templates.py

from dataclasses import dataclass
from typing import Dict, List, Optional
import difflib
import json
import logging
from pathlib import Path

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

@dataclass
class Template:
    """Template data structure"""
    code: str
    description: str
    components: List[str]
    metadata: Dict[str, str]
    example: Optional[str] = None

class TemplateManager:
    """Manages and searches through templates"""
    
    def __init__(self):
        self.templates = {
            "image_classifier": Template(
                code="""
                import gradio as gr
                import numpy as np
                from PIL import Image

                def classify_image(image):
                    if image is None:
                        return {"error": 1.0}
                    return {"class1": 0.8, "class2": 0.2}

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Image Classifier")
                    with gr.Row():
                        with gr.Column():
                            input_image = gr.Image(type="pil")
                            classify_btn = gr.Button("Classify")
                        with gr.Column():
                            output_labels = gr.Label()

                    classify_btn.click(
                        fn=classify_image,
                        inputs=input_image,
                        outputs=output_labels
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Basic image classification interface",
                components=["Image", "Button", "Label"],
                metadata={"category": "computer_vision"}
            ),
            "chatbot": Template(
                code="""
                import gradio as gr

                def respond(message, history):
                    return f"You said: {message}"

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# AI Chatbot")
                    chatbot = gr.Chatbot()
                    msg = gr.Textbox(label="Message")
                    clear = gr.Button("Clear")

                    msg.submit(respond, [msg, chatbot], [chatbot])
                    clear.click(lambda: None, None, chatbot, queue=False)

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Interactive chatbot interface",
                components=["Chatbot", "Textbox", "Button"],
                metadata={"category": "nlp"}
            ),
            "audio_processor": Template(
                code="""
                import gradio as gr
                import numpy as np

                def process_audio(audio, volume_factor=1.0):
                    if audio is None:
                        return None
                    sr, data = audio
                    return (sr, data * volume_factor)

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Audio Processor")
                    with gr.Row():
                        with gr.Column():
                            input_audio = gr.Audio(source="microphone", type="numpy")
                            volume = gr.Slider(minimum=0, maximum=2, value=1, label="Volume")
                            process_btn = gr.Button("Process")
                        with gr.Column():
                            output_audio = gr.Audio(type="numpy")

                    process_btn.click(
                        fn=process_audio,
                        inputs=[input_audio, volume],
                        outputs=output_audio
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Audio processing interface",
                components=["Audio", "Slider", "Button"],
                metadata={"category": "audio"}
            ),
            "file_processor": Template(
                code="""
                import gradio as gr

                def process_file(file):
                    if file is None:
                        return "No file uploaded"
                    return f"Processed file: {file.name}"

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# File Processor")
                    with gr.Row():
                        with gr.Column():
                            file_input = gr.File(label="Upload File")
                            process_btn = gr.Button("Process")
                        with gr.Column():
                            output = gr.Textbox(label="Results")
                            json_output = gr.JSON(label="Detailed Results")

                    process_btn.click(
                        fn=process_file,
                        inputs=file_input,
                        outputs=[output, json_output]
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="File processing interface",
                components=["File", "Button", "Textbox", "JSON"],
                metadata={"category": "utility"}
            ),
            "data_visualization": Template(
                code="" """
                import gradio as gr
                import pandas as pd
                import plotly.express as px

                def visualize_data(data, plot_type):
                    if data is None:
                        return None

                    df = pd.read_csv(data.name)
                    if plot_type == "scatter":
                        fig = px.scatter(df, x=df.columns[0], y=df.columns[1])
                    elif plot_type == "line":
                        fig = px.line(df, x=df.columns[0], y=df.columns[1])
                    else:
                        fig = px.bar(df, x=df.columns[0], y=df.columns[1])

                    return fig

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Data Visualizer")
                    with gr.Row():
                        with gr.Column():
                            file_input = gr.File(label="Upload CSV")
                            plot_type = gr.Radio(
                                choices=["scatter", "line", "bar"],
                                label="Plot Type",
                                value="scatter"
                            )
                            visualize_btn = gr.Button("Visualize")
                        with gr.Column():
                            plot_output = gr.Plot(label="Visualization")

                    visualize_btn.click(
                        fn=visualize_data,
                        inputs=[file_input, plot_type],
                        outputs=plot_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Data visualization interface",
                components=["File", "Radio", "Button", "Plot"],
                metadata={"category": "data_science"}
            ),
            "form_builder": Template(
                code="""
                import gradio as gr
                import json

                def submit_form(name, email, age, interests, subscribe):
                    return json.dumps({
                        "name": name,
                        "email": email,
                        "age": age,
                        "interests": interests,
                        "subscribe": subscribe
                    }, indent=2)

