app.py

import gradio as gr
import datetime
import Implementation as imp
import time  
import os

# ========== Fonctions Backend ==========

def detect_hate_speech(video_path, mode):
    if mode == "Low 🌱":
        Co2_release = "low"
    elif mode == "Medium ♨️":
        Co2_release = "medium"
    else:
        Co2_release = "high"

    hate_speech_time, C02_emissions = imp.detectHateSpeechSmartFilter(video_path, Co2_release)
    return hate_speech_time, C02_emissions

def detect_hate_speech_audio(audio_path, mode):
    if mode == "Low 🌱":
        Co2_release = "low"
    elif mode == "Medium ♨️":
        Co2_release = "medium"
    else:
        Co2_release = "high"

    hate_speech_time, C02_emissions = imp.Detect_hate_speech_emo_hate_bert(audio_path, Co2_release)
    return hate_speech_time, C02_emissions

def convertir_timestamp_en_secondes(timestamp):
    h, m, s = map(int, timestamp.split(":"))
    return h * 3600 + m * 60 + s

def analyser_media(fichier, mode, is_audio=False):
    if not fichier:
        return "<p style='color:red;'>❌ No files uploaded. Please add video or audio.</p>", ""

    if is_audio:
        timestamps, carbone = detect_hate_speech_audio(fichier, mode)
    else:
        timestamps, carbone = detect_hate_speech(fichier, mode)

    liens = "<div style='line-height: 2; font-size: 16px;'>"
    total_seconds = 0
    for idx, (start_time, end_time) in enumerate(timestamps, 1):
        secondes = convertir_timestamp_en_secondes(start_time)
        liens += f'<button style="margin:5px; padding:8px 12px; border:none; border-radius:8px; background:#2d2d2d; color:white; cursor:pointer;" onclick="var player=document.getElementById(\'video-player\').querySelector(\'video, audio\'); if(player){{player.currentTime={secondes}; player.play();}}">🕑 Segment {idx} : {start_time} ➔ {end_time}</button><br>'
        duree_segment = convertir_timestamp_en_secondes(end_time) - secondes
        total_seconds += duree_segment
    liens += "</div>"

    nb_segments = len(timestamps)
    duree_totale = str(datetime.timedelta(seconds=total_seconds))

    resume = f"<div style='margin-top:20px; font-size:18px;'>🧮 <b>Segments detected</b>: {nb_segments}<br>⏳ <b>Total Hate Speech Duration</b>: {duree_totale} <br>♻️ <b>Carbon Footprint</b>: {carbone}</div>"

    return liens, resume

def afficher_pipeline(show):
    return gr.update(visible=show)

def show_loader():
    return gr.update(visible=True), "", ""

def analyser_avec_loading(video, mode):
    liens, resume = analyser_media(video, mode, is_audio=False)
    return gr.update(visible=False), liens, resume

def analyser_audio_avec_loading(audio, mode):
    liens, resume = analyser_media(audio, mode, is_audio=True)
    return gr.update(visible=False), liens, resume

# ========== Interface Gradio ==========

with gr.Blocks(theme=gr.themes.Monochrome(), css="body {background-color: #121212; color: white;}") as demo:

    # En-tête
    gr.HTML("""
    <div style='text-align: center; margin-bottom: 20px;'>
        <h1 style='color: #00BFFF;'>🎓 EPFL Project – Emotion-Aware and Eco-Conscious Hate Speech Detection in Video and Audio</h1>
        <h3 style="color: white;"> This project provides an intelligent and environmentally conscious platform for detecting hate speech in videos and audio. It combines the latest tools in NLP, emotion analysis and computer vision, with CO₂ tracking, to offer both performance and eco-responsibility.</h3>
        <h3 style='color: #AAAAAA;'>Participants: Loris Alan Fabbro, Mohammed Al-Hussini, Loic Misenta</h3>
        <a href='https://github.com/loris-fab/Deep_learning/blob/main/README.md'
       target='_blank' style='color: orange; font-weight:bold;'>🔗 GitHub</a>
    </div>
    """)

    gr.Image("logo.png", width=150, show_label=False, show_download_button=False)

    # Affichage du pipeline
    with gr.Row():
        show_pipeline = gr.Checkbox(label="👀 Show Pipeline Overview", value=False)

    pipeline_image = gr.Image(
        value="pipeline.png",
        label="Pipeline Overview",
        show_label=True,
        visible=False
    )

    show_pipeline.change(
        afficher_pipeline,
        inputs=[show_pipeline],
        outputs=[pipeline_image]
    )

    gr.Markdown("# 🎥 Hate Speech Detector in Your Videos or Audio", elem_id="titre")

    with gr.Row():
        video_input = gr.Video(label="Upload your video", elem_id="video-player")
    with gr.Row():
        audio_input = gr.Audio(label="Upload your audio", type="filepath")
    with gr.Row():
        mode_selection = gr.Radio(["Low 🌱", "Medium ♨️", "High Consumption ⚠️"], label="Carbon Footprint Mode")

    bouton_analyse_video = gr.Button("Detect Hate Speech in Video 🔥")
    bouton_analyse_audio = gr.Button("Detect Hate Speech in Audio 🎧")

    with gr.Column() as resultats:
        loading_gif = gr.Image(
            value="loading.gif",
            visible=False,
            show_label=False
        )
        liens_resultats = gr.HTML()
        resume_resultats = gr.HTML()

    bouton_analyse_video.click(
        fn=show_loader,
        inputs=[],
        outputs=[loading_gif, liens_resultats, resume_resultats],
        show_progress=False
    )

    bouton_analyse_video.click(
        fn=analyser_avec_loading,
        inputs=[video_input, mode_selection],
        outputs=[loading_gif, liens_resultats, resume_resultats],
        show_progress=True
    )

    bouton_analyse_audio.click(
        fn=show_loader,
        inputs=[],
        outputs=[loading_gif, liens_resultats, resume_resultats],
        show_progress=False
    )

    bouton_analyse_audio.click(
        fn=analyser_audio_avec_loading,
        inputs=[audio_input, mode_selection],
        outputs=[loading_gif, liens_resultats, resume_resultats],
        show_progress=True
    )


demo.launch(share=True)