add infermatic-text and whois plugins for AI text generation and WHOIS lookups
This commit is contained in:
@@ -205,6 +205,41 @@ Data Returned:
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Requires DNSDUMPSTER_KEY environment variable in .env file
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```
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### 🔍 WHOIS Lookup
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**🌐 !whois <domain/ip>**
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Perform comprehensive WHOIS lookups for domains and IP addresses.
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**Features:**
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- Domain validation and IP address recognition
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- Registrar information and WHOIS server details
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- Registration, update, and expiration dates
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- Domain status and name server information
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- Organization and geographic contact details
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- Formatted HTML output with clear sections
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- Comprehensive error handling for invalid queries
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**Usage Examples:**
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```bash
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!whois example.com
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!whois google.com
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!whois 8.8.8.8
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!whois 1.1.1.1
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```
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**Output includes:**
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- Domain/IP query information
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- Registrar and WHOIS server
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- Important dates (creation, update, expiration)
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- Domain status codes
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- Name servers (up to 5, with count if more)
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- Contact information (organization, country, state, city)
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**Error Handling:**
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- Validates domain/IP format before querying
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- Provides clear error messages for failed lookups
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- Handles rate limiting and WHOIS server unavailability
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## ExploitDB Plugin
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A security plugin that searches Exploit-DB for vulnerabilities and exploits directly from Matrix.
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@@ -368,9 +403,33 @@ Generates images using self-hosted Stable Diffusion with customizable parameters
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- `--sampler` - Sampler name (default: DPM++ SDE)
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**📄 !text [prompt] [options]**
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Generates text using Ollama's Mistral 7B Instruct model:
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- `--max_tokens` - Maximum tokens to generate (default: 512)
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- `--temperature` - Sampling temperature (default: 0.7)
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Generates text using the Infermatic AI API with multiple model support:
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**Main Commands:**
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- `!text <prompt>` - Generate text using the default model from INFERMATIC_MODEL
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- `!text --list-models` - List all available models from Infermatic AI
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- `!text --use-model <model> <prompt>` - Use a specific model instead of the default
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**Parameters:**
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- `--temperature <value>` - Set generation temperature (0.0-1.0, default: 0.9)
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- `--max-tokens <value>` - Set maximum tokens to generate (default: 2048)
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**Configuration:**
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- Requires `INFERMATIC_API` environment variable in `.env` file (your API key)
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- Requires `INFERMATIC_MODEL` environment variable in `.env` file (default: Sao10K-L3.1-70B-Hanami-x1)
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**Examples:**
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```bash
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!text write a python function to calculate fibonacci numbers
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!text --use-model llama-v3-8b-instruct explain quantum computing simply
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!text --temperature 0.7 --max-tokens 500 write a haiku about artificial intelligence
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!text --list-models
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```
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**Model Management:**
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- Use `--list-models` to see available models with their capabilities
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- Different models support various context lengths and specializations
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- Costs and token limits vary by model
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### Media & Search Commands
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+1
-1
@@ -14,4 +14,4 @@ config_file = "funguy.conf"
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[plugins.disabled]
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"!uFhErnfpYhhlauJsNK:matrix.org" = [ "youtube-preview", "ai", "proxy",]
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"!vYcfWXpPvxeQvhlFdV:matrix.org" = []
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"!NXdVjDXPxXowPkrJJY:matrix.org" = [ "karma",]
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"!NXdVjDXPxXowPkrJJY:matrix.org" = [ "karma"]
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@@ -1,4 +1,4 @@
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#!/usr/bin/env python
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#!/usr/bin/env python3
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"""
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Funguy Bot Class
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+44
-1
@@ -77,6 +77,23 @@ async def handle_command(room, message, bot, prefix, config):
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<p>Fetches the current Bitcoin price in USD from bitcointicker.co API. Shows real-time BTC/USD price with proper formatting. Includes error handling for API timeouts and data parsing issues.</p>
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</details>
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<details><summary>🌐 <strong>!whois <domain/ip></strong></summary>
