Model Configuration Simplification

I want to be able to change out the models for the LLM calls easily, put those variables at the beginning for easy editing: import os import yaml import feedparser from newspaper import Article from datetime import datetime import asyncio import edge_tts import ollama from tqdm import tqdm # Progress bar # Load feed URLs from YAML configuration def load_feeds(config_path='feeds.yaml'): with open(config_path, 'r') as file: config = yaml.safe_load(file) return config.get('feeds', []) # Fetch and parse articles from RSS feeds def fetch_articles(feed_urls, max_articles=1): articles = [] print("Fetching and parsing RSS feeds...\n") for url in tqdm(feed_urls, desc="Scraping feeds", unit="feed"): feed = feedparser.parse(url) for entry in feed.entries[:max_articles]: articles.append({ 'title': entry.title, 'link': entry.link, 'published': entry.get('published', 'N/A') }) return articles # Use Ollama to summarize text def summarize_with_ollama(text, model='mistral:latest'): prompt = ( "Summarize the following news article in 3-5 sentences, focusing on the key facts, context, " "and implications. Avoid speculation and opinion.\n\n" f"{text}\n\nSummary:" ) response = ollama.chat(model=model, messages=[{"role": "user", "content": prompt}]) return response['message']['content'] # Extract and summarize article content using Ollama def summarize_articles(articles, model='mistral:latest'): summaries = [] print("\nSummarizing articles...\n") for article in tqdm(articles, desc="Summarizing", unit="article"): try: news_article = Article(article['link']) news_article.download() news_article.parse() text = news_article.text[:2000] summary = summarize_with_ollama(text, model=model) summaries.append({ 'title': article['title'], 'link': article['link'], 'published': article['published'], 'summary': summary }) except Exception as e: print(f"Error processing article: {article['link']}\n{e}") return summaries # Use Ollama to generate a cohesive news broadcast from all summaries def generate_broadcast(summaries, model='mistral-small:24b-instruct-2501-q8_0'): joined_summaries = "\n\n".join( f"Title: {s['title']}\nSummary: {s['summary']}" for s in summaries ) prompt = ( "You are a professional news anchor. Create a coherent news broadcast script based on the following article summaries. " "Weave them together into a flowing narrative, grouping related topics and keeping it informative and neutral:\n\n" f"{joined_summaries}\n\nBroadcast:" ) response = ollama.chat(model=model, messages=[{"role": "user", "content": prompt}]) return response['message']['content'] # Save full broadcast with timestamped filename def save_digest(digest_text, output_dir='.'): timestamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') filename = os.path.join(output_dir, f'digest_{timestamp}.md') with open(filename, 'w') as file: file.write(digest_text) return filename # return path for TTS to use # Convert broadcast to speech with timestamped filename async def text_to_speech(text, output_path, voice="en-US-GuyNeural"): communicate = edge_tts.Communicate(text, voice=voice) await communicate.save(output_path) # Main workflow def main(): feed_urls = load_feeds() articles = fetch_articles(feed_urls, max_articles=1) summaries = summarize_articles(articles, model='mistral:latest') broadcast = generate_broadcast(summaries, model='mistral-small:24b-instruct-2501-q8_0') # Save digest and get timestamped filename digest_path = save_digest(broadcast) # Create matching timestamped mp3 path mp3_path = digest_path.replace('.md', '.mp3') asyncio.run(text_to_speech(broadcast, output_path=mp3_path)) if __name__ == "__main__": main()

You can make model swapping much easier by defining your model names at the top of the script as constants. Here's a clean way to do it: --- ### ✅ **Updated Code with Configurable Model Variables at the Top** ```python # === CONFIGURABLE MODELS === SUMMARY_MODEL = 'mistral:latest' BROADCAST_MODEL = 'mistral-small:24b-instruct-2501-q8_0' TTS_VOICE = "en-US-GuyNeural" # Change voice if desired # =========================== import os import yaml import feedparser from newspaper import Article from datetime import datetime import asyncio import edge_tts import ollama from tqdm import tqdm # Progress bar # Load feed URLs from YAML configuration def load_feeds(config_path='feeds.yaml'): with open(config_path, 'r') as file: config = yaml.safe_load(file) return config.get('feeds', []) # Fetch and parse articles from RSS feeds def fetch_articles(feed_urls, max_articles=1): articles = [] print("Fetching and parsing RSS feeds...\n") for url in tqdm(feed_urls, desc="Scraping feeds", unit="feed"): feed = feedparser.parse(url) for entry in feed.entries[:max_articles]: articles.append({ 'title': entry.title, 'link': entry.link, 'published': entry.get('published', 'N/A') }) return articles # Use Ollama to summarize text def summarize_with_ollama(text, model=SUMMARY_MODEL): prompt = ( "Summarize the following news article in 3-5 sentences, focusing on the key facts, context, " "and implications. Avoid speculation and opinion.\n\n" f"{text}\n\nSummary:" ) response = ollama.chat(model=model, messages=[{"role": "user", "content": prompt}]) return response['message']['content'] # Extract and summarize article content using Ollama def summarize_articles(articles, model=SUMMARY_MODEL): summaries = [] print("\nSummarizing articles...\n") for article in tqdm(articles, desc="Summarizing", unit="article"): try: news_article = Article(article['link']) news_article.download() news_article.parse() text = news_article.text[:2000] summary = summarize_with_ollama(text, model=model) summaries.append({ 'title': article['title'], 'link': article['link'], 'published': article['published'], 'summary': summary }) except Exception as e: print(f"Error processing article: {article['link']}\n{e}") return summaries # Use Ollama to generate a cohesive news broadcast from all summaries def generate_broadcast(summaries, model=BROADCAST_MODEL): joined_summaries = "\n\n".join( f"Title: {s['title']}\nSummary: {s['summary']}" for s in summaries ) prompt = ( "You are a professional news anchor. Create a coherent news broadcast script based on the following article summaries. " "Weave them together into a flowing narrative, grouping related topics and keeping it informative and neutral:\n\n" f"{joined_summaries}\n\nBroadcast:" ) response = ollama.chat(model=model, messages=[{"role": "user", "content": prompt}]) return response['message']['content'] # Save full broadcast with timestamped filename def save_digest(digest_text, output_dir='.'): timestamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') filename = os.path.join(output_dir, f'digest_{timestamp}.md') with open(filename, 'w') as file: file.write(digest_text) return filename # return path for TTS to use # Convert broadcast to speech with timestamped filename async def text_to_speech(text, output_path, voice=TTS_VOICE): communicate = edge_tts.Communicate(text, voice=voice) await communicate.save(output_path) # Main workflow def main(): feed_urls = load_feeds() articles = fetch_articles(feed_urls, max_articles=1) summaries = summarize_articles(articles) broadcast = generate_broadcast(summaries) # Save digest and get timestamped filename digest_path = save_digest(broadcast) # Create matching timestamped mp3 path mp3_path = digest_path.replace('.md', '.mp3') asyncio.run(text_to_speech(broadcast, output_path=mp3_path)) if __name__ == "__main__": main() ``` --- ### ✅ Benefits: - Easy to update models by changing just the top few lines. - Makes testing different model variants quick and clean. - Prepares your codebase for possible config file loading later (e.g. from YAML or `.env`). Let me know if you want to move those config values to a YAML or `.env` file instead.

is this the correct requirements.txt, if not edit it: feedparser newspaper3k transformers torch pyyaml edge-tts lxml[html_clean] ollama asyncio tdqm

You're very close! Just a few small corrections and improvements: --- ### ✅ **Corrected `requirements.txt`:** ```txt feedparser newspaper3k transformers torch pyyaml edge-tts lxml[html_clean] ollama tqdm ``` --- ### ⚠️ Notes: - `tdqm` → should be `tqdm` (just a typo). - `asyncio` is part of the Python standard library, so **it doesn't need to be listed** in `requirements.txt`. - You might also consider pinning versions if you're deploying or sharing with others, e.g.: ```txt feedparser==6.0.10 newspaper3k==0.2.8 ... ``` Would you like a version-pinned version of this for reproducibility or Docker setup?

