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600 lines (523 loc) · 20 KB
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"""
Flask web application for TransDocs - Document Translation and Proofreading Tool.
"""
import os
import logging
import secrets
import threading
import time
import uuid
from urllib.parse import urlparse
import requests as http_requests
from flask import (
Flask,
abort,
jsonify,
redirect,
render_template,
request,
send_from_directory,
url_for,
)
from werkzeug.utils import secure_filename
# Import from the src package
from src.transdoc import process_document
logger = logging.getLogger(__name__)
def _int_env(name, default):
value = os.getenv(name)
if value is None:
return default
try:
return int(value)
except ValueError:
logger.warning("Invalid integer for %s=%r, using %s", name, value, default)
return default
def _is_debug_mode():
return os.getenv("FLASK_DEBUG", "").lower() in {"1", "true", "yes", "on"}
def _allowed_api_hosts():
configured = os.getenv("TRANSDOC_ALLOWED_API_HOSTS", "")
if configured.strip():
return {host.strip().lower() for host in configured.split(",") if host.strip()}
return {"localhost", "127.0.0.1", "::1", "host.docker.internal"}
def validate_api_url(api_url):
parsed = urlparse(api_url.strip())
if parsed.scheme not in {"http", "https"} or not parsed.hostname:
raise ValueError("API URL must be a valid http(s) URL")
if parsed.hostname.lower() not in _allowed_api_hosts():
raise ValueError(
"API host is not allowed. Set TRANSDOC_ALLOWED_API_HOSTS to permit it."
)
return api_url.strip()
app = Flask(__name__, template_folder="templates") # Templates in templates/ subfolder
app.secret_key = os.getenv("FLASK_SECRET_KEY")
if not app.secret_key:
if _is_debug_mode():
app.secret_key = "dev-secret-key"
else:
app.secret_key = secrets.token_hex(32)
logger.warning(
"FLASK_SECRET_KEY is not set; using an ephemeral secret key for this process."
)
# Configure upload folder and allowed extensions
UPLOAD_FOLDER = os.path.abspath("uploads")
OUTPUT_FOLDER = os.path.abspath("outputs")
ALLOWED_EXTENSIONS = {"docx", "pdf"}
MAX_CONTENT_LENGTH = _int_env("TRANSDOC_MAX_UPLOAD_MB", 16) * 1024 * 1024
JOB_RETENTION_SECONDS = _int_env("TRANSDOC_JOB_RETENTION_SECONDS", 3600)
MAX_JOB_RECORDS = _int_env("TRANSDOC_MAX_JOB_RECORDS", 500)
app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER
app.config["OUTPUT_FOLDER"] = OUTPUT_FOLDER
app.config["MAX_CONTENT_LENGTH"] = MAX_CONTENT_LENGTH
# Ensure the upload and output directories exist
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
os.makedirs(OUTPUT_FOLDER, exist_ok=True)
TRANSLATION_JOBS = {}
TRANSLATION_JOBS_LOCK = threading.Lock()
def allowed_file(filename):
return "." in filename and filename.rsplit(".", 1)[1].lower() in ALLOWED_EXTENSIONS
def cleanup_jobs(now=None):
now = now or time.time()
with TRANSLATION_JOBS_LOCK:
expired_job_ids = [
job_id
for job_id, job in TRANSLATION_JOBS.items()
if job.get("status") in {"done", "error"} and (now - job.get("updated_at", now) > JOB_RETENTION_SECONDS)
]
for job_id in expired_job_ids:
TRANSLATION_JOBS.pop(job_id, None)
if len(TRANSLATION_JOBS) <= MAX_JOB_RECORDS:
return
removable_jobs = sorted(
(
(job.get("updated_at", 0), job_id)
for job_id, job in TRANSLATION_JOBS.items()
if job.get("status") in {"done", "error"}
)
)
while len(TRANSLATION_JOBS) > MAX_JOB_RECORDS and removable_jobs:
_, job_id = removable_jobs.pop(0)
TRANSLATION_JOBS.pop(job_id, None)
def create_job_state(job_id, filename):
cleanup_jobs()
now = time.time()
with TRANSLATION_JOBS_LOCK:
TRANSLATION_JOBS[job_id] = {
"id": job_id,
"status": "queued",
"message": "Queued",
"error": None,
