Deploying a Gradio App

What is Gradio?

Gradio is an open-source Python library for building and sharing web-based machine learning and data processing applications with minimal code. With a simple interface-building syntax, Gradio allows data scientists and machine learning engineers to quickly create interactive demos, prototypes, and production-ready applications without requiring extensive web development experience.

Key features include:

• Simple, intuitive API for building web interfaces
• Support for diverse input types (text, images, audio, video, files, sliders, dropdowns)
• Support for diverse output types (text, images, audio, video, dataframes, JSON)
• Automatic API endpoint generation for programmatic access
• Built-in support for machine learning model inference
• File upload and download capabilities
• Real-time streaming for long-running tasks
• Theme customization and responsive design
• Queue system for managing concurrent requests
• Authentication support for access control
• Analytics and usage tracking
• Share and embedding capabilities
• Integration with popular ML frameworks (TensorFlow, PyTorch, scikit-learn, transformers)
• Session management and state handling
• Caching for improved performance
• CORS support for cross-origin requests
• Docker containerization support

Gradio is ideal for sharing machine learning models, creating data processing pipelines, building interactive data visualizations, prototyping AI applications, deploying computer vision models, natural language processing demos, audio processing tools, and scientific computing interfaces.

Prerequisites

Before deploying a Gradio application to Klutch.sh, ensure you have:

• Python 3.9+ installed on your local machine
• pip or conda for dependency management
• Git and a GitHub account
• A Klutch.sh account with dashboard access
• Basic understanding of Python programming
• Optional: Machine learning models or data processing functions
• Optional: Understanding of model serving and inference

---

Getting Started with Gradio

Step 1: Create Your Project Directory and Virtual Environment

mkdir my-gradio-app
cd my-gradio-app
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Step 2: Install Gradio and Dependencies

pip install gradio torch pillow transformers requests

Key packages:

• gradio: The UI framework for machine learning apps
• torch: PyTorch machine learning framework
• pillow: Image processing library
• transformers: Pre-trained models from Hugging Face
• requests: HTTP library for API calls

Step 3: Create Your Gradio Application

Create app.py:

import gradio as gr
from PIL import Image
import numpy as np
import os
from pathlib import Path

# Load environment variables
ALLOWED_ORIGINS = os.getenv('ALLOWED_ORIGINS', 'localhost').split(',')
QUEUE_ENABLED = os.getenv('QUEUE_ENABLED', 'false').lower() == 'true'

# Example function: Image classification
def classify_image(image):
    """Classify an image using a pre-trained model."""
    if image is None:
        return "Please upload an image"

    try:
        # Load a pre-trained model from transformers
        from transformers import pipeline
        classifier = pipeline("image-classification", model="google/vit-base-patch16-224")
        results = classifier(image)

        # Format results as readable text
        output = "Classification Results:\n"
        for result in results[:5]:
            output += f"- {result['label']}: {result['score']:.2%}\n"
        return output
    except Exception as e:
        return f"Error: {str(e)}"

# Example function: Text processing
def process_text(text, operation="uppercase"):
    """Process text with various operations."""
    if not text:
        return "Please enter text"

    if operation == "uppercase":
        return text.upper()
    elif operation == "lowercase":
        return text.lower()
    elif operation == "reverse":
        return text[::-1]
    elif operation == "word_count":
        return f"Word count: {len(text.split())}"
    else:
        return text

# Example function: Numerical computation
def calculate_statistics(numbers_list):
    """Calculate statistics from a list of numbers."""
    try:
        numbers = [float(x) for x in numbers_list.strip().split(',')]

        return {
            "count": len(numbers),
            "sum": sum(numbers),
            "mean": sum(numbers) / len(numbers),
            "min": min(numbers),
            "max": max(numbers)
        }
    except ValueError:
        return {"error": "Please enter valid numbers separated by commas"}