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<p>Perform comprehensive WHOIS lookups for domains and IP addresses. Retrieves registrar information, registration dates, name servers, and contact details from WHOIS databases.</p>
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<p><strong>Usage:</strong></p>
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<ul>
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<li><code>!whois <domain></code> - Query domain registration information</li>
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<li><code>!whois <ip></code> - Query IP address allocation details</li>
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</ul>
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<p><strong>Examples:</strong></p>
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<ul>
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<li><code>!whois example.com</code></li>
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<li><code>!whois google.com</code></li>
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<li><code>!whois 8.8.8.8</code></li>
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<li><code>!whois 1.1.1.1</code></li>
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</ul>
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<p><strong>Output includes:</strong> Domain/IP information, registrar, WHOIS server, creation/expiration dates, name servers, and contact details.</p>
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</details>
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<details><summary>🔍 <strong>!shodan [command] [query]</strong></summary>
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<p>Shodan.io integration for security reconnaissance and threat intelligence.</p>
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<p><strong>Commands:</strong></p>
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@@ -290,7 +307,33 @@ Search Exploit-DB for security vulnerabilities and exploits. Returns detailed in
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</details>
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<details><summary>📄 <strong>!text [prompt]</strong></summary>
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<p>Generates text using Ollama's Mistral 7B Instruct model. Options: --max_tokens, --temperature. Uses queuing system for sequential processing.</p>
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<p>Generates text using the Infermatic AI API. Supports multiple models, configurable parameters, and model listing. Uses queuing system for sequential processing.</p>
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<p><strong>Usage:</strong></p>
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<ul>
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<li><code>!text <prompt></code> - Generate text using the default model</li>
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<li><code>!text --list-models</code> - List all available models from Infermatic AI</li>
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<li><code>!text --use-model <model_name> <prompt></code> - Use a specific model instead of the default</li>
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<li><code>!text --temperature <value> <prompt></code> - Set temperature (0.0-1.0, default: 0.9)</li>
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<li><code>!text --max-tokens <value> <prompt></code> - Set maximum tokens to generate (default: 2048)</li>
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</ul>
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<p><strong>Configuration:</strong></p>
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<ul>
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<li>Requires <code>INFERMATIC_API</code> environment variable set to your API key</li>
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<li>Requires <code>INFERMATIC_MODEL</code> environment variable for default model (default: Sao10K-L3.1-70B-Hanami-x1)</li>
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</ul>
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<p><strong>Model Management:</strong></p>
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<ul>
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<li>Use <code>!text --list-models</code> to see all available models</li>
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<li>Models support different capabilities and context lengths</li>
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<li>Costs and token limits vary by model</li>
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</ul>
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<p><strong>Examples:</strong></p>
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<ul>
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<li><code>!text write a python function to calculate fibonacci</code></li>
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<li><code>!text --list-models</code></li>
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<li><code>!text --use-model llama-v3-8b-instruct explain quantum computing</code></li>
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<li><code>!text --temperature 0.7 --max-tokens 500 write a haiku about AI</code></li>
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</ul>
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</details>
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<details><summary>📰 <strong>!xkcd</strong></summary>
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@@ -0,0 +1,233 @@
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"""
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Plugin for generating text using Infermatic AI API and sending it to a Matrix chat room.
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"""
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import os
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import requests
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import argparse
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import json
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import simplematrixbotlib as botlib
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from asyncio import Queue
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from dotenv import load_dotenv
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# Load environment variables from .env file in the parent directory
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plugin_dir = os.path.dirname(os.path.abspath(__file__))
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parent_dir = os.path.dirname(plugin_dir)
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dotenv_path = os.path.join(parent_dir, '.env')
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load_dotenv(dotenv_path)
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# Infermatic AI API configuration
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INFERMATIC_API_KEY = os.getenv("INFERMATIC_API", "")
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DEFAULT_MODEL = os.getenv("INFERMATIC_MODEL", "Sao10K-L3.1-70B-Hanami-x1")
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INFERMATIC_API_BASE = "https://api.totalgpt.ai/v1"
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# Queue to store pending commands
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command_queue = Queue()
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async def process_command(room, message, bot, prefix, config):
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"""Queue and process !text commands sequentially."""