"completed": 0,
"total": 0,
"percent": 0,
"output_filename": filename,
"created_at": now,
"updated_at": now,
}
def update_job_state(job_id, **updates):
cleanup_jobs()
with TRANSLATION_JOBS_LOCK:
if job_id in TRANSLATION_JOBS:
updates.setdefault("updated_at", time.time())
if updates.get("status") in {"done", "error"}:
updates.setdefault("finished_at", updates["updated_at"])
TRANSLATION_JOBS[job_id].update(updates)
def get_job_state(job_id):
cleanup_jobs()
with TRANSLATION_JOBS_LOCK:
job = TRANSLATION_JOBS.get(job_id)
return dict(job) if job else None
def run_translation_job(
job_id,
input_filepath,
output_filepath,
output_filename,
model,
target_lang,
api_token,
src_lang,
api_url,
backend,
):
try:
update_job_state(job_id, status="running", message="Initializing translation")
def progress_callback(completed, total, message):
percent = int((completed / total) * 100) if total else 0
update_job_state(
job_id,
completed=completed,
total=total,
percent=percent,
message=message or f"Processing {completed}/{total}",
status="running",
)
process_document(
input_filepath,
output_filepath,
model,
target_lang,
api_token,
src_lang,
api_url=api_url,
backend=backend,
progress_callback=progress_callback,
)
if not os.path.exists(output_filepath):
raise RuntimeError("Translation completed but output file was not created")
update_job_state(
job_id,
status="done",
message="Translation completed",
completed=max(get_job_state(job_id).get("completed", 0), 1),
total=max(get_job_state(job_id).get("total", 1), 1),
percent=100,
output_filename=output_filename,
)
except Exception as exc:
update_job_state(
job_id,
status="error",
error=str(exc),
message="Translation failed",
)
def build_models_endpoints(api_url, backend):
"""Build ordered model-list endpoint candidates for the selected backend."""
base_url = api_url.rstrip("/")
endpoints = []
def add(url):
if url not in endpoints:
endpoints.append(url)
if backend == "openai_compatible":
if base_url.endswith(("/v1/models", "/models")):
add(base_url)
elif base_url.endswith("/v1"):
add(f"{base_url}/models")
else:
add(f"{base_url}/v1/models")
# Fallback for Ollama-style gateways
if base_url.endswith("/api/tags"):
add(base_url)
else:
add(f"{base_url}/api/tags")
return endpoints
# Default: ollama
if base_url.endswith("/api/tags"):
add(base_url)
else:
add(f"{base_url}/api/tags")
# Fallback for OpenAI-compatible gateways
if base_url.endswith(("/v1/models", "/models")):
add(base_url)
elif base_url.endswith("/v1"):
add(f"{base_url}/models")
else:
add(f"{base_url}/v1/models")
return endpoints
def extract_model_names(data):
"""Extract model names from either Ollama or OpenAI-compatible responses."""
items = data.get("data", [])
openai_models = [m.get("id") for m in items if isinstance(m, dict) and m.get("id")]
if openai_models:
return openai_models
items = data.get("models", [])
return [m.get("name") for m in items if isinstance(m, dict) and m.get("name")]
@app.errorhandler(413)
def request_entity_too_large(_error):
message = f"Uploaded file is too large. Maximum size is {MAX_CONTENT_LENGTH // (1024 * 1024)} MB."
if request.path == "/start_translation":
return jsonify({"success": False, "error": message}), 413
return (
render_template(
"upload.html",
error=message,
selected_backend=request.form.get("backend", "ollama").strip() or "ollama",
selected_api_url=request.form.get(
"api_url", "http://localhost:11434"
).strip(),
),
413,
)
@app.route("/query_ollama", methods=["POST"]) # backward-compatible endpoint name
@app.route("/query_models", methods=["POST"])
def query_models():
"""API endpoint to test backend connection and get available models."""