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Form Builder")
                    with gr.Row():
                        with gr.Column():
                            name = gr.Textbox(label="Name")
                            email = gr.Textbox(label="Email")
                            age = gr.Number(label="Age")
                            interests = gr.CheckboxGroup(
                                choices=["Sports", "Music", "Art", "Technology"],
                                label="Interests"
                            )
                            subscribe = gr.Checkbox(label="Subscribe to newsletter")
                            submit_btn = gr.Button("Submit")
                        with gr.Column():
                            output = gr.JSON(label="Form Data")

                    submit_btn.click(
                        fn=submit_form,
                        inputs=[name, email, age, interests, subscribe],
                        outputs=output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Form builder interface",
                components=["Textbox", "Number", "CheckboxGroup", "Checkbox", "Button", "JSON"],
                metadata={"category": "utility"}
            ),
            "text_summarizer": Template(
                code="""
                import gradio as gr
                from transformers import pipeline

                summarizer = pipeline("summarization")

                def summarize_text(text):
                    summary = summarizer(text, max_length=150, min_length=40, do_sample=False)
                    return summary[0]['summary_text']

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Text Summarizer")
                    with gr.Row():
                        with gr.Column():
                            input_text = gr.Textbox(label="Input Text", lines=10, placeholder="Enter text to summarize...")
                            summarize_btn = gr.Button("Summarize")
                        with gr.Column():
                            summary_output = gr.Textbox(label="Summary", lines=5)

                    summarize_btn.click(
                        fn=summarize_text,
                        inputs=input_text,
                        outputs=summary_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Text summarization interface using a transformer model",
                components=["Textbox", "Button"],
                metadata={"category": "nlp"}
            ),
            "image_captioner": Template(
                code="""
                import gradio as gr
                from transformers import BlipProcessor, BlipForConditionalGeneration
                from PIL import Image

                processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
                model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")

                def generate_caption(image):
                    inputs = processor(image, return_tensors="pt")
                    out = model.generate(**inputs)
                    caption = processor.decode(out[0], skip_special_tokens=True)
                    return caption

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Image Caption Generator")
                    with gr.Row():
                        with gr.Column():
                            input_image = gr.Image(type="pil", label="Upload Image")
                            caption_btn = gr.Button("Generate Caption")
                        with gr.Column():
                            caption_output = gr.Textbox(label="Generated Caption")

                    caption_btn.click(
                        fn=generate_caption,
                        inputs=input_image,
                        outputs=caption_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Image captioning interface using a transformer model",
                components=["Image", "Button", "Textbox"],
                metadata={"category": "computer_vision"}
            ),
            "style_transfer": Template(
                code="""
                import gradio as gr
                import tensorflow as tf
                import tensorflow_hub as hub

                hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')

                def apply_style(content_image, style_image):
                    content_image = tf.image.convert_image_dtype(content_image, tf.float32)[tf.newaxis, ...]
                    style_image = tf.image.convert_image_dtype(style_image, tf.float32)[tf.newaxis, ...]
                    stylized_image = hub_model(content_image, style_image)[0]
                    return tf.squeeze(stylized_image).numpy()

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Neural Style Transfer")
                    with gr.Row():
                        with gr.Column():
                            content_image = gr.Image(label="Content Image")
                            style_image = gr.Image(label="Style Image")
                            transfer_btn = gr.Button("Transfer Style")
                        with gr.Column():
                            output_image = gr.Image(label="Stylized Image")

                    transfer_btn.click(
                        fn=apply_style,
                        inputs=[content_image, style_image],
                        outputs=output_image
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Neural style transfer between two images",
                components=["Image", "Button"],
                metadata={"category": "computer_vision"}
            ),
            "sentiment_analysis": Template(
                code="""
                import gradio as gr
                from transformers import pipeline

                sentiment_pipeline = pipeline("sentiment-analysis")

                def analyze_sentiment(text):
                    result = sentiment_pipeline(text)[0]
                    return f"{result['label']} ({result['score']:.2f})"

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Sentiment Analysis")
                    with gr.Row():
                        with gr.Column():
                            input_text = gr.Textbox(label="Input Text", lines=5, placeholder="Enter text to analyze sentiment...")
                            analyze_btn = gr.Button("Analyze Sentiment")
                        with gr.Column():
                            sentiment_output = gr.Textbox(label="Sentiment Result")

                    analyze_btn.click(
                        fn=analyze_sentiment,
                        inputs=input_text,
                        outputs=sentiment_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Sentiment analysis using transformer model",
                components=["Textbox", "Button"],
                metadata={"category": "nlp"}
            ),
            "pdf_to_text": Template(
                code="""
                import gradio as gr
                import PyPDF2

                def extract_text_from_pdf(pdf):
                    reader = PyPDF2.PdfFileReader(pdf)
                    text = ''
                    for page_num in range(reader.numPages):
                        page = reader.getPage(page_num)
                        text += page.extract_text()
                    return text

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# PDF to Text Extractor")
                    with gr.Row():
                        with gr.Column():
                            pdf_file = gr.File(label="Upload PDF")
                            extract_btn = gr.Button("Extract Text")
                        with gr.Column():
                            output_text = gr.Textbox(label="Extracted Text", lines=10)

                    extract_btn.click(
                        fn=extract_text_from_pdf,
                        inputs=pdf_file,
                        outputs=output_text
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Extract text from PDF files",
                components=["File", "Button", "Textbox"],
                metadata={"category": "utility"}
            ),
            "website_monitor": Template(
                code="""
                import gradio as gr
                import requests
                from datetime import datetime

                def monitor_website(url):
                    try:
                        response = requests.get(url)
                        status_code = response.status_code
                        status = "Up" if status_code == 200 else "Down"
                        return {
                            "url": url,
                            "status": status,
                            "response_time": response.elapsed.total_seconds(),
                            "last_checked": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
                        }
                    except Exception as e:
                        return {"error": str(e)}

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Website Uptime Monitor")
                    with gr.Row():
                        with gr.Column():
                            url_input = gr.Textbox(label="Website URL", placeholder="https://example.com")
                            check_btn = gr.Button("Check Website")
                        with gr.Column():
                            result_output = gr.JSON(label="Monitoring Result")

                    check_btn.click(
                        fn=monitor_website,
                        inputs=url_input,
                        outputs=result_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Monitor the uptime and response time of a website",
                components=["Textbox", "Button", "JSON"],
                metadata={"category": "web_monitoring"}
            ),
            "rss_feed_fetcher": Template(
                code="""
                import gradio as gr
                import feedparser

                def fetch_rss_feed(url):
                    feed = feedparser.parse(url)
                    if feed.bozo:
                        return {"error": "Invalid RSS feed URL"}
                    
                    return [{"title": entry.title, "link": entry.link} for entry in feed.entries[:5]]

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# RSS Feed Fetcher")
                    with gr.Row():
                        with gr.Column():
                            feed_url = gr.Textbox(label="RSS Feed URL", placeholder="https://example.com/feed")
                            fetch_btn = gr.Button("Fetch Latest Posts")
                        with gr.Column():
                            feed_output = gr.JSON(label="Latest Feed Entries")

                    fetch_btn.click(
                        fn=fetch_rss_feed,
                        inputs=feed_url,
                        outputs=feed_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Fetch the latest entries from an RSS feed",
                components=["Textbox", "Button", "JSON"],
                metadata={"category": "web_scraping"}
            ),
            "web_scraper": Template(
                code="""
                import gradio as gr
                from bs4 import BeautifulSoup
                import requests

                def scrape_website(url, tag):
                    try:
                        response = requests.get(url)
                        soup = BeautifulSoup(response.text, "html.parser")
                        elements = soup.find_all(tag)
                        return [element.get_text() for element in elements][:5]  # Limit to 5 elements
                    except Exception as e:
                        return f"Error: {str(e)}"