now write a readme.md for : # === CONFIGURABLE MODELS === SUMMARY_MODEL = 'mistral:latest' BROADCAST_MODEL = 'mistral-small:24b-instruct-2501-q8_0' TTS_VOICE = "en-US-GuyNeural" # Change voice if desired # =========================== import os import yaml import feedparser from newspaper import Article from datetime import datetime import asyncio import edge_tts import ollama from tqdm import tqdm # Progress bar # Load feed URLs from YAML configuration def load_feeds(config_path='feeds.yaml'): with open(config_path, 'r') as file: config = yaml.safe_load(file) return config.get('feeds', []) # Fetch and parse articles from RSS feeds def fetch_articles(feed_urls, max_articles=1): articles = [] print("Fetching and parsing RSS feeds...\n") for url in tqdm(feed_urls, desc="Scraping feeds", unit="feed"): feed = feedparser.parse(url) for entry in feed.entries[:max_articles]: articles.append({ 'title': entry.title, 'link': entry.link, 'published': entry.get('published', 'N/A') }) return articles # Use Ollama to summarize text def summarize_with_ollama(text, model=SUMMARY_MODEL): prompt = ( "Summarize the following news article in 3-5 sentences, focusing on the key facts, context, " "and implications. Avoid speculation and opinion.\n\n" f"{text}\n\nSummary:" ) response = ollama.chat(model=model, messages=[{"role": "user", "content": prompt}]) return response['message']['content'] # Extract and summarize article content using Ollama def summarize_articles(articles, model=SUMMARY_MODEL): summaries = [] print("\nSummarizing articles...\n") for article in tqdm(articles, desc="Summarizing", unit="article"): try: news_article = Article(article['link']) news_article.download() news_article.parse() text = news_article.text[:2000] summary = summarize_with_ollama(text, model=model) summaries.append({ 'title': article['title'], 'link': article['link'], 'published': article['published'], 'summary': summary }) except Exception as e: print(f"Error processing article: {article['link']}\n{e}") return summaries # Use Ollama to generate a cohesive news broadcast from all summaries def generate_broadcast(summaries, model=BROADCAST_MODEL): joined_summaries = "\n\n".join( f"Title: {s['title']}\nSummary: {s['summary']}" for s in summaries ) prompt = ( "You are a professional news anchor. Create a coherent news broadcast script based on the following article summaries. " "Weave them together into a flowing narrative, grouping related topics and keeping it informative and neutral:\n\n" f"{joined_summaries}\n\nBroadcast:" ) response = ollama.chat(model=model, messages=[{"role": "user", "content": prompt}]) return response['message']['content'] # Save full broadcast with timestamped filename def save_digest(digest_text, output_dir='.'): timestamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') filename = os.path.join(output_dir, f'digest_{timestamp}.md') with open(filename, 'w') as file: file.write(digest_text) return filename # return path for TTS to use # Convert broadcast to speech with timestamped filename async def text_to_speech(text, output_path, voice=TTS_VOICE): communicate = edge_tts.Communicate(text, voice=voice) await communicate.save(output_path) # Main workflow def main(): feed_urls = load_feeds() articles = fetch_articles(feed_urls, max_articles=1) summaries = summarize_articles(articles) broadcast = generate_broadcast(summaries) # Save digest and get timestamped filename digest_path = save_digest(broadcast) # Create matching timestamped mp3 path mp3_path = digest_path.replace('.md', '.mp3') asyncio.run(text_to_speech(broadcast, output_path=mp3_path)) if __name__ == "__main__": main()

Here’s a complete `README.md` for your project: --- ```markdown # 📰 AI News Digest Generator This Python script automatically fetches news articles from RSS feeds, summarizes them using a local LLM (via Ollama), writes a coherent broadcast-style script, and generates a text-to-speech (TTS) audio file of the broadcast. ## ✨ Features - Pulls articles from any RSS feed (configurable via `feeds.yaml`) - Summarizes news using a local LLM (Ollama) - Generates a flowing, anchor-style news script - Converts the script into an MP3 audio broadcast using Microsoft Edge TTS - Saves both text and audio versions with timestamped filenames --- ## 🔧 Configurable Models You can easily modify which models or voices to use by changing these variables at the top of the script: ```python # === CONFIGURABLE MODELS === SUMMARY_MODEL = 'mistral:latest' # For summarizing articles BROADCAST_MODEL = 'mistral-small:24b-instruct-2501-q8_0' # For combining summaries into a narrative TTS_VOICE = "en-US-GuyNeural" # Microsoft Edge TTS voice ``` --- ## 📦 Installation 1. **Clone this repository:** ```bash git clone https://github.com/yourusername/news-digest-ai.git cd news-digest-ai ``` 2. **Install dependencies:** Make sure you have Python 3.8+ and install the required packages: ```bash pip install -r requirements.txt ``` 3. **Install Ollama** (if not already): - Follow setup instructions at [https://ollama.com](https://ollama.com) --- ## 📄 feeds.yaml Example Create a file called `feeds.yaml` in the project root: ```yaml feeds: - https://rss.nytimes.com/services/xml/rss/nyt/World.xml - https://feeds.bbci.co.uk/news/rss.xml ``` --- ## 🚀 Running the Script ```bash python your_script_name.py ``` The script will: - Fetch one article per feed - Summarize each - Generate a full news digest - Save the digest as `digest_YYYY-MM-DD_HH-MM-SS.md` - Save an audio file as `digest_YYYY-MM-DD_HH-MM-SS.mp3` --- ## 🗣️ Voice Options You can change the voice used for TTS by editing the `TTS_VOICE` variable. Supported voices include: - `"en-US-GuyNeural"` (default, male) - `"en-US-JennyNeural"` (female) - `"en-GB-RyanNeural"` (UK male) - `"en-IN-PrabhatNeural"` (Indian English male) See [Edge TTS Voice List](https://learn.microsoft.com/en-us/azure/ai-services/speech-service/language-support#text-to-speech) for more. --- ## 🧠 Customization Tips - Adjust `max_articles` in `fetch_articles()` to increase number of articles per feed. - Modify the `summarize_with_ollama()` and `generate_broadcast()` prompts for different tone or depth. - Swap `SUMMARY_MODEL` or `BROADCAST_MODEL` to use other Ollama-supported models like `llama3`, `gemma`, etc. --- ## 📁 Output - Markdown news summary file - Matching MP3 file with TTS narration Files are saved in the same directory by default. You can change `output_dir` in `save_digest()`. --- ## 🔒 License MIT License --- ## 🤝 Contributing PRs welcome! If you have ideas for new features (like sentiment filters, daily scheduling, or multiple language support), feel free to open an issue or submit a pull request. ``` --- Let me know if you want this turned into a `mkdocs` or `README.html` version for fancy hosting/documentation.

format this as a .md: # 📰 AI News Digest Generator This Python script automatically fetches news articles from RSS feeds, summarizes them using a local LLM (via Ollama), writes a coherent broadcast-style script, and generates a text-to-speech (TTS) audio file of the broadcast. ## ✨ Features - Pulls articles from any RSS feed (configurable via `feeds.yaml`) - Summarizes news using a local LLM (Ollama) - Generates a flowing, anchor-style news script - Converts the script into an MP3 audio broadcast using Microsoft Edge TTS - Saves both text and audio versions with timestamped filenames --- ## 🔧 Configurable Models You can easily modify which models or voices to use by changing these variables at the top of the script: ```python # === CONFIGURABLE MODELS === SUMMARY_MODEL = 'mistral:latest' # For summarizing articles BROADCAST_MODEL = 'mistral-small:24b-instruct-2501-q8_0' # For combining summaries into a narrative TTS_VOICE = "en-US-GuyNeural" # Microsoft Edge TTS voice ``` ⸻ 📦 Installation 1. Clone this repository: ```bash git clone https://github.com/kliewerdaniel/news02.git cd news02 ``` 2. Install dependencies: Make sure you have Python 3.8+ and install the required packages: ```bash pip install -r requirements.txt ``` 3. Install Ollama (if not already): • Follow setup instructions at https://ollama.com ⸻ 📄 feeds.yaml Example Create a file called feeds.yaml in the project root: ```yaml feeds: - https://rss.nytimes.com/services/xml/rss/nyt/World.xml - https://feeds.bbci.co.uk/news/rss.xml ``` ⸻ 🚀 Running the Script ```bash python your_script_name.py ``` The script will: • Fetch one article per feed • Summarize each • Generate a full news digest • Save the digest as digest_YYYY-MM-DD_HH-MM-SS.md • Save an audio file as digest_YYYY-MM-DD_HH-MM-SS.mp3 ⸻ 🗣️ Voice Options You can change the voice used for TTS by editing the TTS_VOICE variable. Supported voices include: • "en-US-GuyNeural" (default, male) • "en-US-JennyNeural" (female) • "en-GB-RyanNeural" (UK male) • "en-IN-PrabhatNeural" (Indian English male) See Edge TTS Voice List for more. ⸻ 🧠 Customization Tips • Adjust max_articles in fetch_articles() to increase number of articles per feed. • Modify the summarize_with_ollama() and generate_broadcast() prompts for different tone or depth. • Swap SUMMARY_MODEL or BROADCAST_MODEL to use other Ollama-supported models like llama3, gemma, etc. ⸻ 📁 Output • Markdown news summary file • Matching MP3 file with TTS narration Files are saved in the same directory by default. You can change output_dir in save_digest(). ⸻ 🔒 License MIT License ⸻