# Example function: File processing
def process_file(file_obj):
    """Process uploaded file."""
    if file_obj is None:
        return "No file uploaded", 0

    try:
        file_path = file_obj.name if hasattr(file_obj, 'name') else str(file_obj)
        file_size = os.path.getsize(file_path)

        # Read file content for text files
        if file_path.endswith(('.txt', '.csv')):
            with open(file_path, 'r') as f:
                content = f.read()[:500]  # First 500 chars
            return f"File: {Path(file_path).name}\nSize: {file_size} bytes\nContent preview:\n{content}", file_size
        else:
            return f"File: {Path(file_path).name}\nSize: {file_size} bytes", file_size
    except Exception as e:
        return f"Error processing file: {str(e)}", 0

# Create Gradio interface with tabs
with gr.Blocks(title="Gradio App on Klutch.sh", theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # Machine Learning & Data Processing App

    Welcome to your Gradio application deployed on Klutch.sh!
    This app demonstrates various input/output capabilities.
    """)

    with gr.Tabs():
        # Image Classification Tab
        with gr.Tab("Image Classification"):
            gr.Markdown("Upload an image to classify it using a pre-trained vision model.")
            with gr.Row():
                image_input = gr.Image(type="pil", label="Upload Image")
                image_output = gr.Textbox(label="Classification Results", lines=5)

            classify_button = gr.Button("Classify Image", variant="primary")
            classify_button.click(
                fn=classify_image,
                inputs=image_input,
                outputs=image_output,
                queue=QUEUE_ENABLED
            )

        # Text Processing Tab
        with gr.Tab("Text Processing"):
            gr.Markdown("Process text with various operations.")
            with gr.Row():
                with gr.Column():
                    text_input = gr.Textbox(
                        label="Enter Text",
                        placeholder="Type something here...",
                        lines=4
                    )
                    operation = gr.Dropdown(
                        choices=["uppercase", "lowercase", "reverse", "word_count"],
                        value="uppercase",
                        label="Operation"
                    )
                    process_button = gr.Button("Process Text", variant="primary")

                text_output = gr.Textbox(label="Result", lines=4)

            process_button.click(
                fn=process_text,
                inputs=[text_input, operation],
                outputs=text_output,
                queue=QUEUE_ENABLED
            )

        # Statistics Tab
        with gr.Tab("Statistics"):
            gr.Markdown("Calculate statistics from a list of numbers.")
            with gr.Row():
                numbers_input = gr.Textbox(
                    label="Numbers (comma-separated)",
                    placeholder="1, 2, 3, 4, 5",
                    lines=2
                )
                stats_button = gr.Button("Calculate", variant="primary")

            stats_output = gr.JSON(label="Statistics")

            stats_button.click(
                fn=calculate_statistics,
                inputs=numbers_input,
                outputs=stats_output,
                queue=QUEUE_ENABLED
            )

        # File Processing Tab
        with gr.Tab("File Processing"):
            gr.Markdown("Upload a file to see its details.")
            with gr.Row():
                with gr.Column():
                    file_input = gr.File(label="Upload File")
                    file_button = gr.Button("Process File", variant="primary")

                with gr.Column():
                    file_info = gr.Textbox(label="File Information", lines=5)
                    file_size = gr.Number(label="File Size (bytes)")

            file_button.click(
                fn=process_file,
                inputs=file_input,
                outputs=[file_info, file_size],
                queue=QUEUE_ENABLED
            )

    # Footer
    gr.Markdown("""
    ---
    *Deployed on Klutch.sh with Gradio*
    """)

# Configure for production
demo.queue() if QUEUE_ENABLED else None

# Launch the app
if __name__ == "__main__":
    port = int(os.getenv('PORT', 7860))
    demo.launch(
        server_name="0.0.0.0",
        server_port=port,
        share=False,
        show_error=True,
        allowed_paths=["/app/uploads"],
        blocked_paths=["__pycache__", ".git"]
    )