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Web Scraper")
                    with gr.Row():
                        with gr.Column():
                            url_input = gr.Textbox(label="Website URL", placeholder="https://example.com")
                            tag_input = gr.Textbox(label="HTML Tag to Scrape", placeholder="h1, p, div, etc.")
                            scrape_btn = gr.Button("Scrape Website")
                        with gr.Column():
                            result_output = gr.JSON(label="Scraped Results")

                    scrape_btn.click(
                        fn=scrape_website,
                        inputs=[url_input, tag_input],
                        outputs=result_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Scrape text from a website based on the specified HTML tag",
                components=["Textbox", "Button", "JSON"],
                metadata={"category": "web_scraping"}
            ),
            "api_tester": Template(
                code="""
                import gradio as gr
                import requests

                def test_api(endpoint, method, payload):
                    try:
                        if method == "GET":
                            response = requests.get(endpoint)
                        elif method == "POST":
                            response = requests.post(endpoint, json=payload)
                        else:
                            return "Unsupported method"

                        return {
                            "status_code": response.status_code,
                            "response_body": response.json() if response.headers.get("Content-Type") == "application/json" else response.text
                        }
                    except Exception as e:
                        return {"error": str(e)}

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# API Tester")
                    with gr.Row():
                        with gr.Column():
                            endpoint = gr.Textbox(label="API Endpoint", placeholder="https://api.example.com/endpoint")
                            method = gr.Radio(choices=["GET", "POST"], label="HTTP Method", value="GET")
                            payload = gr.JSON(label="Payload (for POST)", value={})
                            test_btn = gr.Button("Test API")
                        with gr.Column():
                            result_output = gr.JSON(label="API Response")

                    test_btn.click(
                        fn=test_api,
                        inputs=[endpoint, method, payload],
                        outputs=result_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Test API endpoints with GET and POST requests",
                components=["Textbox", "Radio", "JSON", "Button"],
                metadata={"category": "api_testing"}
            ),
            "email_scheduler": Template(
                code="""
                import gradio as gr
                import smtplib
                from email.mime.text import MIMEText
                from email.mime.multipart import MIMEMultipart
                from apscheduler.schedulers.background import BackgroundScheduler

                scheduler = BackgroundScheduler()
                scheduler.start()

                def send_email(to_email, subject, body):
                    try:
                        sender_email = "your_email@example.com"
                        password = "your_password"

                        msg = MIMEMultipart()
                        msg['From'] = sender_email
                        msg['To'] = to_email
                        msg['Subject'] = subject

                        msg.attach(MIMEText(body, 'plain'))

                        server = smtplib.SMTP('smtp.example.com', 587)
                        server.starttls()
                        server.login(sender_email, password)
                        text = msg.as_string()
                        server.sendmail(sender_email, to_email, text)
                        server.quit()

                        return "Email sent successfully"
                    except Exception as e:
                        return f"Error: {str(e)}"

                def schedule_email(to_email, subject, body, delay):
                    scheduler.add_job(send_email, 'interval', seconds=delay, args=[to_email, subject, body])
                    return f"Email scheduled to be sent in {delay} seconds"

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Email Scheduler")
                    with gr.Row():
                        with gr.Column():
                            to_email = gr.Textbox(label="Recipient Email")
                            subject = gr.Textbox(label="Subject")
                            body = gr.Textbox(label="Email Body", lines=5)
                            delay = gr.Slider(label="Delay (seconds)", minimum=10, maximum=300, step=10, value=60)
                            schedule_btn = gr.Button("Schedule Email")
                        with gr.Column():
                            result_output = gr.Textbox(label="Result")

                    schedule_btn.click(
                        fn=schedule_email,
                        inputs=[to_email, subject, body, delay],
                        outputs=result_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Schedule emails to be sent after a delay",
                components=["Textbox", "Slider", "Button"],
                metadata={"category": "task_automation"}
            ),
            "log_file_analyzer": Template(
                code="""
                import gradio as gr
                import re

                def analyze_logs(log_file, filter_text):
                    try:
                        logs = log_file.read().decode("utf-8")
                        if filter_text:
                            filtered_logs = "\n".join([line for line in logs.splitlines() if re.search(filter_text, line)])
                        else:
                            filtered_logs = logs
                        return filtered_logs
                    except Exception as e:
                        return f"Error: {str(e)}"

