app.py

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
from gtts import gTTS
import tempfile
import os

# 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 webpage content in a clear, concise way:

{text}

Summary:
""")

summary_chain = LLMChain(llm=llm, prompt=summary_prompt)

def url_to_audio_summary(url):
    try:
        loader = WebBaseLoader(url)
        docs = loader.load()
        splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=100)
        splits = splitter.split_documents(docs)

        full_text = "\n".join([s.page_content for s in splits])
        summary = summary_chain.run(text=full_text)

        # Use gTTS for TTS since Hugging Face TTS model failed
        tts = gTTS(text=summary)
        temp_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
        tts.save(temp_path.name)

        return summary, temp_path.name

    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 article from a URL and gives an audio summary. CPU-only using gTTS."
)

if __name__ == "__main__":
    iface.launch()