Step 4: Create a Requirements File

pip freeze > requirements.txt

Your requirements.txt should contain:

gradio==4.26.0
torch==2.1.2
pillow==10.1.0
transformers==4.35.2
requests==2.31.0
numpy==1.24.3

Step 5: Test Locally

Create a .env file for local development:

PORT=7860
ALLOWED_ORIGINS=localhost,127.0.0.1
QUEUE_ENABLED=false

Run the application:

python app.py

Access the interface at http://localhost:7860 in your browser. You should see the tabbed interface with image classification, text processing, statistics, and file upload capabilities.

---

Deploying Without a Dockerfile

Klutch.sh uses Nixpacks to automatically detect and build your Gradio application from your source code.

Prepare Your Repository

1. Initialize a Git repository and commit your code:

git init
git add .
git commit -m "Initial Gradio app commit"

2. Create a .gitignore file:

venv/
__pycache__/
*.pyc
*.pyo
*.egg-info/
.env
.DS_Store
.gradio/
flagged/
.venv/
*.model
*.pkl
*.h5
*.pth
uploads/
logs/

3. Push to GitHub:

git remote add origin https://github.com/YOUR_USERNAME/my-gradio-app.git
git branch -M main
git push -u origin main

Deploy to Klutch.sh

1. Log in to Klutch.sh dashboard.

2. Click “Create a new project” and provide a project name.

3. Inside your project, click “Create a new app”.

4. Repository Configuration:

   • Select your GitHub repository containing the Gradio app
   • Select the branch to deploy (typically main)

5. Traffic Settings:

   • Select “HTTP” as the traffic type

6. Port Configuration:

   • Set the internal port to 7860 (the default Gradio port)

7. Environment Variables: Set the following environment variables in the Klutch.sh dashboard:

   • PORT: Set to 7860 (Gradio default)
   • ALLOWED_ORIGINS: CORS allowed origins (e.g., https://example-app.klutch.sh,https://myapp.example.com)
   • QUEUE_ENABLED: Set to true to enable request queuing for long-running tasks
   • PYTHONUNBUFFERED: Set to 1 to ensure Python output is logged immediately

8. Build and Start Commands (Optional): If you need to customize the build or start command, set these environment variables:

   • BUILD_COMMAND: Default runs pip install -r requirements.txt
   • START_COMMAND: Default is python app.py

   For example, to download models before starting:

   START_COMMAND=python app.py

9. Region, Compute, and Instances:

   • Choose your desired region for optimal latency
   • Select compute resources (Pro/Premium for ML models, as Starter may be insufficient)
   • Set the number of instances (start with 1-2, scale as needed based on traffic)

10. Click “Create” to deploy. Klutch.sh will automatically build your application using Nixpacks and deploy it.

11. Once deployment completes, your app will be accessible at example-app.klutch.sh.

Verifying the Deployment

Navigate to your deployed app:

https://example-app.klutch.sh

You should see the Gradio interface with all the tabs (Image Classification, Text Processing, Statistics, File Processing) and be able to interact with each function.

---

Deploying With a Dockerfile

If you prefer more control over your build environment, you can provide a custom Dockerfile. Klutch.sh automatically detects and uses a Dockerfile in your repository’s root directory.

Create a Multi-Stage Dockerfile

Create a Dockerfile in your project root:

# Build stage
FROM python:3.11-slim as builder

WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
    build-essential \
    git \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements and install Python dependencies
COPY requirements.txt .
RUN pip install --user --no-cache-dir -r requirements.txt

# Runtime stage
FROM python:3.11-slim

WORKDIR /app

# Install runtime dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
    libsm6 \
    libxext6 \
    libxrender-dev \
    curl \
    && rm -rf /var/lib/apt/lists/*