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Log File Analyzer")
                    with gr.Row():
                        with gr.Column():
                            log_input = gr.File(label="Upload Log File")
                            filter_input = gr.Textbox(label="Filter (Regex)", placeholder="Error|Warning")
                            analyze_btn = gr.Button("Analyze Logs")
                        with gr.Column():
                            output_logs = gr.Textbox(label="Filtered Logs", lines=20)

                    analyze_btn.click(
                        fn=analyze_logs,
                        inputs=[log_input, filter_input],
                        outputs=output_logs
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Analyze and filter log files using regex",
                components=["File", "Textbox", "Button"],
                metadata={"category": "log_analysis"}
            ),
            "file_encryption_tool": Template(
                code="""
                import gradio as gr
                from cryptography.fernet import Fernet

                def encrypt_file(file, password):
                    try:
                        key = password.ljust(32, '0').encode()[:32]  # Basic password -> key mapping
                        cipher = Fernet(Fernet.generate_key())
                        file_data = file.read()
                        encrypted_data = cipher.encrypt(file_data)
                        return encrypted_data.decode("utf-8")
                    except Exception as e:
                        return f"Error: {str(e)}"

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# File Encryption Tool")
                    with gr.Row():
                        with gr.Column():
                            file_input = gr.File(label="Upload File")
                            password_input = gr.Textbox(label="Password", type="password")
                            encrypt_btn = gr.Button("Encrypt File")
                        with gr.Column():
                            encrypted_output = gr.Textbox(label = "Encrypted Data", lines=20)

                    encrypt_btn.click(
                        fn=encrypt_file,
                        inputs=[file_input, password_input],
                        outputs=encrypted_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Encrypt a file using a password-based key",
                components=["File", "Textbox", "Button"],
                metadata={"category": "security"}
            ),
            "task_scheduler": Template(
                code="""
                import gradio as gr
                from apscheduler.schedulers.background import BackgroundScheduler
                from datetime import datetime

                scheduler = BackgroundScheduler()
                scheduler.start()

                def schedule_task(task_name, interval):
                    scheduler.add_job(lambda: print(f"Running task: {task_name} at {datetime.now()}"), 'interval', seconds=interval)
                    return f"Task '{task_name}' scheduled to run every {interval} seconds."

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Task Scheduler")
                    with gr.Row():
                        with gr.Column():
                            task_input = gr.Textbox(label="Task Name", placeholder="Example Task")
                            interval_input = gr.Slider(minimum=1, maximum=60, label="Interval (Seconds)", value=10)
                            schedule_btn = gr.Button("Schedule Task")
                        with gr.Column():
                            result_output = gr.Textbox(label="Result")

                    schedule_btn.click(
                        fn=schedule_task,
                        inputs=[task_input, interval_input],
                        outputs=result_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Schedule tasks to run at regular intervals",
                components=["Textbox", "Slider", "Button"],
                metadata={"category": "task_automation"}
            ),
            "code_comparator": Template(
                code="""
                import gradio as gr
                import difflib

                def compare_code(code1, code2):
                    diff = difflib.unified_diff(code1.splitlines(), code2.splitlines(), lineterm='', fromfile='code1', tofile='code2')
                    return '\n'.join(diff)