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match = botlib.MessageMatch(room, message, bot, prefix)
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if match.prefix() and match.command("text"):
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if command_queue.empty():
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await handle_command(room, message, bot, prefix, config)
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else:
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await command_queue.put((room, message, bot, prefix, config))
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await bot.api.send_text_message(room.room_id, "Command queued. Please wait for the current request to finish.")
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async def handle_command(room, message, bot, prefix, config):
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"""Handle !text command: generate text using Infermatic AI API."""
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match = botlib.MessageMatch(room, message, bot, prefix)
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if not (match.prefix() and match.command("text")):
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return
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# Check if API key is configured
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if not INFERMATIC_API_KEY:
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await bot.api.send_text_message(
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room.room_id,
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"Infermatic API key not configured. Please set INFERMATIC_API environment variable."
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)
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return
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# Parse command arguments
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args = match.args()
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if len(args) < 1:
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await show_usage(room, bot)
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return
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# Check if it's a --list-models command
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if args[0] == "--list-models":
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await list_models(room, bot)
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return
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# Parse other arguments
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try:
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# Extract options manually since argparse doesn't handle mixed positional/optional well
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temperature = 0.9
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max_tokens = 2048
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custom_model = None
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prompt_parts = []
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i = 0
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while i < len(args):
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if args[i] == "--temperature" and i + 1 < len(args):
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temperature = float(args[i + 1])
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i += 2
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elif args[i] == "--max-tokens" and i + 1 < len(args):
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max_tokens = int(args[i + 1])
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i += 2
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elif args[i] == "--use-model" and i + 1 < len(args):
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custom_model = args[i + 1]
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i += 2
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else:
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prompt_parts.append(args[i])
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i += 1
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prompt = ' '.join(prompt_parts).strip()
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if not prompt:
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await show_usage(room, bot)
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return
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model = custom_model or DEFAULT_MODEL
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await generate_text(room, bot, prompt, model, temperature, max_tokens)
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except ValueError as e:
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await bot.api.send_text_message(room.room_id, f"Invalid parameter value: {e}")
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except Exception as e:
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await bot.api.send_text_message(room.room_id, f"Error processing command: {str(e)}")
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async def show_usage(room, bot):
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"""Display command usage information."""
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usage = """
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<strong>📄 Infermatic Text Generation Usage:</strong>
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<strong>Basic:</strong>
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• <code>!text <prompt></code> - Generate text using default model
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<strong>Commands:</strong>
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• <code>!text --list-models</code> - List all available models
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• <code>!text --use-model <model> <prompt></code> - Use specific model
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<strong>Parameters:</strong>
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• <code>--temperature <0.0-1.0></code> - Set temperature (default: 0.9)
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• <code>--max-tokens <number></code> - Set max tokens (default: 2048)
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<strong>Examples:</strong>
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• <code>!text write a python function to calculate fibonacci</code>
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• <code>!text --list-models</code>
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• <code>!text --use-model llama-v3-8b-instruct explain quantum computing</code>
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• <code>!text --temperature 0.7 write a haiku about AI</code>
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"""
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await bot.api.send_markdown_message(room.room_id, usage)
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async def list_models(room, bot):
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"""List all available models from Infermatic AI."""
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try:
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await bot.api.send_text_message(room.room_id, "🔍 Fetching available models...")
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url = f"{INFERMATIC_API_BASE}/models"
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headers = {
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"Authorization": f"Bearer {INFERMATIC_API_KEY}",
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"Content-Type": "application/json"
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}
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response = requests.get(url, headers=headers, timeout=30)
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response.raise_for_status()
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data = response.json()
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models = data.get('data', [])
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if not models:
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await bot.api.send_text_message(room.room_id, "No models found or error in response.")
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return
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# Format the model list
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output = "<strong>🔧 Available Models:</strong><br><br>"
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for model in models:
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model_id = model.get('id', 'Unknown')
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model_name = model.get('name', model_id)
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context_length = model.get('context_length', 'Unknown')
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pricing = model.get('pricing', {})
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output += f"<strong>• {model_name}</strong><br>"
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output += f" └─ ID: <code>{model_id}</code><br>"
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output += f" └─ Context: {context_length}<br>"
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if pricing:
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prompt_price = pricing.get('prompt', '0')
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completion_price = pricing.get('completion', '0')
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output += f" └─ Price: ${prompt_price}/${completion_price} per 1M tokens<br>"
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output += f" └─ <strong>Usage:</strong> <code>!text --use-model {model_id} <prompt></code><br><br>"
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# Wrap in collapsible details since list can be long
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output = f"<details><summary><strong>🔧 Available Models (Click to expand)</strong></summary>{output}</details>"
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await bot.api.send_markdown_message(room.room_id, output)
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except requests.exceptions.RequestException as e:
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await bot.api.send_text_message(room.room_id, f"❌ Error fetching models: {str(e)}")
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except Exception as e:
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await bot.api.send_text_message(room.room_id, f"❌ Unexpected error: {str(e)}")
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async def generate_text(room, bot, prompt, model, temperature, max_tokens):
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"""Generate text using the Infermatic AI API."""
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try:
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# Send initial processing message
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await bot.api.send_text_message(room.room_id, f"📝 Generating text...")
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url = f"{INFERMATIC_API_BASE}/chat/completions"
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headers = {
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"Authorization": f"Bearer {INFERMATIC_API_KEY}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": model,
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"messages": [
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{"role": "user", "content": prompt}
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],
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"temperature": temperature,
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"max_tokens": max_tokens
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}
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response = requests.post(url, headers=headers, json=payload, timeout=120)
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response.raise_for_status()
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data = response.json()
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generated_text = data.get('choices', [{}])[0].get('message', {}).get('content', '').strip()
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if not generated_text:
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await bot.api.send_text_message(room.room_id, "No response generated.")
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return
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# Format the output with collapsible sections
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output = f"<details><summary><strong>📝 Generated Text (Click to expand)</strong></summary>"
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output += f"<strong>Model:</strong> <code>{model}</code><br><br>"
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output += f"<strong>Prompt:</strong> {prompt}<br><br>"
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output += f"<strong>Response:</strong><br><br>"
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output += f"{generated_text}"
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output += f"</details>"
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await bot.api.send_markdown_message(room.room_id, output)
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except requests.exceptions.Timeout:
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await bot.api.send_text_message(room.room_id, "❌ Request timed out. The model is taking too long to respond.")
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except requests.exceptions.HTTPError as e:
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if e.response.status_code == 401:
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await bot.api.send_text_message(room.room_id, "❌ Authentication failed. Please check your INFERMATIC_API key.")
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elif e.response.status_code == 429:
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await bot.api.send_text_message(room.room_id, "❌ Rate limit exceeded. Please try again later.")
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else:
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await bot.api.send_text_message(room.room_id, f"❌ API error: HTTP {e.response.status_code}")
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except Exception as e:
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await bot.api.send_text_message(room.room_id, f"❌ Error generating text: {str(e)}")
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finally:
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# Process next queued command
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if not command_queue.empty():
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next_command = await command_queue.get()
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await handle_command(*next_command)
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@@ -1,95 +0,0 @@
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"""
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Plugin for generating text using Ollama's Mistral 7B Instruct model and sending it to a Matrix chat room.
|
||||
"""
|
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import requests
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from asyncio import Queue
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import simplematrixbotlib as botlib
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import argparse
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# Queue to store pending commands
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command_queue = Queue()
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API_URL = "http://localhost:11434/api/generate"
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MODEL_NAME = "mistral:7b-instruct"
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async def process_command(room, message, bot, prefix, config):
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"""
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Queue and process !text commands sequentially.
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"""
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match = botlib.MessageMatch(room, message, bot, prefix)
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if match.prefix() and match.command("text"):
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if command_queue.empty():
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await handle_command(room, message, bot, prefix, config)
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else:
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await command_queue.put((room, message, bot, prefix, config))
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async def handle_command(room, message, bot, prefix, config):
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"""
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Send the prompt to Ollama API and return the generated text.
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"""
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match = botlib.MessageMatch(room, message, bot, prefix)
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if not (match.prefix() and match.command("text")):
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return
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# Parse optional arguments
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parser = argparse.ArgumentParser(description='Generate text using Ollama API')
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parser.add_argument('--max_tokens', type=int, default=512, help='Maximum tokens to generate')
|
||||
parser.add_argument('--temperature', type=float, default=0.7, help='Temperature for generation')
|
||||
parser.add_argument('prompt', nargs='+', help='Prompt for the model')
|
||||
|
||||
try:
|
||||
args = parser.parse_args(message.body.split()[1:]) # Skip command itself
|
||||
prompt = ' '.join(args.prompt).strip()
|
||||
|
||||
if not prompt:
|
||||
await bot.api.send_text_message(room.room_id, "Usage: !text <your prompt here>")
|
||||
return
|
||||
|
||||
payload = {
|
||||
"model": MODEL_NAME,
|
||||
"prompt": prompt,
|
||||
"max_tokens": args.max_tokens,
|
||||
"temperature": args.temperature,
|
||||
"stream": False
|
||||
}
|
||||
|
||||
response = requests.post(API_URL, json=payload, timeout=60)
|
||||
response.raise_for_status()
|
||||
r = response.json()
|
||||
|
||||
generated_text = r.get("response", "").strip()
|
||||
if not generated_text:
|
||||
generated_text = "(No response from model)"
|
||||
|
||||
await bot.api.send_text_message(room.room_id, generated_text)
|
||||
|
||||
except argparse.ArgumentError as e:
|
||||
await bot.api.send_text_message(room.room_id, f"Argument error: {e}")
|
||||
except requests.exceptions.RequestException as e:
|
||||
await bot.api.send_text_message(room.room_id, f"Error connecting to Ollama API: {e}")
|
||||
except Exception as e:
|
||||
await bot.api.send_text_message(room.room_id, f"Unexpected error: {e}")
|
||||
finally:
|
||||
# Process next command from the queue, if any
|
||||
if not command_queue.empty():
|
||||
next_command = await command_queue.get()
|
||||
await handle_command(*next_command)
|
||||
|
||||
def print_help():
|
||||
"""
|
||||
Generates help text for the !text command.
|
||||
"""
|
||||
return """
|
||||
<p>Generate text using Ollama's Mistral 7B Instruct model</p>
|
||||
|
||||
<p>Usage:</p>
|
||||
<ul>
|
||||
<li>!text <prompt> - Basic prompt for the model</li>
|
||||
<li>Optional arguments:</li>
|
||||
<ul>
|
||||
<li>--max_tokens MAX_TOKENS - Maximum tokens to generate (default 512)</li>
|
||||
<li>--temperature TEMPERATURE - Sampling temperature (default 0.7)</li>
|
||||
</ul>
|
||||
</ul>
|
||||
"""
|
||||
@@ -118,7 +118,7 @@ async def handle_command(room, message, bot, prefix, config):
|
||||
r = response.json()
|
||||
|
||||
# Use secure temporary file
|
||||
with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as temp_file:
|
||||
with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as temp_file:
|
||||
filename = temp_file.name
|
||||
temp_file.write(base64.b64decode(r['images'][0]))
|
||||
|
||||
|
||||
@@ -1,187 +0,0 @@
|
||||
"""
|
||||
Plugin for providing a command to fetch YouTube video information from links.
|
||||
"""
|
||||
|
||||
# Importing necessary libraries
|
||||
import re
|
||||
import logging
|
||||
import asyncio
|
||||
import aiohttp
|
||||
import yt_dlp
|
||||
import simplematrixbotlib as botlib
|
||||
from youtube_title_parse import get_artist_title
|
||||
|
||||
LYRICIST_API_URL = "https://lyrist.vercel.app/api/{}/{}"
|
||||
|
||||
|
||||
def seconds_to_minutes_seconds(seconds):
|
||||
"""
|
||||
Converts seconds to a string representation of minutes and seconds.
