Update app.py

app.py

@@ -4,57 +4,7 @@ from fastapi.staticfiles import StaticFiles
 from fastapi.middleware.cors import CORSMiddleware
 from transformers import pipeline, M2M100ForConditionalGeneration, M2M100Tokenizer, MarianMTModel, MarianTokenizer
 import shutil
-#
-import os
-import logging
-from PyPDF2 import PdfReader
-import docx
-from PIL import Image
-import openpyxl  # 📌 Pour lire les fichiers Excel (.xlsx)
-from pptx import Presentation
-import fitz  # PyMuPDF
-import io
-from docx import Document
-import matplotlib.pyplot as plt
-import seaborn as sns
-import torch
-import re
-import pandas as pd
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from fastapi.responses import FileResponse
-import os
-from fastapi.middleware.cors import CORSMiddleware
-import matplotlib
-matplotlib.use('Agg')
 
-import re
-import torch
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from fastapi import FastAPI, File, UploadFile, Form
-from fastapi.responses import FileResponse
-import os
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi import FastAPI, File, UploadFile, Form
-from fastapi.responses import JSONResponse, RedirectResponse
-from fastapi.staticfiles import StaticFiles
-from transformers import pipeline, M2M100ForConditionalGeneration, M2M100Tokenizer
-import shutil
-import os
-import logging
-from fastapi.middleware.cors import CORSMiddleware
-from PyPDF2 import PdfReader
-import docx
-from PIL import Image  # Pour ouvrir les images avant analyse
-from transformers import MarianMTModel, MarianTokenizer
-import os
-import fitz 
-from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
-
-import logging
-import openpyxl
 
 
 # Configuration du logging
@@ -62,96 +12,6 @@ logging.basicConfig(level=logging.INFO)
 
 app = FastAPI()
 
-# Configuration CORS
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-UPLOAD_DIR = "uploads"
-os.makedirs(UPLOAD_DIR, exist_ok=True)
-
-#traduction-----------------------------------------------------------------------------------------------------------
-
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-model_name = "facebook/m2m100_418M"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-
-# Fonction pour extraire le texte
-def extract_text_from_pdf(file):
-    doc = fitz.open(stream=file.file.read(), filetype="pdf")
-    return "\n".join([page.get_text() for page in doc]).strip()
-
-def extract_text_from_docx(file):
-    doc = Document(io.BytesIO(file.file.read()))
-    return "\n".join([para.text for para in doc.paragraphs]).strip()
-
-def extract_text_from_pptx(file):
-    prs = Presentation(io.BytesIO(file.file.read()))
-    return "\n".join([shape.text for slide in prs.slides for shape in slide.shapes if hasattr(shape, "text")]).strip()
-
-def extract_text_from_excel(file):
-    wb = openpyxl.load_workbook(io.BytesIO(file.file.read()), data_only=True)
-    text = [str(cell) for sheet in wb.worksheets for row in sheet.iter_rows(values_only=True) for cell in row if cell]
-    return "\n".join(text).strip()
-
-@app.post("/translate/")
-async def translate_document(file: UploadFile = File(...), target_lang: str = Form(...)):
-    """API pour traduire un document."""
-    try:
-        logging.info(f"📥 Fichier reçu : {file.filename}")
-        logging.info(f"🌍 Langue cible reçue : {target_lang}")
-
-        if model is None or tokenizer is None:
-            return JSONResponse(status_code=500, content={"error": "Modèle de traduction non chargé"})
-
-        # Extraction du texte
-        if file.filename.endswith(".pdf"):
-            text = extract_text_from_pdf(file)
-        elif file.filename.endswith(".docx"):
-            text = extract_text_from_docx(file)
-        elif file.filename.endswith(".pptx"):
-            text = extract_text_from_pptx(file)
-        elif file.filename.endswith(".xlsx"):
-            text = extract_text_from_excel(file)
-        else:
-            return JSONResponse(status_code=400, content={"error": "Format non supporté"})
-
-        logging.info(f"📜 Texte extrait : {text[:50]}...")
-
-        if not text:
-            return JSONResponse(status_code=400, content={"error": "Aucun texte trouvé dans le document"})
-
-        # Vérifier si la langue cible est supportée
-        target_lang_id = tokenizer.get_lang_id(target_lang)
-
-        if target_lang_id is None:
-            return JSONResponse(
-                status_code=400,
-                content={"error": f"Langue cible '{target_lang}' non supportée. Langues disponibles : {list(tokenizer.lang_code_to_id.keys())}"}
-            )
-
-        # Traduction
-        tokenizer.src_lang = "fr"
-        encoded_text = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
-
-        logging.info(f"🔍 ID de la langue cible : {target_lang_id}")
-
-        generated_tokens = model.generate(**encoded_text, forced_bos_token_id=target_lang_id)
-
-        translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
-
-        logging.info(f"✅ Traduction réussie : {translated_text[:50]}...")
-        return {"translated_text": translated_text}
-
-    except Exception as e:
-        logging.error(f"❌ Erreur lors de la traduction : {e}")
-        return JSONResponse(status_code=500, content={"error": "Échec de la traduction"})
 
 # Servir les fichiers statiques (HTML, CSS, JS)
 app.mount("/static", StaticFiles(directory="static", html=True), name="static")