Here's your formatted `README.md` in proper Markdown: ```markdown # 📰 AI News Digest Generator This Python script automatically fetches news articles from RSS feeds, summarizes them using a local LLM (via Ollama), writes a coherent broadcast-style script, and generates a text-to-speech (TTS) audio file of the broadcast. --- ## ✨ Features - Pulls articles from any RSS feed (configurable via `feeds.yaml`) - Summarizes news using a local LLM (Ollama) - Generates a flowing, anchor-style news script - Converts the script into an MP3 audio broadcast using Microsoft Edge TTS - Saves both text and audio versions with timestamped filenames --- ## 🔧 Configurable Models You can easily modify which models or voices to use by changing these variables at the top of the script: ```python # === CONFIGURABLE MODELS === SUMMARY_MODEL = 'mistral:latest' # For summarizing articles BROADCAST_MODEL = 'mistral-small:24b-instruct-2501-q8_0' # For combining summaries into a narrative TTS_VOICE = "en-US-GuyNeural" # Microsoft Edge TTS voice ``` --- ## 📦 Installation 1. **Clone this repository:** ```bash git clone https://github.com/kliewerdaniel/news02.git cd news02 ``` 2. **Install dependencies:** Make sure you have Python 3.8+ and install the required packages: ```bash pip install -r requirements.txt ``` 3. **Install Ollama (if not already):** Follow setup instructions at [https://ollama.com](https://ollama.com) --- ## 📄 feeds.yaml Example Create a file called `feeds.yaml` in the project root: ```yaml feeds: - https://rss.nytimes.com/services/xml/rss/nyt/World.xml - https://feeds.bbci.co.uk/news/rss.xml ``` --- ## 🚀 Running the Script ```bash python your_script_name.py ``` The script will: - Fetch one article per feed - Summarize each - Generate a full news digest - Save the digest as `digest_YYYY-MM-DD_HH-MM-SS.md` - Save an audio file as `digest_YYYY-MM-DD_HH-MM-SS.mp3` --- ## 🗣️ Voice Options You can change the voice used for TTS by editing the `TTS_VOICE` variable. Supported voices include: - `"en-US-GuyNeural"` (default, male) - `"en-US-JennyNeural"` (female) - `"en-GB-RyanNeural"` (UK male) - `"en-IN-PrabhatNeural"` (Indian English male) See [Edge TTS Voice List](https://learn.microsoft.com/en-us/azure/ai-services/speech-service/language-support#text-to-speech) for more. --- ## 🧠 Customization Tips - Adjust `max_articles` in `fetch_articles()` to increase number of articles per feed. - Modify the `summarize_with_ollama()` and `generate_broadcast()` prompts for different tone or depth. - Swap `SUMMARY_MODEL` or `BROADCAST_MODEL` to use other Ollama-supported models like `llama3`, `gemma`, etc. --- ## 📁 Output - Markdown news summary file - Matching MP3 file with TTS narration Files are saved in the same directory by default. You can change `output_dir` in `save_digest()`. --- ## 🔒 License MIT License --- ``` Let me know if you want this version written into a `README.md` file directly or converted into HTML for a web page.

Here's your README content properly formatted as Markdown (`.md`): ```md # 📰 AI News Digest Generator This Python script automatically fetches news articles from RSS feeds, summarizes them using a local LLM (via Ollama), writes a coherent broadcast-style script, and generates a text-to-speech (TTS) audio file of the broadcast. ## ✨ Features - Pulls articles from any RSS feed (configurable via `feeds.yaml`) - Summarizes news using a local LLM (Ollama) - Generates a flowing, anchor-style news script - Converts the script into an MP3 audio broadcast using Microsoft Edge TTS - Saves both text and audio versions with timestamped filenames --- ## 🔧 Configurable Models You can easily modify which models or voices to use by changing these variables at the top of the script: ```python # === CONFIGURABLE MODELS === SUMMARY_MODEL = 'mistral:latest' # For summarizing articles BROADCAST_MODEL = 'mistral-small:24b-instruct-2501-q8_0' # For combining summaries into a narrative TTS_VOICE = "en-US-GuyNeural" # Microsoft Edge TTS voice ``` --- ## 📦 Installation 1. Clone this repository: ```bash git clone https://github.com/kliewerdaniel/news02.git cd news02 ``` 2. Install dependencies: Make sure you have Python 3.8+ and install the required packages: ```bash pip install -r requirements.txt ``` 3. Install Ollama (if not already): - Follow setup instructions at [https://ollama.com](https://ollama.com) --- ## 📄 feeds.yaml Example Create a file called `feeds.yaml` in the project root: ```yaml feeds: - https://rss.nytimes.com/services/xml/rss/nyt/World.xml - https://feeds.bbci.co.uk/news/rss.xml ``` --- ## 🚀 Running the Script ```bash python your_script_name.py ``` The script will: - Fetch one article per feed - Summarize each - Generate a full news digest - Save the digest as `digest_YYYY-MM-DD_HH-MM-SS.md` - Save an audio file as `digest_YYYY-MM-DD_HH-MM-SS.mp3` --- ## 🗣️ Voice Options You can change the voice used for TTS by editing the `TTS_VOICE` variable. Supported voices include: - `"en-US-GuyNeural"` (default, male) - `"en-US-JennyNeural"` (female) - `"en-GB-RyanNeural"` (UK male) - `"en-IN-PrabhatNeural"` (Indian English male) See [Edge TTS Voice List](https://learn.microsoft.com/en-us/azure/ai-services/speech-service/language-support#text-to-speech) for more. --- ## 🧠 Customization Tips - Adjust `max_articles` in `fetch_articles()` to increase number of articles per feed. - Modify the `summarize_with_ollama()` and `generate_broadcast()` prompts for different tone or depth. - Swap `SUMMARY_MODEL` or `BROADCAST_MODEL` to use other Ollama-supported models like `llama3`, `gemma`, etc. --- ## 📁 Output - Markdown news summary file - Matching MP3 file with TTS narration Files are saved in the same directory by default. You can change `output_dir` in `save_digest()`. --- ## 🔒 License MIT License --- ``` If you want me to generate a downloadable `.md` file or add anything else, just let me know!

```markdown # 📰 AI News Digest Generator This Python script automatically fetches news articles from RSS feeds, summarizes them using a local LLM (via Ollama), writes a coherent broadcast-style script, and generates a text-to-speech (TTS) audio file of the broadcast. ## ✨ Features - Pulls articles from any RSS feed (configurable via `feeds.yaml`) - Summarizes news using a local LLM (Ollama) - Generates a flowing, anchor-style news script - Converts the script into an MP3 audio broadcast using Microsoft Edge TTS - Saves both text and audio versions with timestamped filenames --- ## 🔧 Configurable Models You can easily modify which models or voices to use by changing these variables at the top of the script: ```python # === CONFIGURABLE MODELS === SUMMARY_MODEL = 'mistral:latest' # For summarizing articles BROADCAST_MODEL = 'mistral-small:24b-instruct-2501-q8_0' # For combining summaries into a narrative TTS_VOICE = "en-US-GuyNeural" # Microsoft Edge TTS voice # =========================== ``` --- ## 📦 Installation 1. **Clone this repository:** ```bash git clone https://github.com/kliewerdaniel/news02.git cd news02 ``` 2. **Install dependencies:** Make sure you have Python 3.8+ and install the required packages: ```bash pip install -r requirements.txt ``` 3. **Install Ollama (if not already):** Follow setup instructions at [https://ollama.com](https://ollama.com) --- ## 📄 `feeds.yaml` Example Create a file called `feeds.yaml` in the project root: ```yaml feeds: - https://rss.nytimes.com/services/xml/rss/nyt/World.xml - https://feeds.bbci.co.uk/news/rss.xml ``` --- ## 🚀 Running the Script ```bash python your_script_name.py ``` The script will: - Fetch one article per feed - Summarize each - Generate a full news digest - Save the digest as `digest_YYYY-MM-DD_HH-MM-SS.md` - Save an audio file as `digest_YYYY-MM-DD_HH-MM-SS.mp3` --- ## 🗣️ Voice Options You can change the voice used for TTS by editing the `TTS_VOICE` variable. Supported voices include: - `en-US-GuyNeural` (default, male) - `en-US-JennyNeural` (female) - `en-GB-RyanNeural` (UK male) - `en-IN-PrabhatNeural` (Indian English male) See [Edge TTS Voice List](https://learn.microsoft.com/en-us/azure/ai-services/speech-service/language-support#text-to-speech) for more. --- ## 🧠 Customization Tips - Adjust `max_articles` in `fetch_articles()` to increase the number of articles per feed. - Modify the `summarize_with_ollama()` and `generate_broadcast()` prompts for a different tone or depth. - Swap `SUMMARY_MODEL` or `BROADCAST_MODEL` to use other Ollama-supported models like `llama3`, `gemma`, etc. --- ## 📁 Output - Markdown news summary file - Matching MP3 file with TTS narration Files are saved in the same directory by default. You can change `output_dir` in `save_digest()`. --- ## 🔒 License MIT License ```