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| import gradio as gr | |
| from langchain.prompts import PromptTemplate | |
| from langchain_community.llms import HuggingFacePipeline # Updated import path | |
| from transformers import pipeline | |
| from bs4 import BeautifulSoup | |
| import requests | |
| from TTS.api import TTS | |
| import tempfile | |
| import os | |
| # Setup summarization LLM | |
| summary_pipe = pipeline("text2text-generation", model="google/flan-t5-base", device=-1) | |
| llm = HuggingFacePipeline(pipeline=summary_pipe) | |
| # Prompt for more engaging summary | |
| summary_prompt = PromptTemplate.from_template(""" | |
| Summarize the following article content in a clear, warm, and motivational tone like a preacher speaking to an audience: | |
| {text} | |
| Summary: | |
| """) | |
| # Updated chaining method | |
| summary_chain = summary_prompt | llm | |
| # TTS model setup | |
| tts_model = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=False) | |
| def extract_main_content(url): | |
| try: | |
| response = requests.get(url, timeout=10) | |
| soup = BeautifulSoup(response.content, "html.parser") | |
| for tag in soup(["nav", "header", "footer", "aside", "script", "style", "noscript"]): | |
| tag.decompose() | |
| paragraphs = soup.find_all("p") | |
| content = "\n".join([p.get_text() for p in paragraphs if len(p.get_text()) > 60]) | |
| return content.strip() | |
| except Exception as e: | |
| return f"Error extracting article content: {str(e)}" | |
| def generate_human_like_audio(text): | |
| try: | |
| temp_dir = tempfile.mkdtemp() | |
| wav_path = os.path.join(temp_dir, "summary.wav") | |
| mp3_path = os.path.join(temp_dir, "summary.mp3") | |
| tts_model.tts_to_file(text=text, file_path=wav_path) | |
| os.system(f"ffmpeg -y -i {wav_path} -codec:a libmp3lame -qscale:a 4 {mp3_path}") | |
| if os.path.exists(mp3_path): | |
| return wav_path, mp3_path | |
| else: | |
| return wav_path, None | |
| except Exception as e: | |
| print(f"TTS ERROR: {e}") | |
| return None, None | |
| def url_to_audio_summary(url): | |
| try: | |
| article_text = extract_main_content(url) | |
| if article_text.startswith("Error"): | |
| return article_text, None, None | |
| if len(article_text) > 1500: | |
| article_text = article_text[:1500] + "..." | |
| summary = summary_chain.invoke({"text": article_text}) | |
| summary = summary["text"] if isinstance(summary, dict) and "text" in summary else summary | |
| wav_path, mp3_path = generate_human_like_audio(summary) | |
| return summary, wav_path, mp3_path | |
| except Exception as e: | |
| return f"Error: {str(e)}", None, None | |
| def interface_wrapper(url): | |
| summary, wav_path, mp3_path = url_to_audio_summary(url) | |
| download_html = "" | |
| if mp3_path and os.path.exists(mp3_path): | |
| download_html = f'<a href="file/{os.path.basename(mp3_path)}" download target="_blank">Click to download MP3</a>' | |
| return summary, wav_path, download_html | |
| iface = gr.Interface( | |
| fn=interface_wrapper, | |
| inputs=gr.Textbox(label="Article URL", placeholder="Paste a news/blog URL here..."), | |
| outputs=[ | |
| gr.Textbox(label="Summary"), | |
| gr.Audio(label="Preacher-style Audio Summary", type="filepath"), | |
| gr.HTML(label="Download MP3") | |
| ], | |
| title="Preaching-Style URL to Audio Agent", | |
| description="Summarizes article content and reads it aloud in a warm, preacher-style voice using YourTTS. CPU-only." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |