app.py

"""

BabelDocs x Agentic AI MCP - Gradio Application



PDF Translation with Google Drive Integration.

Accepts public GDrive links or local file uploads.



For Anthropic Hackathon - Track 1: Building MCP



Usage:

    python app.py

"""

import os
import re
import base64
import tempfile
import httpx
import gradio as gr
from pathlib import Path
from datetime import datetime
from dotenv import load_dotenv

load_dotenv()

# Modal endpoint configuration
# Set BABELDOCS_MODAL_URL as HuggingFace Space secret for production
MODAL_BASE_URL = os.getenv("BABELDOCS_MODAL_URL")
if not MODAL_BASE_URL:
    raise ValueError("BABELDOCS_MODAL_URL environment variable required. Set it as a HuggingFace Space secret.")
MODAL_TRANSLATE_URL = f"{MODAL_BASE_URL}-babeldocstranslator-api.modal.run"
MODAL_HEALTH_URL = f"{MODAL_BASE_URL}-babeldocstranslator-health.modal.run"

# Max pages limit (test phase)
MAX_PAGES = 20

# Supported languages
LANGUAGES = {
    "fr": "French",
    "en": "English",
    "es": "Spanish",
    "de": "German",
    "it": "Italian",
    "pt": "Portuguese",
    "zh": "Chinese",
    "ja": "Japanese",
    "ko": "Korean",
    "ru": "Russian",
    "ar": "Arabic",
}


def log_message(logs: list, message: str) -> list:
    """Add timestamped message to logs."""
    timestamp = datetime.now().strftime("%H:%M:%S")
    logs.append(f"[{timestamp}] {message}")
    return logs


def extract_gdrive_file_id(url: str) -> str | None:
    """Extract file ID from Google Drive URL."""
    patterns = [
        r"/file/d/([a-zA-Z0-9_-]+)",
        r"id=([a-zA-Z0-9_-]+)",
        r"/d/([a-zA-Z0-9_-]+)",
    ]
    for pattern in patterns:
        match = re.search(pattern, url)
        if match:
            return match.group(1)
    return None


async def download_gdrive_public(url: str) -> tuple[bytes, str]:
    """Download file from public Google Drive link.



    Returns (file_bytes, filename).

    """
    file_id = extract_gdrive_file_id(url)
    if not file_id:
        raise ValueError("Invalid Google Drive URL")

    # Direct download URL
    download_url = f"https://drive.google.com/uc?export=download&id={file_id}"

    async with httpx.AsyncClient(timeout=120.0, follow_redirects=True) as client:
        response = await client.get(download_url)
        response.raise_for_status()

        # Try to get filename from Content-Disposition header
        content_disp = response.headers.get("Content-Disposition", "")
        filename_match = re.search(r'filename="?([^";\n]+)"?', content_disp)
        if filename_match:
            filename = filename_match.group(1)
        else:
            filename = f"gdrive_{file_id}.pdf"

        return response.content, filename


async def translate_pdf_modal(

    pdf_file,

    gdrive_url: str,

    target_lang: str,

    progress=gr.Progress()

) -> tuple:
    """Translate PDF using Modal cloud."""
    logs = []

    # Validate input
    if not pdf_file and not gdrive_url:
        return None, None, "Please upload a PDF or provide a Google Drive link", "", "\n".join(logs)

    if pdf_file and gdrive_url:
        return None, None, "Please use either file upload OR Google Drive link, not both", "", "\n".join(logs)

    try:
        logs = log_message(logs, "Starting translation...")

        # Get PDF bytes and filename
        if gdrive_url:
            logs = log_message(logs, f"Downloading from Google Drive...")
            progress(0.05, desc="Downloading from Google Drive...")
            pdf_bytes, source_filename = await download_gdrive_public(gdrive_url.strip())
            logs = log_message(logs, f"Downloaded: {source_filename}")
        else:
            pdf_path = Path(pdf_file)
            pdf_bytes = pdf_path.read_bytes()
            source_filename = pdf_path.name

        pdf_base64 = base64.b64encode(pdf_bytes).decode("utf-8")

        logs = log_message(logs, f"Input: {source_filename}")
        logs = log_message(logs, f"Size: {len(pdf_bytes) / 1024:.1f} KB")
        logs = log_message(logs, f"Target: {LANGUAGES.get(target_lang, target_lang)}")

        progress(0.1, desc="Uploading to Modal...")

        payload = {
            "pdf_base64": pdf_base64,
            "target_lang": target_lang,
        }

        logs = log_message(logs, "Translating on Modal cloud...")
        logs = log_message(logs, "(This may take several minutes)")

        progress(0.2, desc="Translating...")
        start_time = datetime.now()

        async with httpx.AsyncClient(timeout=900.0, follow_redirects=True) as client:
            response = await client.post(MODAL_TRANSLATE_URL, json=payload)
            response.raise_for_status()
            result = response.json()

        duration = (datetime.now() - start_time).total_seconds()
        progress(0.8, desc="Processing result...")

