_type="space",
+                    token=token
+                )
+                
+                # Create requirements.txt for the space
+                requirements_content = """gradio>=5.34.0
+huggingface-hub>=0.20.0
+pandas>=2.0.0
+"""
+                
+                api.upload_file(
+                    path_or_fileobj=requirements_content.encode(),
+                    path_in_repo="requirements.txt",
+                    repo_id=space_repo_id,
+                    repo_type="space",
+                    token=token
+                )
+                
+                return f"""✅ Successfully created Apple Health Landing Zone!
+
+**Private Dataset:** [{dataset_repo_id}]({dataset_url})
+- Your export.xml file has been securely uploaded
+
+**MCP Server Space:** [{space_repo_id}]({space_url})
+- Query interface for your health data
+- MCP endpoint configuration included
+
+Both repositories are private and only accessible by you."""
+                
+            except Exception as e:
+                return f"❌ Error creating landing zone: {str(e)}"
+        
+        def update_ui(profile: gr.OAuthProfile | None) -> tuple:
+            """Update UI based on login status."""
+            if profile:
+                username = profile.username
+                return (
+                    f"✅ Logged in as **{username}**",
+                    gr.update(visible=True)
+                )
+            else:
+                return (
+                    "",
+                    gr.update(visible=False)
+                )
+        
+        # Update UI when login state changes
+        demo.load(update_ui, inputs=None, outputs=[user_info, upload_section])
+        
+        # Create landing zone button click
+        create_btn.click(
+            fn=create_health_landing_zone,
+            inputs=[file_input, space_name_input],
+            outputs=[create_status]
+        )
+    
+    return demo
+
+
+def main():
+    # Check if running in Hugging Face Spaces
+    if os.getenv("SPACE_ID"):
+        # Running in Spaces, launch with appropriate settings
+        demo = create_interface()
+        demo.launch()
+    else:
+        # Running locally, note that OAuth won't work
+        print("Note: OAuth login only works when deployed to Hugging Face Spaces.")
+        print("To test locally, deploy this as a Space with hf_oauth: true in README.md")
+        demo = create_interface()
+        demo.launch()
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

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+[project]
+name = "apple-health-landing-zone"
+version = "0.1.0"
+description = "Apple Health export.xml landing zone with MCP server creation"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "gradio>=5.34.0",
+    "huggingface-hub>=0.33.0",
+]
+
+[dependency-groups]
+dev = [
+    "huggingface-hub[cli]>=0.33.0",
+    "mypy>=1.16.0",
+    "pytest>=8.4.0",
+    "ruff>=0.11.13",
+]