|
||||
|
||||
Args:
|
||||
seconds (int): The number of seconds.
|
||||
|
||||
Returns:
|
||||
str: A string representation of minutes and seconds in the format MM:SS.
|
||||
"""
|
||||
minutes = seconds // 60
|
||||
seconds %= 60
|
||||
return f"{minutes:02d}:{seconds:02d}"
|
||||
|
||||
|
||||
async def fetch_lyrics(song, artist):
|
||||
"""
|
||||
Asynchronously fetches lyrics for a song from the Lyricist API.
|
||||
|
||||
Args:
|
||||
song (str): The name of the song.
|
||||
artist (str): The name of the artist.
|
||||
|
||||
Returns:
|
||||
str: Lyrics of the song.
|
||||
None if an error occurs during fetching.
|
||||
"""
|
||||
try:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
url = LYRICIST_API_URL.format(artist, song)
|
||||
logging.info(f"Fetching lyrics from: {url}")
|
||||
async with session.get(url, timeout=aiohttp.ClientTimeout(total=10)) as response:
|
||||
if response.status == 200:
|
||||
data = await response.json()
|
||||
return data.get("lyrics")
|
||||
else:
|
||||
logging.warning(f"Lyrics API returned status {response.status}")
|
||||
return None
|
||||
except asyncio.TimeoutError:
|
||||
logging.error("Timeout fetching lyrics")
|
||||
return None
|
||||
except Exception as e:
|
||||
logging.error(f"Error fetching lyrics: {str(e)}")
|
||||
return None
|
||||
|
||||
|
||||
async def fetch_youtube_info(youtube_url):
|
||||
"""
|
||||
Asynchronously fetches information about a YouTube video using yt-dlp.
|
||||
|
||||
Args:
|
||||
youtube_url (str): The URL of the YouTube video.
|
||||
|
||||
Returns:
|
||||
str: A message containing information about the YouTube video.
|
||||
None if an error occurs during fetching.
|
||||
"""
|
||||
try:
|
||||
logging.info(f"Fetching YouTube info for: {youtube_url}")
|
||||
|
||||
# Configure yt-dlp options
|
||||
ydl_opts = {
|
||||
'quiet': True,
|
||||
'no_warnings': True,
|
||||
'extract_flat': False,
|
||||
'skip_download': True,
|
||||
}
|
||||
|
||||
# Run yt-dlp in thread pool to avoid blocking
|
||||
loop = asyncio.get_event_loop()
|
||||
|
||||
def extract_info():
|
||||
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
|
||||
return ydl.extract_info(youtube_url, download=False)
|
||||
|
||||
info = await loop.run_in_executor(None, extract_info)
|
||||
|
||||
if not info:
|
||||
logging.error("No info returned from yt-dlp")
|
||||
return None
|
||||
|
||||
# Extract video information
|
||||
title = info.get('title', 'Unknown Title')
|
||||
description = info.get('description', 'No description available')
|
||||
duration = info.get('duration', 0)
|
||||
view_count = info.get('view_count', 0)
|
||||
uploader = info.get('uploader', 'Unknown')
|
||||
|
||||
logging.info(f"Video title: {title}")
|
||||
|
||||
length = seconds_to_minutes_seconds(duration)
|
||||
|
||||
# Parse artist and song from title
|
||||
artist, song = get_artist_title(title)
|
||||
logging.info(f"Parsed artist: {artist}, song: {song}")
|
||||
|
||||
# Limit description length to avoid huge messages
|
||||
if len(description) > 500:
|
||||
description = description[:500] + "..."
|
||||
|
||||
description_with_breaks = description.replace('\n', '<br>')
|
||||
|
||||
# Build basic info message
|
||||
info_message = f"""<strong>🎬🎝 Title:</strong> {title}<br><strong>Length:</strong> {length} | <strong>Views:</strong> {view_count:,} | <strong>Uploader:</strong> {uploader}<br><details><summary><strong>⤵︎Description⤵︎</strong></summary>{description_with_breaks}</details>"""
|
||||
|
||||
# Try to fetch lyrics if artist and song were parsed
|
||||
if artist and song:
|
||||
logging.info("Attempting to fetch lyrics...")