        if not result.get("success"):
            error_msg = result.get("message", "Unknown error")
            logs = log_message(logs, f"ERROR: {error_msg}")
            return None, None, "Translation failed", "", "\n".join(logs)

        # Process mono_img PDF
        mono_img_path = None
        mono_img_base64 = result.get("mono_img_pdf_base64")
        if mono_img_base64:
            mono_img_bytes = base64.b64decode(mono_img_base64)
            stem = Path(source_filename).stem
            mono_img_filename = f"{stem}_translated.{target_lang}.pdf"
            mono_img_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
            mono_img_file.write(mono_img_bytes)
            mono_img_file.close()
            mono_img_path = mono_img_file.name
            logs = log_message(logs, f"Mono: {mono_img_filename} ({len(mono_img_bytes) / 1024:.1f} KB)")

        # Process dual_img PDF
        dual_img_path = None
        dual_img_base64 = result.get("dual_img_pdf_base64")
        if dual_img_base64:
            dual_img_bytes = base64.b64decode(dual_img_base64)
            stem = Path(source_filename).stem
            dual_img_filename = f"{stem}_translated.{target_lang}.dual.pdf"
            dual_img_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
            dual_img_file.write(dual_img_bytes)
            dual_img_file.close()
            dual_img_path = dual_img_file.name
            logs = log_message(logs, f"Dual: {dual_img_filename} ({len(dual_img_bytes) / 1024:.1f} KB)")

        if not mono_img_path and not dual_img_path:
            logs = log_message(logs, "ERROR: No output PDF in response")
            return None, None, "Translation failed", "", "\n".join(logs)

        logs = log_message(logs, f"Duration: {duration:.1f} seconds")

        stats_msg = f"""**Translation completed!**



- **Duration:** {duration:.1f} seconds

- **Target:** {LANGUAGES.get(target_lang, target_lang)}"""

        progress(1.0, desc="Done!")

        return mono_img_path, dual_img_path, "Translation successful!", stats_msg, "\n".join(logs)

    except httpx.TimeoutException:
        logs = log_message(logs, "ERROR: Translation timed out")
        return None, None, "Translation timed out", "", "\n".join(logs)
    except httpx.HTTPStatusError as e:
        logs = log_message(logs, f"ERROR: HTTP {e.response.status_code}")
        return None, None, f"HTTP error: {e.response.status_code}", "", "\n".join(logs)
    except Exception as e:
        logs = log_message(logs, f"ERROR: {str(e)}")
        return None, None, f"Error: {str(e)}", "", "\n".join(logs)


# Gradio Interface
with gr.Blocks(title="BabelDocs x Agentic AI MCP") as demo:

    gr.Markdown("""

# BabelDocs x Agentic AI MCP - PDF Translation with Google Drive Integration



**Translate PDFs directly from Google Drive and save back automatically**



---



## Key Feature: Full Google Drive Workflow in CLAUDE Desktop MCP



```

"Translate my Q3 report to French and save it to Translations folder"

                              ↓

        Claude searches → downloads → translates → uploads → done!

```



---

    """)

    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### Input")

            gdrive_url = gr.Textbox(
                label="Google Drive Link (public)",
                placeholder="https://drive.google.com/file/d/... or leave empty",
                info="Paste a public GDrive link, OR upload a local file below",
            )

            gr.Markdown("**OR**")

            pdf_input = gr.File(
                label="Upload PDF",
                file_types=[".pdf"],
                type="filepath",
            )

            target_lang = gr.Dropdown(
                choices=list(LANGUAGES.keys()),
                value="fr",
                label="Target Language",
            )

            translate_btn = gr.Button(
                "Translate PDF",
                variant="primary",
                size="lg",
            )

        with gr.Column(scale=1):
            gr.Markdown("### Result")

            status_output = gr.Textbox(
                label="Status",
                interactive=False,
            )

            stats_output = gr.Markdown(label="Statistics")

            gr.Markdown("**Downloads:**")
            with gr.Row():
                mono_img_output = gr.File(label="Mono (translated + images)")
                dual_img_output = gr.File(label="Dual (bilingual + images)")

            logs_output = gr.Textbox(
                label="Logs",
                interactive=False,
                lines=10,
                max_lines=15,
            )

    gr.Markdown("""

---



### How it works



```

1. Upload PDF or paste GDrive link

       ↓

2. Send to Modal cloud (serverless)

       ↓

3. BabelDOC with Agentic AI translates text + images, preserves layout

       ↓

4. Download translated PDF

```



---



**Built with:** BabelDOC, Modal, Nebius AI, Gradio | **Hackathon:** Anthropic MCP Track 1

    """)

    translate_btn.click(
        fn=translate_pdf_modal,
        inputs=[pdf_input, gdrive_url, target_lang],
        outputs=[mono_img_output, dual_img_output, status_output, stats_output, logs_output],
    )


if __name__ == "__main__":
    demo.launch(
        server_name="127.0.0.1",
        server_port=7860,
        share=False,
    )