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| import gradio as gr | |
| from langchain.chains import LLMChain | |
| from langchain.prompts import PromptTemplate | |
| from langchain.document_loaders import WebBaseLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.llms import HuggingFacePipeline | |
| from transformers import pipeline | |
| import tempfile | |
| import os | |
| from bs4 import BeautifulSoup | |
| import requests | |
| import pyttsx3 | |
| # CPU-friendly summarization LLM | |
| summary_pipe = pipeline("text2text-generation", model="google/flan-t5-base", device=-1) | |
| llm = HuggingFacePipeline(pipeline=summary_pipe) | |
| # Summarization prompt | |
| summary_prompt = PromptTemplate.from_template(""" | |
| Summarize the following article content in a clear, concise, and emotionally engaging manner as if you're speaking to a curious listener: | |
| {text} | |
| Summary: | |
| """) | |
| summary_chain = LLMChain(llm=llm, prompt=summary_prompt) | |
| 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: | |
| engine = pyttsx3.init() | |
| engine.setProperty('rate', 150) # slower pace | |
| engine.setProperty('volume', 1.0) | |
| voices = engine.getProperty('voices') | |
| # Choose a more natural voice if available (optional: pick female) | |
| for voice in voices: | |
| if 'female' in voice.name.lower(): | |
| engine.setProperty('voice', voice.id) | |
| break | |
| temp_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") | |
| engine.save_to_file(text, temp_path.name) | |
| engine.runAndWait() | |
| return temp_path.name | |
| except Exception as e: | |
| return None | |
| def url_to_audio_summary(url): | |
| try: | |
| article_text = extract_main_content(url) | |
| if article_text.startswith("Error"): | |
| return article_text, None | |
| summary = summary_chain.run(text=article_text) | |
| audio_path = generate_human_like_audio(summary) | |
| if not audio_path: | |
| return summary, None | |
| return summary, audio_path | |
| except Exception as e: | |
| return f"Error: {str(e)}", None | |
| iface = gr.Interface( | |
| fn=url_to_audio_summary, | |
| inputs=gr.Textbox(label="Article URL", placeholder="Paste a news/blog URL here..."), | |
| outputs=[ | |
| gr.Textbox(label="Summary"), | |
| gr.Audio(label="Audio Summary") | |
| ], | |
| title="URL to Audio Summary Agent", | |
| description="Summarizes only the article content from a URL and gives a more human-like audio summary using pyttsx3. CPU-only." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |