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#!/usr/bin/env python3
"""
Application principale pour Hugging Face Space
"""
import gradio as gr
import os
import tempfile
from pathlib import Path
import sys
# Ajouter le dossier courant au path
sys.path.append(os.path.dirname(__file__))
# Importer les modules nécessaires
try:
from transcribe_audio import transcribe_file, load_whisper_model, get_audio_files
from analyze_bob_hf import analyze_files_hf
print("✅ Modules importés avec succès")
except Exception as e:
print(f"❌ Erreur d'import: {e}")
def process_audio_file(audio_file):
"""Traite un fichier audio et retourne les résultats"""
try:
# Créer des dossiers temporaires
with tempfile.TemporaryDirectory() as temp_dir:
temp_path = Path(temp_dir)
input_dir = temp_path / "input"
output_dir = temp_path / "output"
transcriptions_dir = output_dir / "transcriptions"
input_dir.mkdir(parents=True, exist_ok=True)
output_dir.mkdir(parents=True, exist_ok=True)
transcriptions_dir.mkdir(parents=True, exist_ok=True)
# Copier le fichier audio
audio_path = input_dir / os.path.basename(audio_file)
import shutil
shutil.copy2(audio_file, audio_path)
# Configurer les variables d'environnement
os.environ["BOB_INPUT_DIR"] = str(input_dir)
os.environ["BOB_TRANSCRIPTIONS_DIR"] = str(transcriptions_dir)
os.environ["BOB_OUTPUT_FILE"] = str(output_dir / "resume_bob.txt")
os.environ["WHISPER_MODEL"] = "small" # Modèle léger pour le Space
# Transcription
print("🔄 Transcription en cours...")
model = load_whisper_model("small")
success = transcribe_file(model, audio_path, transcriptions_dir)
if not success:
return "❌ Erreur de transcription", ""
# Analyse
print("🤖 Analyse en cours...")
result = analyze_files_hf(
transcriptions_dir=transcriptions_dir,
input_dir=input_dir,
output_file=output_dir / "resume_bob.txt",
log_fn=print
)
# Lire les résultats
resume_path = output_dir / "resume_bob.txt"
if resume_path.exists():
with open(resume_path, 'r', encoding='utf-8') as f:
resume_content = f.read()
else:
resume_content = "❌ Fichier de résultat non trouvé"
# Lire la transcription
transcription_files = list(transcriptions_dir.glob("*.txt"))
if transcription_files:
with open(transcription_files[0], 'r', encoding='utf-8') as f:
transcription_content = f.read()
else:
transcription_content = "❌ Transcription non trouvée"
return transcription_content, resume_content
except Exception as e:
return f"❌ Erreur: {str(e)}", f"❌ Erreur: {str(e)}"
# Interface Gradio
with gr.Blocks(title="BOB Processor") as demo:
gr.Markdown("# 🎵 BOB Processor")
gr.Markdown("### Transcription et analyse automatique de fichiers audio")
with gr.Row():
with gr.Column():
audio_input = gr.Audio(
label="🎤 Fichier audio à traiter",
type="filepath"
)
process_btn = gr.Button("▶️ Traiter le fichier")
with gr.Column():
transcription_output = gr.Textbox(
label="📝 Transcription",
lines=10
)
resume_output = gr.Textbox(
label="📋 Résumé analysé",
lines=10
)
process_btn.click(
process_audio_file,
inputs=[audio_input],
outputs=[transcription_output, resume_output]
)
if __name__ == "__main__":
demo.launch()
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