|
||||
lyrics = await fetch_lyrics(song, artist)
|
||||
if lyrics:
|
||||
lyrics = lyrics.replace('\n', "<br>")
|
||||
# Limit lyrics length
|
||||
if len(lyrics) > 3000:
|
||||
lyrics = lyrics[:3000] + "<br>...(truncated)"
|
||||
info_message += f"<br><details><summary><strong>🎵 Lyrics:</strong></summary><br>{lyrics}</details>"
|
||||
else:
|
||||
logging.info("No lyrics found")
|
||||
else:
|
||||
logging.info("Could not parse artist/song from title, skipping lyrics")
|
||||
|
||||
return info_message
|
||||
except Exception as e:
|
||||
logging.error(f"Error fetching YouTube video information: {str(e)}", exc_info=True)
|
||||
return None
|
||||
|
||||
|
||||
async def handle_command(room, message, bot, prefix, config):
|
||||
"""
|
||||
Asynchronously handles the command to fetch YouTube video information.
|
||||
|
||||
Args:
|
||||
room (Room): The Matrix room where the command was invoked.
|
||||
message (RoomMessage): The message object containing the command.
|
||||
bot (MatrixBot): The Matrix bot instance.
|
||||
prefix (str): The command prefix.
|
||||
config (dict): The bot's configuration.
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
match = botlib.MessageMatch(room, message, bot, prefix)
|
||||
|
||||
# Check if message contains a YouTube link
|
||||
if match.is_not_from_this_bot() and re.search(r'(youtube\.com/watch\?v=|youtu\.be/)', message.body):
|
||||
logging.info(f"YouTube link detected in message: {message.body}")
|
||||
|
||||
# Match both youtube.com and youtu.be formats
|
||||
video_id_match = re.search(r'(?:youtube\.com/watch\?v=|youtu\.be/)([a-zA-Z0-9_-]{11})', message.body)
|
||||
|
||||
if video_id_match:
|
||||
video_id = video_id_match.group(1)
|
||||
youtube_url = f"https://www.youtube.com/watch?v={video_id}"
|
||||
logging.info(f"Fetching information for YouTube video ID: {video_id}")
|
||||
|
||||
retry_count = 2 # Reduced retries since yt-dlp is more reliable
|
||||
while retry_count > 0:
|
||||
info_message = await fetch_youtube_info(youtube_url)
|
||||
if info_message:
|
||||
await bot.api.send_markdown_message(room.room_id, info_message)
|
||||
logging.info("Sent YouTube video information to the room")
|
||||
break
|
||||
else:
|
||||
logging.warning(f"Failed to fetch info, retrying... ({retry_count-1} attempts left)")
|
||||
retry_count -= 1
|
||||
if retry_count > 0:
|
||||
await asyncio.sleep(2) # wait for 2 seconds before retrying
|
||||
else:
|
||||
logging.error("Failed to fetch YouTube video information after all retries")
|
||||
await bot.api.send_text_message(room.room_id, "Failed to fetch YouTube video information. The video may be unavailable or age-restricted.")
|
||||
else:
|
||||
logging.warning("Could not extract video ID from YouTube URL")
|
||||
@@ -28,7 +28,7 @@ async def handle_command(room, message, bot, PREFIX, config):
|
||||
else:
|
||||
search_terms = " ".join(args)
|
||||
logging.info(f"Performing YouTube search for: {search_terms}")
|
||||
results = YoutubeSearch(search_terms, max_results=1).to_dict()
|
||||
results = YoutubeSearch(search_terms, max_results=3).to_dict()
|
||||
if results:
|
||||
output = generate_output(results)
|
||||
await send_collapsible_message(room, bot, output)
|
||||
|
||||
@@ -13,3 +13,5 @@ schedule
|
||||
yt-dlp
|
||||
pyopenssl
|
||||
psutil
|
||||
toml
|
||||
python-whois
|
||||
Reference in New Issue
Block a user