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Code Comparator")
                    with gr.Row():
                        with gr.Column():
                            code1_input = gr.Textbox(label="Code 1", lines=15, placeholder="Paste the first code snippet here...")
                            code2_input = gr.Textbox(label="Code 2", lines=15, placeholder="Paste the second code snippet here...")
                            compare_btn = gr.Button("Compare Codes")
                        with gr.Column():
                            diff_output = gr.Textbox(label="Difference", lines=20)

                    compare_btn.click(
                        fn=compare_code,
                        inputs=[code1_input, code2_input],
                        outputs=diff_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Compare two code snippets and show the differences",
                components=["Textbox", "Button"],
                metadata={"category": "development"}
            ),
            "database_query_tool": Template(
                code="""
                import gradio as gr
                import sqlite3

                def query_database(db_file, query):
                    try:
                        conn = sqlite3.connect(db_file.name)
                        cursor = conn.cursor()
                        cursor.execute(query)
                        results = cursor.fetchall()
                        conn.close()
                        return results
                    except Exception as e:
                        return f"Error: {str(e)}"

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Database Query Tool")
                    with gr.Row():
                        with gr.Column():
                            db_input = gr.File(label="Upload SQLite DB File")
                            query_input = gr.Textbox(label="SQL Query", placeholder="SELECT * FROM table_name;")
                            query_btn = gr.Button("Run Query")
                        with gr.Column():
                            query_output = gr.JSON(label="Query Results")

                    query_btn.click(
                        fn=query_database,
                        inputs=[db_input, query_input],
                        outputs=query_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Run SQL queries on a SQLite database",
                components=["File", "Textbox", "Button", "JSON"],
                metadata={"category": "database"}
            ),
            "code_generator": Template(
                code="""
                import gradio as gr
                from transformers import pipeline

                code_generator = pipeline("text-generation", model="Salesforce/codegen-2B-multi")

                def generate_code(prompt):
                    response = code_generator(prompt, max_length=150, num_return_sequences=1)
                    return response[0]['generated_text']

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Code Generator ")
                    with gr.Row():
                        with gr.Column():
                            prompt_input = gr.Textbox(label="Enter Code Prompt", placeholder="Write a Python function to reverse a string")
                            generate_btn = gr.Button("Generate Code")
                        with gr.Column():
                            generated_code = gr.Textbox(label="Generated Code", lines=10)

                    generate_btn.click(
                        fn=generate_code,
                        inputs=prompt_input,
                        outputs=generated_code
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Generate code snippets based on natural language prompts",
                components=["Textbox", "Button"],
                metadata={"category": "development"}
            ),
            "code_debugger": Template(
                code="""
                import gradio as gr
                import subprocess

                def debug_code(code):
                    try:
                        with open("temp_code.py", "w") as f:
                            f.write(code)
                        result = subprocess.run(["python3", "temp_code.py"], capture_output=True, text=True)
                        return {"stdout": result.stdout, "stderr": result.stderr}
                    except Exception as e:
                        return {"error": str(e)}

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Code Debugger")
                    with gr.Row():
                        with gr.Column():
                            code_input = gr.Textbox(label="Code to Debug", lines=10, placeholder="Paste your Python code here...")
                            debug_btn = gr.Button("Run Debug")
                        with gr.Column():
                            debug_output = gr.JSON(label="Debug Output")

                    debug_btn.click(
                        fn=debug_code,
                        inputs=code_input,
                        outputs=debug_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Run and debug Python code by capturing stdout and stderr",
                components=["Textbox", "Button", "JSON"],
                metadata={"category": "development"}
            ),
            "multi_agent_task_manager": Template(
                code="""
                import gradio as gr
                from concurrent.futures import ThreadPoolExecutor
                import time

                agent_pool = ThreadPoolExecutor(max_workers=5)
                agent_status = {}

                def agent_task(agent_name, task):
                    agent_status[agent_name] = "Running"
                    time.sleep(5)  # Simulate task duration
                    agent_status[agent_name] = "Completed"
                    return f"Agent {agent_name} has completed the task: {task}"

                def trigger_agents(agents, task):
                    results = []
                    for agent in agents:
                        future = agent_pool.submit(agent_task, agent, task)
                        results.append(f"Agent {agent} triggered")
                    return results

                def get_agent_status():
                    return agent_status

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Multi-Agent Task Manager")
                    with gr.Row():
                        with gr.Column():
                            agents = gr.CheckboxGroup(choices=["Agent1", "Agent2", "Agent3", "Agent4", "Agent5"], label="Select Agents")
                            task_input = gr.Textbox(label="Task to Perform", placeholder="Describe the task...")
                            trigger_btn = gr.Button("Trigger Agents")
                        with gr.Column():
                            task_output = gr.JSON(label="Agent Responses")

                    with gr.Row():
                        status_btn = gr.Button("Get Agent Status")
                        status_output = gr.JSON(label="Current Agent Status")

                    trigger_btn.click(
                        fn=trigger_agents,
                        inputs=[agents, task_input],
                        outputs=task_output
                    )
                    
                    status_btn.click(
                        fn=get_agent_status,
                        inputs=[],
                        outputs=status_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Manage and trigger tasks for multiple autonomous agents",
                components=["CheckboxGroup", "Textbox", "Button", "JSON"],
                metadata={"category": "task_automation"}
            ),
            "auto_code_refactor": Template(
                code="""
                import gradio as gr
                from transformers import pipeline

                code_refactor = pipeline("text2text-generation", model="facebook/bart-large-cnn")

                def refactor_code(code):
                    result = code_refactor(f"Refactor the following Python code: {code}", max_length=150)
                    return result[0]['generated_text']

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Auto Code Refactor")
                    with gr.Row():
                        with gr.Column():
                            code_input = gr.Textbox(label="Code to Refactor", lines=10, placeholder="Paste your Python code here...")
                            refactor_btn = gr.Button("Refactor Code")
                        with gr.Column():
                            refactored_code = gr.Textbox(label="Refactored Code", lines =10)

                    refactor_btn.click(
                        fn=refactor_code,
                        inputs=code_input,
                        outputs=refactored_code
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Automatically refactor Python code for better efficiency or readability",
                components=["Textbox", "Button"],
                metadata={"category": "development"}
            ),
            "agent_cluster_deployer": Template(
                code="""
                import gradio as gr
                import random

                cluster_status = {}

                def deploy_agent_cluster(agent_count, task):
                    cluster_id = random.randint(1000, 9999)
                    cluster_status[cluster_id] = {
                        "status": "Deploying",
                        "agents": agent_count,
                        "task": task
                    }
                    return f"Cluster {cluster_id} is deploying with {agent_count} agents for task: {task}"

                def get_cluster_status():
                    return cluster_status

                with gr.Blocks(theme=gr.themes.Soft()) as demo:
                    gr.Markdown("# Agent Cluster Deployer")
                    with gr.Row():
                        with gr.Column():
                            agent_count = gr.Slider(label="Number of Agents", minimum=1, maximum=10, step=1, value=3)
                            task_input = gr.Textbox(label="Cluster Task", placeholder="Describe the task for the cluster...")
                            deploy_btn = gr.Button("Deploy Cluster")
                        with gr.Column():
                            deploy_output = gr.Textbox(label="Cluster Deployment Status")

                    with gr.Row():
                        status_btn = gr.Button("Get Cluster Status")
                        status_output = gr.JSON(label="Current Cluster Status")

                    deploy_btn.click(
                        fn=deploy_agent_cluster,
                        inputs=[agent_count, task_input],
                        outputs=deploy_output
                    )

                    status_btn.click(
                        fn=get_cluster_status,
                        inputs=[],
                        outputs=status_output
                    )

                if __name__ == "__main__":
                    demo.launch()
                """,
                description="Deploy an autonomous agent cluster for task execution",
                components=["Slider", "Textbox", "Button", "JSON"],
                metadata={"category": "task_automation"}
            )
        }
        
        self.component_index: Dict[str, List[str]] = self._build_component_index()
        self.category_index: Dict[str, List[str]] = self._build_category_index()

    def _build_component_index(self) -> Dict[str, List[str]]:
        """Build index of templates by component"""
        index = {}
        for name, template in self.templates.items():
            for component in template.components:
                if component not in index:
                    index[component] = []
                index[component].append(name)
        return index

    def _build_category_index(self) -> Dict[str, List[str]]:
        """Build index of templates by category"""
        index = {}
        for name, template in self.templates.items():
            category = template.metadata.get("category", "other")
            if category not in index:
                index[category] = []
            index[category].append(name)
        return index

    def search(self, query: str, limit: int = 5) -> List[Dict]:
        """Search templates by description or metadata"""
        try:
            results = []
            for name, template in self.templates.items():
                desc_score = difflib.SequenceMatcher(
                    None, 
                    query.lower(), 
                    template.description.lower()
                ).ratio()

                category_score = difflib.SequenceMatcher(
                    None,
                    query.lower(),
                    template.metadata.get("category", "").lower()
                ).ratio()

                comp_score = sum(0.2 for component in template.components if component.lower() in query.lower())

                final_score = max(desc_score, category_score) + comp_score

                results.append({
                    "name": name,
                    "template": template,
                    "score": final_score
                })

            results.sort(key=lambda x: x["score"], reverse=True)
            return results[:limit]

        except Exception as e:
            logger.error(f"Error searching templates: {str(e)}")
            return []

    def search_by_components(self, components: List[str], limit: int = 5) -> List[Dict]:
        """Search templates by required components"""
        try:
            results = []
            for name, template in self.templates.items():
                matches = sum(1 for c in components if c in template.components)
                if matches > 0:
                    score = matches / len(components)
                    results.append({
                        "name": name,
                        "template": template,
                        "score": score
                    })

            results.sort(key=lambda x: x["score"], reverse=True)
            return results[:limit]

        except Exception as e:
            logger.error(f"Error searching by components: {str(e)}")
            return []

    def search_by_category(self, category: str) -> List[Dict]:
        """Get all templates in a category"""
        try:
            return [
                {
                    "name": name,
                    "template": self.templates[name]
                }
                for name in self.category_index.get(category, [])
            ]
        except Exception as e:
            logger.error(f"Error searching by category: {str(e)}")
            return []

    def get_template(self, name: str) -> Optional[Template]:
        """Get specific template by name"""
        return self.templates.get(name)

    def get_categories(self) -> List[str]:
        """Get list of all categories"""
        return list(self.category_index.keys())

    def get_components(self) -> List[str]:
        """Get list of all components"""
        return list(self.component_index.keys())

    def export_templates(self, path: str):
        """Export templates to JSON file"""
        try:
            data = {
                name: {
                    "code": template.code,
                    "description": template.description,
                    "components": template.components,
                    "metadata": template.metadata,
                    "example": template.example
                }
                for name, template in self.templates.items()
            }

            with open(path, 'w') as f:
                json.dump(data, f, indent=2)

            logger.info(f"Templates exported to {path}")

        except FileNotFoundError:
            logger.error(f"File not found: {path}")
            raise
        except Exception as e:
            logger.error(f"Error exporting templates: {str(e)}")
            raise

    def import_templates(self, path: str):
        """Import templates from JSON file"""
        try:
            with open(path, 'r') as f:
                data = json.load(f)

            for name, template_data in data.items():
                self.templates[name] = Template(
                    code=template_data.get("code", ""),  # Load the actual code from the JSON file
                    description=template_data["description"],
                    components=template_data["components"],
                    metadata=template_data["metadata"],
                    example=template_data.get("example")
                )

            # Rebuild indexes
            self.component_index = self._build_component_index()
            self.category_index = self._build_category_index()

            logger.info(f"Templates imported from {path}")

        except FileNotFoundError:
            logger.error(f"File not found: {path}")
            raise
        except json.JSONDecodeError:
            logger.error(f"Error decoding JSON from file: {path}")
            raise
        except Exception as e:
            logger.error(f"Error importing templates: {str(e)}")
            raise

# Usage example:
if __name__ == "__main__":
    # Initialize template manager
    manager = TemplateManager()

    # Search examples
    print("\nSearching for 'machine learning':")
    results = manager.search("machine learning")
    for result in results:
        print(f"{result['name']}: {result['score']:.2f}")

    print("\nSearching for components ['Image', 'Slider']:")
    results = manager.search_by_components(['Image', 'Slider'])
    for result in results:
        print(f"{result['name']}: {result['score']:.2f}")

    print("\nCategories available:")
    print(manager.get_categories())

    print("\nComponents available:")
    print(manager.get_components())