External Integration

def load(
    name,
    src=None,
    api_key=None,
    alias=None,
    **kwargs
):
    """
    Load external Hugging Face models and Spaces into Gradio interfaces.

    Parameters:
    - name: Model or Space name (e.g., "microsoft/DialoGPT-medium", "huggingface/CodeBERTa-small-v1")
    - src: Source type ("huggingface", "github", or None for auto-detection)
    - api_key: Hugging Face API key for private models
    - alias: Alternative name for the loaded interface
    - kwargs: Additional parameters for model loading

    Returns:
    - Gradio interface object for the loaded model/Space
    """

def load_chat(
    name,
    src=None,
    api_key=None,
    **kwargs
):
    """
    Load chat interfaces from external sources with conversational UI.

    Parameters:
    - name: Chat model or Space name
    - src: Source type ("huggingface" or None)
    - api_key: API key for private models
    - kwargs: Additional chat configuration parameters

    Returns:
    - ChatInterface object configured for the loaded model
    """

def load_openapi(
    url,
    api_key=None,
    headers=None,
    **kwargs
):
    """
    Load and integrate OpenAPI specifications as Gradio interfaces.

    Parameters:
    - url: URL to OpenAPI specification (JSON or YAML)
    - api_key: API key for authenticated endpoints
    - headers: Additional HTTP headers for API requests
    - kwargs: Additional configuration options

    Returns:
    - Gradio interface for interacting with the API
    """

import gradio as gr

# Load a Hugging Face model
text_generator = gr.load("microsoft/DialoGPT-medium")

# Load a Hugging Face Space
image_classifier = gr.load("huggingface/image-classification-demo")

# Load with custom configuration
private_model = gr.load(
    "organization/private-model",
    api_key="hf_your_token_here",
    src="huggingface"
)

# Load chat interface
chatbot = gr.load_chat("microsoft/DialoGPT-medium")

# Load OpenAPI service
weather_api = gr.load_openapi(
    "https://api.openweathermap.org/data/2.5/openapi.json",
    api_key="your_weather_api_key"
)

# Combine loaded interfaces
with gr.Blocks() as demo:
    gr.Markdown("# Multi-Model Demo")
    
    with gr.Tab("Text Generation"):
        text_generator.render()
    
    with gr.Tab("Image Classification"):
        image_classifier.render()
    
    with gr.Tab("Chat"):
        chatbot.render()

demo.launch()

def mount_gradio_app(
    app,
    gradio_app,
    path="/gradio",
    gradio_api_url="http://localhost:7860",
    **kwargs
):
    """
    Mount Gradio applications in existing web frameworks (FastAPI, Flask, etc.).

    Parameters:
    - app: Existing web application instance (FastAPI, Flask app, etc.)
    - gradio_app: Gradio interface or Blocks instance to mount
    - path: URL path where Gradio app will be mounted
    - gradio_api_url: URL for Gradio API endpoints
    - kwargs: Additional mounting configuration

    Returns:
    - Modified web application with Gradio integration
    """

class Request:
    def __init__(self, request):
        """
        HTTP request wrapper for accessing request data in Gradio functions.

        Parameters:
        - request: Underlying HTTP request object
        
        Attributes:
        - headers: Request headers dictionary
        - query_params: Query parameters dictionary
        - path_params: Path parameters dictionary
        - cookies: Request cookies
        - client: Client information (IP, host, etc.)
        - method: HTTP method (GET, POST, etc.)
        - url: Full request URL
        """

    @property
    def headers(self):
        """Access request headers."""

    @property
    def query_params(self):
        """Access query parameters."""

    @property
    def cookies(self):
        """Access request cookies."""

    @property
    def client(self):
        """Access client information."""

    def get_header(self, name, default=None):
        """Get specific header value."""

class Header:
    def __init__(self, name, value):
        """
        HTTP header utility for request/response handling.

