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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()
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