# Copy Python dependencies from builder
COPY --from=builder /root/.local /root/.local

# Set PATH to use pip from builder
ENV PATH=/root/.local/bin:$PATH
ENV PYTHONUNBUFFERED=1
ENV GRADIO_SERVER_NAME=0.0.0.0
ENV GRADIO_SERVER_PORT=7860

# Copy application code
COPY . .

# Create non-root user for security
RUN useradd -m -u 1000 gradio_user && \
    chown -R gradio_user:gradio_user /app

USER gradio_user

# Create necessary directories
RUN mkdir -p /app/uploads /app/logs

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=15s --retries=3 \
    CMD curl -f http://localhost:7860/api/ || exit 1

# Expose port
EXPOSE 7860

# Start the application
CMD ["python", "app.py"]

Deploy the Dockerfile Version

1. Push your code with the Dockerfile to GitHub:

git add Dockerfile
git commit -m "Add Dockerfile for custom build"
git push

2. Log in to Klutch.sh dashboard.

3. Create a new app:

   • Select your GitHub repository and branch
   • Set traffic type to “HTTP”
   • Set the internal port to 7860
   • Add environment variables (same as Nixpacks deployment)
   • Click “Create”

4. Klutch.sh will automatically detect your Dockerfile and use it for building and deployment.

---

Building Custom Interfaces

Image Processing Interface

Create advanced image processing capabilities:

import gradio as gr
from PIL import Image, ImageFilter, ImageOps
import numpy as np

def apply_blur(image, radius):
    """Apply blur filter to image."""
    if image is None:
        return None
    return image.filter(ImageFilter.GaussianBlur(radius=radius))

def apply_grayscale(image):
    """Convert image to grayscale."""
    if image is None:
        return None
    return ImageOps.grayscale(image)

def resize_image(image, width, height):
    """Resize image to specified dimensions."""
    if image is None:
        return None
    return image.resize((width, height), Image.Resampling.LANCZOS)

# Create interface
with gr.Blocks(title="Image Editor") as image_editor:
    gr.Markdown("# Image Editor - Gradio on Klutch.sh")

    with gr.Row():
        with gr.Column():
            image_input = gr.Image(type="pil", label="Upload Image")
            operation = gr.Dropdown(
                choices=["blur", "grayscale", "resize"],
                value="blur",
                label="Operation"
            )

            # Parameters based on operation
            with gr.Group():
                blur_radius = gr.Slider(1, 20, 5, label="Blur Radius")
                resize_width = gr.Number(value=256, label="Width", visible=False)
                resize_height = gr.Number(value=256, label="Height", visible=False)

            process_button = gr.Button("Process", variant="primary")

        image_output = gr.Image(label="Result")

    def process_image(img, op, blur_r, w, h):
        if op == "blur":
            return apply_blur(img, blur_r)
        elif op == "grayscale":
            return apply_grayscale(img)
        elif op == "resize":
            return resize_image(img, int(w), int(h))
        return img

    process_button.click(
        fn=process_image,
        inputs=[image_input, operation, blur_radius, resize_width, resize_height],
        outputs=image_output
    )

image_editor.launch(server_name="0.0.0.0", server_port=7860)

Machine Learning Model Interface

Create an interface for serving ML models:

import gradio as gr
from transformers import pipeline
import torch

# Load models
sentiment_analyzer = pipeline("sentiment-analysis")
summarizer = pipeline("summarization")

def analyze_sentiment(text):
    """Analyze sentiment of input text."""
    if not text:
        return {"error": "Please enter text"}

    results = sentiment_analyzer(text[:512])  # Limit to 512 chars
    return {
        "sentiment": results[0]["label"],
        "confidence": f"{results[0]['score']:.2%}"
    }

def summarize_text(text, max_length=130, min_length=30):
    """Summarize long text."""
    if len(text.split()) < 50:
        return "Text is too short to summarize"

    try:
        summary = summarizer(text, max_length=max_length, min_length=min_length)
        return summary[0]["summary_text"]
    except Exception as e:
        return f"Error: {str(e)}"

