import whisper
import openai
import gradio as gr
from gtts import gTTS
from moviepy.editor import VideoFileClip
import os

openai.api_key = "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"

def transcribe_video(video_path):
    # Extract audio from video file
    video = VideoFileClip(video_path)
    audio_path = "temp_audio.wav"
    video.audio.write_audiofile(audio_path, codec='pcm_s16le')

    # Load Whisper model and transcribe audio
    model = whisper.load_model("base")
    result = model.transcribe(audio_path)
    transcription = result["text"]
    
    # Remove temporary audio file
    os.remove(audio_path)
    return transcription

def summarize_text(text):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=f"Summarize the following text:\n\n{text}",
        max_tokens=150
    )
    summary = response.choices[0].text.strip()
    return summary

def text_to_speech(text, language="en"):
    tts = gTTS(text=text, lang=language)
    tts.save("summary_audio.mp3")
    return "summary_audio.mp3"

def process_video(video):
    # Transcribe the video
    transcription = transcribe_video(video)
    
    # Summarize the transcription
    summary = summarize_text(transcription)
    
    # Convert summary to speech
    audio_file = text_to_speech(summary)
    
    return transcription, summary, audio_file

# Create Gradio interface
iface = gr.Interface(
    fn=process_video,
    inputs=gr.Video(label="Upload Video"),
    outputs=[
        gr.Textbox(label="Transcription"),
        gr.Textbox(label="Summary"),
        gr.Audio(label="Summary Audio")
    ],
    title="Video Transcription and Summarization",
    description="Upload a video file to transcribe and summarize its content."
)

# Launch the interface
iface.launch()