import gradio as gr
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.llms import HuggingFacePipeline
from transformers import pipeline
from gtts import gTTS
from bs4 import BeautifulSoup
import tempfile
import os
import requests
from moviepy.editor import *

# CPU-friendly summarization model
summary_pipe = pipeline("text2text-generation", model="google/flan-t5-base", device=-1)
llm = HuggingFacePipeline(pipeline=summary_pipe)

# LangChain summarization prompt
summary_prompt = PromptTemplate.from_template("""
Summarize the following article content in a clear, concise way:

{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 url_to_av_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)

        # Generate speech using gTTS
        tts = gTTS(text=summary)
        audio_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3").name
        tts.save(audio_path)

        # Create video clip (text overlay + audio)
        video_clip = TextClip(summary, fontsize=32, color='white', bg_color='black', size=(1280, 720), method='caption')
        video_clip = video_clip.set_duration(AudioFileClip(audio_path).duration)
        video_clip = video_clip.set_audio(AudioFileClip(audio_path))

        video_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
        video_clip.write_videofile(video_path, fps=24, codec='libx264')

        return summary, video_path

    except Exception as e:
        return f"Error: {str(e)}", None

iface = gr.Interface(
    fn=url_to_av_summary,
    inputs=gr.Textbox(label="Article URL", placeholder="Paste a news/blog URL here..."),
    outputs=[
        gr.Textbox(label="Summary"),
        gr.Video(label="Video Summary")
    ],
    title="URL to AV Summary Agent",
    description="Summarizes only article content from a URL and creates a narrated video. CPU-only."
)

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