# Create interface
with gr.Blocks(title="NLP Tools") as nlp_demo:
    gr.Markdown("# NLP Tools - Sentiment & Summarization")

    with gr.Tabs():
        with gr.Tab("Sentiment Analysis"):
            text = gr.Textbox(
                label="Enter Text",
                placeholder="Type text here...",
                lines=4
            )
            sentiment_btn = gr.Button("Analyze", variant="primary")
            output = gr.JSON(label="Results")

            sentiment_btn.click(
                fn=analyze_sentiment,
                inputs=text,
                outputs=output,
                queue=True
            )

        with gr.Tab("Text Summarization"):
            text = gr.Textbox(
                label="Enter Text to Summarize",
                lines=6
            )
            max_len = gr.Slider(50, 300, 130, label="Max Summary Length")
            min_len = gr.Slider(10, 100, 30, label="Min Summary Length")
            summary_btn = gr.Button("Summarize", variant="primary")
            output = gr.Textbox(label="Summary", lines=4)

            summary_btn.click(
                fn=summarize_text,
                inputs=[text, max_len, min_len],
                outputs=output,
                queue=True
            )

nlp_demo.launch(server_name="0.0.0.0", server_port=7860)

---

Handling File Uploads and Downloads

File Processing with Downloads

import gradio as gr
import pandas as pd
import csv
from pathlib import Path

def process_csv(file_obj):
    """Process CSV file and return statistics."""
    try:
        df = pd.read_csv(file_obj.name)

        # Generate statistics
        stats = {
            "rows": len(df),
            "columns": len(df.columns),
            "column_names": list(df.columns),
            "dtypes": df.dtypes.astype(str).to_dict(),
            "missing_values": df.isnull().sum().to_dict()
        }

        return stats, df
    except Exception as e:
        return {"error": str(e)}, None

def generate_report(file_obj):
    """Generate a text report from CSV."""
    try:
        df = pd.read_csv(file_obj.name)
        report = f"""
CSV Report
==========
Rows: {len(df)}
Columns: {len(df.columns)}

Column Details:
{df.describe().to_string()}
"""
        # Save report
        report_path = "/app/uploads/report.txt"
        Path("/app/uploads").mkdir(parents=True, exist_ok=True)
        with open(report_path, 'w') as f:
            f.write(report)

        return report, report_path
    except Exception as e:
        return f"Error: {str(e)}", None

# Create interface
with gr.Blocks(title="CSV Processor") as csv_demo:
    gr.Markdown("# CSV File Processor")

    with gr.Tabs():
        with gr.Tab("Analysis"):
            with gr.Row():
                csv_input = gr.File(label="Upload CSV", file_types=[".csv"])
                analyze_btn = gr.Button("Analyze", variant="primary")

            stats_output = gr.JSON(label="Statistics")
            table_output = gr.Dataframe(label="Data Preview")

            analyze_btn.click(
                fn=process_csv,
                inputs=csv_input,
                outputs=[stats_output, table_output]
            )

        with gr.Tab("Report Generation"):
            with gr.Row():
                csv_input2 = gr.File(label="Upload CSV", file_types=[".csv"])
                report_btn = gr.Button("Generate Report", variant="primary")

            report_text = gr.Textbox(label="Report", lines=10)
            report_file = gr.File(label="Download Report")

            report_btn.click(
                fn=generate_report,
                inputs=csv_input2,
                outputs=[report_text, report_file]
            )

csv_demo.launch(server_name="0.0.0.0", server_port=7860)

---

Authentication and Access Control

Adding User Authentication

import gradio as gr
import os

def authenticate(username, password):
    """Simple authentication function."""
    # In production, use secure password hashing and database
    valid_users = {
        "admin": "secure_password_here",
        "user": "another_password"
    }

    if username in valid_users and valid_users[username] == password:
        return True, f"Welcome, {username}!"
    return False, "Invalid credentials"

def protected_function(input_text):
    """Function accessible only after authentication."""
    return f"Processing: {input_text}"