api_url = request.form.get("api_url", "http://localhost:11434").strip()
backend = request.form.get("backend", "ollama").strip() or "ollama"
api_token = request.form.get("api_token", "").strip() or None
if backend not in {"ollama", "openai_compatible"}:
backend = "ollama"
try:
api_url = validate_api_url(api_url)
except ValueError as exc:
return jsonify({"success": False, "error": str(exc)}), 400
models_urls = build_models_endpoints(api_url, backend)
headers = {}
if api_token:
headers["Authorization"] = f"Bearer {api_token}"
headers["X-API-Key"] = api_token
headers["api-key"] = api_token
try:
last_status = None
last_text = ""
for models_url in models_urls:
response = http_requests.get(models_url, headers=headers, timeout=8)
if response.status_code == 200:
data = response.json()
models = extract_model_names(data)
return jsonify({"success": True, "models": models})
last_status = response.status_code
last_text = response.text[:500]
return (
jsonify(
{
"success": False,
"error": f"HTTP {last_status}",
"details": last_text,
}
),
400,
)
except Exception as e:
return jsonify({"success": False, "error": str(e)}), 400
@app.route("/start_translation", methods=["POST"])
def start_translation():
file = request.files.get("input_file")
target_lang = request.form.get("target_lang", "").strip()
src_lang = request.form.get("src_lang", "").strip() or None
api_token = request.form.get("api_token", "").strip() or None
model = request.form.get("model", "").strip()
backend = request.form.get("backend", "ollama").strip() or "ollama"
api_url = request.form.get("api_url", "http://localhost:11434").strip()
if backend not in {"ollama", "openai_compatible"}:
backend = "ollama"
try:
api_url = validate_api_url(api_url)
except ValueError as exc:
return jsonify({"success": False, "error": str(exc)}), 400
if not model:
return jsonify({"success": False, "error": "Please select a valid model"}), 400
if not target_lang:
return jsonify({"success": False, "error": "Target language is required"}), 400
if not file or not allowed_file(file.filename):
return (
jsonify(
{"success": False, "error": "Please upload a valid .docx or .pdf file"}
),
400,
)
filename = secure_filename(file.filename)
job_id = uuid.uuid4().hex
input_filename = f"{job_id}_{filename}"
input_base_name = os.path.splitext(filename)[0]
input_extension = os.path.splitext(filename)[1].lower()
output_extension = ".pdf" if input_extension == ".pdf" else ".docx"
output_filename = f"translated_{job_id}_{input_base_name}{output_extension}"
input_filepath = os.path.join(app.config["UPLOAD_FOLDER"], input_filename)
output_filepath = os.path.join(app.config["OUTPUT_FOLDER"], output_filename)
file.save(input_filepath)
create_job_state(job_id, output_filename)
worker = threading.Thread(
target=run_translation_job,
args=(
job_id,
input_filepath,
output_filepath,
output_filename,
model,
target_lang,
api_token,
src_lang,
api_url,
backend,
),
daemon=True,
)
worker.start()
return jsonify({"success": True, "job_id": job_id})
@app.route("/translation_status/<job_id>", methods=["GET"])
def translation_status(job_id):
job = get_job_state(job_id)
if not job:
return jsonify({"success": False, "error": "Job not found"}), 404
response = {
"success": True,
"status": job["status"],
"message": job.get("message", ""),
"error": job.get("error"),
"completed": job.get("completed", 0),
"total": job.get("total", 0),
"percent": job.get("percent", 0),
}
if job["status"] == "done":
response["download_url"] = url_for(
"download_file", filename=job["output_filename"]
)
return jsonify(response)
@app.route("/", methods=["GET", "POST"])
def upload_file():
if request.method == "POST":
# Check if this is a test connection action (for backward compatibility)
if request.form.get("action") == "test":
api_url = request.form.get("api_url", "http://localhost:11434").strip()
backend = request.form.get("backend", "ollama").strip() or "ollama"
api_token = request.form.get("api_token", "").strip() or None
if backend not in {"ollama", "openai_compatible"}:
backend = "ollama"
try:
api_url = validate_api_url(api_url)
except ValueError as exc:
return render_template(
"upload.html",
connection_result=f"✗ Connection failed!<br>Error: {str(exc)}",
connection_success=False,
selected_backend=backend,
selected_api_url=api_url,
)
models_urls = build_models_endpoints(api_url, backend)
headers = {}
if api_token:
headers["Authorization"] = f"Bearer {api_token}"
headers["X-API-Key"] = api_token
headers["api-key"] = api_token
try:
model_names = []
last_status = None
for models_url in models_urls:
response = http_requests.get(models_url, headers=headers, timeout=8)
if response.status_code == 200:
data = response.json()
model_names = extract_model_names(data)
break
last_status = response.status_code
if model_names:
result = "✓ Connected!"