        Parameters:
        - name: Header name
        - value: Header value
        """

import gradio as gr
from fastapi import FastAPI
import uvicorn

# Create FastAPI app
app = FastAPI()

# Create Gradio interface
def greet(name, request: gr.Request):
    client_ip = request.client.host
    user_agent = request.headers.get("user-agent", "Unknown")
    
    return f"Hello {name}! IP: {client_ip}, Browser: {user_agent}"

gradio_app = gr.Interface(
    fn=greet,
    inputs="text",
    outputs="text"
)

# Mount Gradio in FastAPI
app = gr.mount_gradio_app(app, gradio_app, path="/demo")

# Add regular FastAPI routes
@app.get("/")
def root():
    return {"message": "FastAPI with Gradio demo at /demo"}

@app.get("/api/status")
def status():
    return {"status": "running", "demo_url": "/demo"}

# Run combined application
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

import gradio as gr
from flask import Flask

# Create Flask app
flask_app = Flask(__name__)

# Create Gradio interface
def process_data(data, request: gr.Request):
    session_id = request.cookies.get("session_id", "anonymous")
    return f"Processed {data} for session {session_id}"

gradio_app = gr.Interface(
    fn=process_data,
    inputs="text",
    outputs="text"
)

# Mount Gradio in Flask
flask_app = gr.mount_gradio_app(flask_app, gradio_app, path="/ml")

# Add Flask routes
@flask_app.route("/")
def home():
    return '<h1>Flask + Gradio</h1><a href="/ml">ML Demo</a>'

@flask_app.route("/api/info")
def info():
    return {"app": "Flask + Gradio", "ml_endpoint": "/ml"}

flask_app.run(debug=True, port=5000)

class OAuthProfile:
    def __init__(self, profile_data):
        """
        OAuth user profile data representation.

        Parameters:
        - profile_data: Raw profile data from OAuth provider

        Attributes:
        - username: User's username or handle
        - name: User's display name
        - email: User's email address
        - avatar_url: URL to user's profile picture
        - profile_url: URL to user's profile page
        - provider: OAuth provider name (e.g., "github", "google")
        """

    @property
    def username(self):
        """Get username from profile."""

    @property
    def name(self):
        """Get display name from profile."""

    @property
    def email(self):
        """Get email from profile."""

    @property
    def avatar_url(self):
        """Get avatar image URL."""

class OAuthToken:
    def __init__(self, token_data):
        """
        OAuth token management and validation.

        Parameters:
        - token_data: Raw token data from OAuth flow

        Attributes:
        - access_token: OAuth access token
        - refresh_token: OAuth refresh token (if available)
        - expires_at: Token expiration timestamp
        - scope: Token permissions scope
        """

    @property
    def access_token(self):
        """Get access token."""

    @property
    def is_expired(self):
        """Check if token is expired."""

    def refresh(self):
        """Refresh the access token if refresh token available."""

import gradio as gr

def authenticated_function(input_data, request: gr.Request):
    # Check if user is authenticated
    auth_header = request.headers.get("authorization")
    
    if not auth_header:
        return "Please log in to use this feature"
    
    # Extract user info (simplified example)
    user_info = validate_token(auth_header)
    
    if user_info:
        return f"Hello {user_info['name']}, processing: {input_data}"
    else:
        return "Invalid authentication"

def oauth_callback(code, state):
    # Handle OAuth callback
    token = exchange_code_for_token(code)
    profile = get_user_profile(token.access_token)
    
    return f"Welcome {profile.name}!"

# Interface with authentication
auth_demo = gr.Interface(
    fn=authenticated_function,
    inputs="text",
    outputs="text",
    title="Authenticated Demo"
)

# OAuth login interface
oauth_demo = gr.Interface(
    fn=oauth_callback,
    inputs=["text", "text"],  # code, state
    outputs="text",
    title="OAuth Login"
)

# MCP module for Model Context Protocol integration
import gradio.mcp as mcp

# MCP integration functions and classes would be defined here
# This is a placeholder for the MCP functionality

import gradio as gr
import gradio.mcp as mcp

def mcp_enabled_function(input_data):
    # Use MCP for enhanced AI interactions
    context = mcp.get_context()
    response = mcp.process_with_context(input_data, context)
    return response

mcp_demo = gr.Interface(
    fn=mcp_enabled_function,
    inputs="text",
    outputs="text",
    title="MCP Enhanced Interface"
)

import gradio as gr
import requests

def proxy_to_service(input_data, request: gr.Request):
    # Forward request to microservice
    api_gateway_url = "https://api.example.com/ml-service"
    
    headers = {
        "Authorization": request.headers.get("authorization"),
        "Content-Type": "application/json"
    }
    
    response = requests.post(
        api_gateway_url,
        json={"input": input_data},
        headers=headers
    )
    
    return response.json()["result"]

# Create interface that proxies to external services
proxy_demo = gr.Interface(
    fn=proxy_to_service,
    inputs="text",
    outputs="text"
)

import gradio as gr
import sqlite3

def save_and_process(user_input, request: gr.Request):
    # Save input to database
    conn = sqlite3.connect("app_data.db")
    cursor = conn.cursor()
    
    cursor.execute(
        "INSERT INTO user_inputs (input, ip_address, timestamp) VALUES (?, ?, datetime('now'))",
        (user_input, request.client.host)
    )
    conn.commit()
    conn.close()
    
    # Process and return result
    result = process_data(user_input)
    return result

def get_history(request: gr.Request):
    # Retrieve user's history
    conn = sqlite3.connect("app_data.db")
    cursor = conn.cursor()
    
    cursor.execute(
        "SELECT input, timestamp FROM user_inputs WHERE ip_address = ? ORDER BY timestamp DESC LIMIT 10",
        (request.client.host,)
    )
    
    history = cursor.fetchall()
    conn.close()
    
    return str(history)

with gr.Blocks() as demo:
    input_text = gr.Textbox(label="Input")
    result_output = gr.Textbox(label="Result")
    history_output = gr.Textbox(label="Your History")
    
    process_btn = gr.Button("Process")
    history_btn = gr.Button("View History")
    
    process_btn.click(save_and_process, input_text, result_output)
    history_btn.click(get_history, outputs=history_output)

import gradio as gr
from fastapi import FastAPI, BackgroundTasks
import json

app = FastAPI()
webhook_data = {"latest": None}

def process_webhook_data():
    if webhook_data["latest"]:
        return f"Latest webhook: {json.dumps(webhook_data['latest'], indent=2)}"
    return "No webhook data received"

def clear_webhook_data():
    webhook_data["latest"] = None
    return "Webhook data cleared"

# Webhook endpoint
@app.post("/webhook")
async def receive_webhook(data: dict, background_tasks: BackgroundTasks):
    webhook_data["latest"] = data
    return {"status": "received"}

# Gradio interface for viewing webhook data
webhook_viewer = gr.Interface(
    fn=process_webhook_data,
    inputs=None,
    outputs="text",
    title="Webhook Data Viewer"
)

# Mount Gradio interface
app = gr.mount_gradio_app(app, webhook_viewer, path="/viewer")

import gradio as gr
import boto3
from io import BytesIO

def upload_to_s3(file, bucket_name="my-gradio-uploads"):
    # Upload file to AWS S3
    s3_client = boto3.client('s3')
    
    try:
        s3_client.upload_file(file.name, bucket_name, file.orig_name)
        return f"File uploaded successfully: s3://{bucket_name}/{file.orig_name}"
    except Exception as e:
        return f"Upload failed: {str(e)}"

def analyze_with_rekognition(image):
    # Use AWS Rekognition for image analysis
    rekognition = boto3.client('rekognition')
    
    with open(image.name, 'rb') as img_file:
        response = rekognition.detect_labels(
            Image={'Bytes': img_file.read()},
            MaxLabels=10
        )
    
    labels = [label['Name'] for label in response['Labels']]
    return f"Detected: {', '.join(labels)}"

# Cloud-integrated interface
cloud_demo = gr.Interface(
    fn=[upload_to_s3, analyze_with_rekognition],
    inputs=["file", "image"],
    outputs=["text", "text"],
    title="Cloud Integration Demo"
)