# Create interface
with gr.Blocks(title="Protected App") as protected_demo:
    gr.Markdown("# Protected Gradio Application")

    with gr.Group(visible=False) as app_group:
        gr.Markdown("## Main Application")
        text_input = gr.Textbox(label="Enter Text")
        process_btn = gr.Button("Process")
        output = gr.Textbox(label="Output")

        process_btn.click(
            fn=protected_function,
            inputs=text_input,
            outputs=output
        )

    with gr.Group() as login_group:
        gr.Markdown("## Login Required")
        username = gr.Textbox(label="Username")
        password = gr.Textbox(label="Password", type="password")
        login_btn = gr.Button("Login", variant="primary")
        message = gr.Textbox(label="Message")

    def login(user, pwd):
        success, msg = authenticate(user, pwd)
        return (
            gr.update(visible=success),
            gr.update(visible=not success),
            msg
        )

    login_btn.click(
        fn=login,
        inputs=[username, password],
        outputs=[app_group, login_group, message]
    )

protected_demo.launch(server_name="0.0.0.0", server_port=7860)

---

Caching and Performance Optimization

Using Gradio’s Caching System

import gradio as gr
from functools import lru_cache
import time

@gr.cache_examples
@lru_cache(maxsize=128)
def expensive_computation(n):
    """Expensive computation with caching."""
    print(f"Computing for n={n}")
    time.sleep(2)  # Simulate expensive operation
    return sum(i**2 for i in range(n))

@gr.cache_examples
def process_with_cache(text):
    """Process text with example caching."""
    return text.upper(), len(text)

# Create interface
with gr.Blocks(title="Cached App") as cached_demo:
    gr.Markdown("# Performance-Optimized Gradio App")

    with gr.Tabs():
        with gr.Tab("Expensive Computation"):
            n = gr.Slider(1, 1000000, 1000, label="Number")
            compute_btn = gr.Button("Compute", variant="primary")
            result = gr.Number(label="Result")

            compute_btn.click(
                fn=expensive_computation,
                inputs=n,
                outputs=result,
                queue=True
            )

        with gr.Tab("Cached Examples"):
            text = gr.Textbox(label="Text")
            examples = gr.Examples(
                examples=[
                    ["hello world"],
                    ["gradio on klutch"],
                    ["machine learning"]
                ],
                inputs=text
            )
            process_btn = gr.Button("Process")

            with gr.Row():
                upper_output = gr.Textbox(label="Uppercase")
                length_output = gr.Number(label="Length")

            process_btn.click(
                fn=process_with_cache,
                inputs=text,
                outputs=[upper_output, length_output],
                queue=True
            )

cached_demo.launch(server_name="0.0.0.0", server_port=7860)

---

Environment Variables and Configuration

Essential Environment Variables

Configure these variables in the Klutch.sh dashboard:

Variable	Description	Example
PORT	Application port	7860
ALLOWED_ORIGINS	CORS allowed origins	https://example-app.klutch.sh
QUEUE_ENABLED	Enable request queue	true
PYTHONUNBUFFERED	Unbuffered Python output	1
MODEL_CACHE_DIR	Directory for model caching	/app/models
LOG_LEVEL	Logging level	INFO

Customization Environment Variables (Nixpacks)

For Nixpacks deployments:

Variable	Purpose	Example
BUILD_COMMAND	Build command	pip install -r requirements.txt
START_COMMAND	Start command	python app.py

---

Persistent Storage for Models and Data

Adding Persistent Volume

1. In the Klutch.sh app dashboard, navigate to “Persistent Storage” or “Volumes”
2. Click “Add Volume”
3. Set the mount path: /app/models (for ML models) or /app/uploads (for user uploads)
4. Set the size based on your needs (e.g., 50 GB for large models, 20 GB for uploads)
5. Save and redeploy