return render_template(
"upload.html",
connection_result=result,
connection_success=True,
selected_backend=backend,
selected_api_url=api_url,
)
if last_status:
result = f"✗ Connection failed! HTTP {last_status}"
return render_template(
"upload.html",
connection_result=result,
connection_success=False,
selected_backend=backend,
selected_api_url=api_url,
)
result = "✓ Connected! No models loaded. Load a model first."
return render_template(
"upload.html",
connection_result=result,
connection_success=True,
selected_backend=backend,
selected_api_url=api_url,
)
except Exception as e:
result = (
f"✗ Connection failed!<br>Error: {str(e)}<br><br>"
"Check URL, backend type, and API token if required."
)
return render_template(
"upload.html",
connection_result=result,
connection_success=False,
selected_backend=backend,
selected_api_url=api_url,
)
# Get form data for translation
file = request.files.get("input_file")
target_lang = request.form.get("target_lang", "").strip()
src_lang = request.form.get("src_lang", "").strip() or None
api_token = request.form.get("api_token", "").strip() or None
model = request.form.get("model", "")
backend = request.form.get("backend", "ollama").strip() or "ollama"
if backend not in {"ollama", "openai_compatible"}:
backend = "ollama"
if not model:
return render_template(
"upload.html",
error="Please select a valid model from the dropdown (query first)",
selected_backend=backend,
selected_api_url=request.form.get(
"api_url", "http://localhost:11434"
).strip(),
)
model = model.strip()
api_url = request.form.get("api_url", "http://localhost:11434").strip()
try:
api_url = validate_api_url(api_url)
except ValueError as exc:
return render_template(
"upload.html",
error=str(exc),
selected_backend=backend,
selected_api_url=request.form.get(
"api_url", "http://localhost:11434"
).strip(),
)
logger.debug(f"Using backend '{backend}' with API base URL: {api_url}")
if not target_lang:
return render_template(
"upload.html",
error="Target language is required",
selected_backend=backend,
selected_api_url=api_url,
)
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
input_filepath = os.path.join(app.config["UPLOAD_FOLDER"], filename)
input_base_name = os.path.splitext(filename)[0]
input_extension = os.path.splitext(filename)[1].lower()
output_extension = ".pdf" if input_extension == ".pdf" else ".docx"
output_filename = f"translated_{input_base_name}{output_extension}"
output_filepath = os.path.join(app.config["OUTPUT_FOLDER"], output_filename)
file.save(input_filepath)
# Call your translation function here
try:
process_document(
input_filepath,
output_filepath,
model,
target_lang,
api_token,
src_lang,
api_url=api_url,
backend=backend,
)
return redirect(url_for("download_file", filename=output_filename))
except Exception as e:
return render_template(
"upload.html",
error=f"Error processing document: {str(e)}",
selected_backend=backend,
selected_api_url=api_url,
)
if not file or not allowed_file(file.filename):
return render_template(
"upload.html",
error="Please upload a valid .docx or .pdf file",
selected_backend=backend,
selected_api_url=api_url,
)
return render_template(
"upload.html",
selected_backend="ollama",
selected_api_url="http://localhost:11434",
)
@app.route("/downloads/<filename>")
def download_file(filename):
safe_filename = secure_filename(filename)
if not safe_filename or safe_filename != filename:
abort(404)
return send_from_directory(
app.config["OUTPUT_FOLDER"], safe_filename, as_attachment=True
)
if __name__ == "__main__":
import os
debug = os.environ.get("FLASK_DEBUG", "0") != "0"
host = os.environ.get(
"FLASK_HOST", "0.0.0.0"
) # Default to all interfaces for LAN access
app.run(host=host, debug=debug)