Organizing Model Storage

Update your app.py to use persistent model directory:

import gradio as gr
import os
from pathlib import Path

# Set up model directory
MODEL_DIR = os.getenv('MODEL_DIR', '/app/models')
Path(MODEL_DIR).mkdir(parents=True, exist_ok=True)

# Configure transformers to use custom cache
os.environ['TRANSFORMERS_CACHE'] = MODEL_DIR

def load_model():
    """Load model from persistent storage."""
    from transformers import pipeline
    # Models will be cached in /app/models
    classifier = pipeline("image-classification")
    return classifier

---

Custom Domains

To serve your Gradio application from a custom domain:

1. In the Klutch.sh app dashboard, navigate to “Custom Domains”
2. Click “Add Custom Domain”
3. Enter your domain (e.g., ml-demo.example.com)
4. Follow the DNS configuration instructions provided
5. Update ALLOWED_ORIGINS to include your custom domain

Example DNS configuration:

ml-demo.example.com CNAME example-app.klutch.sh

Update environment variable:

ALLOWED_ORIGINS=https://ml-demo.example.com,https://example-app.klutch.sh

---

Monitoring and Logging

Application Logging

Configure logging in your Gradio app:

import logging
import os
from pathlib import Path

# Create logs directory
log_dir = os.getenv('LOG_DIR', '/app/logs')
Path(log_dir).mkdir(parents=True, exist_ok=True)

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler(os.path.join(log_dir, 'gradio.log')),
        logging.StreamHandler()
    ]
)

logger = logging.getLogger(__name__)

def logged_function(input_text):
    """Function with logging."""
    logger.info(f"Processing input: {input_text}")
    result = input_text.upper()
    logger.info(f"Generated result: {result}")
    return result

Health Checks and Metrics

Create an endpoint for monitoring:

import gradio as gr
from datetime import datetime
import psutil
import os

def get_system_status():
    """Get system status information."""
    return {
        "timestamp": datetime.now().isoformat(),
        "cpu_percent": psutil.cpu_percent(interval=1),
        "memory_percent": psutil.virtual_memory().percent,
        "disk_usage": psutil.disk_usage('/').percent,
        "process_count": len(psutil.pids())
    }

# Monitor performance
with gr.Blocks() as monitoring:
    status_btn = gr.Button("Get Status")
    status_output = gr.JSON(label="System Status")

    status_btn.click(
        fn=get_system_status,
        outputs=status_output
    )

---

Security Best Practices

1. Environment Variables: Store secrets in environment variables, never in code
2. Input Validation: Validate all user inputs to prevent injection attacks
3. CORS Configuration: Restrict origins to trusted domains
4. HTTPS Only: Always use HTTPS in production
5. Authentication: Implement proper user authentication for sensitive features
6. Rate Limiting: Limit requests to prevent abuse
7. Model Integrity: Verify models come from trusted sources
8. File Uploads: Validate file types and sizes
9. Resource Limits: Set timeouts for long-running operations
10. Dependency Updates: Keep packages updated for security patches

Example security configuration:

import gradio as gr
import os

# Security settings
ALLOWED_ORIGINS = os.getenv('ALLOWED_ORIGINS', 'localhost').split(',')
MAX_FILE_SIZE = 100 * 1024 * 1024  # 100 MB
ALLOWED_EXTENSIONS = {'.txt', '.csv', '.json', '.pdf', '.png', '.jpg', '.jpeg'}

def validate_file(file_obj):
    """Validate uploaded file."""
    if file_obj is None:
        return "No file uploaded"

    file_name = file_obj.name
    file_size = os.path.getsize(file_name)

    # Check file size
    if file_size > MAX_FILE_SIZE:
        return "File size exceeds limit"

    # Check file extension
    ext = os.path.splitext(file_name)[1].lower()
    if ext not in ALLOWED_EXTENSIONS:
        return "File type not allowed"

    return "File validation passed"

demo = gr.Interface(
    fn=validate_file,
    inputs=gr.File(label="Upload File"),
    outputs="text"
)

---

Troubleshooting

Issue 1: Models Not Loading

Problem: Application fails to load pre-trained models during startup.

Solution:

• Verify model directory has sufficient disk space
• Check internet connectivity for model downloads
• Use persistent storage for model caching
• Pre-download models before deployment
• Check TRANSFORMERS_CACHE environment variable is set correctly

Issue 2: Memory Errors with Large Models

Problem: Application crashes with out-of-memory errors.

Solution:

• Use smaller model variants
• Implement model quantization
• Load models on-demand instead of at startup
• Enable request queue to manage concurrent requests
• Scale to instances with more memory
• Use offloading techniques for large models

Issue 3: Queue Processing Issues

Problem: Requests timeout or fail in the queue.

Solution:

• Increase timeout settings
• Optimize model inference time
• Reduce queue size if memory-constrained
• Monitor queue metrics in dashboard
• Implement request cancellation mechanisms
• Test with various input sizes

Issue 4: Interface Display Issues

Problem: Web interface displays incorrectly or elements not rendering.

Solution:

• Clear browser cache
• Check browser compatibility
• Verify CSS/JavaScript resources load
• Test with different Gradio versions
• Use responsive design patterns
• Check console for JavaScript errors

Issue 5: File Upload Failures

Problem: File uploads fail or files not accessible.

Solution:

• Verify upload directory permissions
• Check disk space availability
• Validate file size limits
• Ensure mounted volumes are accessible
• Check file type restrictions
• Verify temporary file cleanup

---

Best Practices for Production Deployment

1. Enable Request Queue: Handle concurrent requests efficiently

   demo.queue().launch(...)

2. Use Persistent Storage: Store models and important data

   MODEL_DIR = os.getenv('MODEL_DIR', '/app/models')

3. Implement Logging: Track application behavior

   logger.info(f"Processing request: {timestamp}")

4. Validate Inputs: Check all user inputs

   if not input_text or len(input_text) > 1000:
       return "Invalid input"

5. Cache Results: Improve performance with caching

   @lru_cache(maxsize=256)
   def cached_function(text): pass

6. Monitor Resources: Track CPU, memory, and disk usage

   cpu_usage = psutil.cpu_percent()

7. Set Timeouts: Prevent hanging requests

   model.generate(..., max_time=30)

8. Use Environment Variables: Externalize configuration

   PORT = os.getenv('PORT', 7860)

9. Implement Error Handling: Graceful error recovery

   try:
       result = process(input)
   except Exception as e:
       logger.error(f"Error: {e}")
       return "Error processing request"

10. Regular Updates: Keep dependencies current

    pip install --upgrade -r requirements.txt

---

Resources

• Gradio Official Documentation
• Gradio Quick Start Guide
• Gradio Blocks API
• Hugging Face Model Hub
• PyTorch Framework
• TensorFlow Framework
• Scikit-Learn Library

---

Conclusion

Deploying Gradio applications to Klutch.sh provides a fast, scalable platform for sharing machine learning models and data processing tools. Gradio’s simple interface-building syntax combined with Klutch.sh’s infrastructure makes it easy to go from local prototype to production application.

Key takeaways:

• Use Nixpacks for quick deployments with automatic Python detection
• Use Docker for complete control over dependencies and model versions
• Enable request queue for handling concurrent inference requests
• Use persistent storage for large ML models and user data
• Configure CORS and authentication for production security
• Monitor application performance through Klutch.sh dashboard
• Optimize model inference time and memory usage
• Implement proper logging for debugging and monitoring
• Keep dependencies updated for security and performance
• Test thoroughly with various input types and sizes

For additional help, refer to the Gradio documentation or Klutch.sh support resources.