app.py

import os
import io
import uuid
import asyncio
import aiohttp
import uvicorn
from typing import List, Dict, Any, Optional, Generator
from fastapi import FastAPI, UploadFile, File, Form, Query, BackgroundTasks, Request
from fastapi.responses import StreamingResponse, JSONResponse, FileResponse, HTMLResponse
from fastapi.staticfiles import StaticFiles
from fastapi.middleware.cors import CORSMiddleware
from fastapi.templating import Jinja2Templates
from pydantic import BaseModel, Field, HttpUrl
import time
from PIL import Image

# Import utility functions
from utils.ocr import detect_text, group_text_regions
from utils.web import scrape_comic_images, download_image
from utils.pdf import pdf_to_images, pdf_stream_to_images
from utils.image import overlay_grouped_text, save_image
from utils.translation import translate_grouped_regions

# Configuration
STATIC_DIR = "static"
TRANSLATED_IMAGE_DIR = os.path.join(STATIC_DIR, "translated")
FONT_PATH = "font/Movistar Text Regular.ttf"

# Ensure directories exist
os.makedirs(TRANSLATED_IMAGE_DIR, exist_ok=True)

# Initialize FastAPI app
app = FastAPI(
    title="Manga OCR Translator API",
    description="API for translating manga images using OCR and machine translation",
    version="1.0.0",
)

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Allow all origins
    allow_credentials=True,
    allow_methods=["*"],  # Allow all methods
    allow_headers=["*"],  # Allow all headers
)

# Mount static files directory
app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")

# Define request and response models
class TranslationRequest(BaseModel):
    url: HttpUrl = Field(..., description="URL of the manga chapter to translate")
    src_lang: str = Field(default="auto", description="Source language (auto, ja, ko, zh)")
    tgt_lang: str = Field(default="en", description="Target language (en, es, fr, de, it, pt, ru)")
    translator: str = Field(default="google", description="Translation engine (google, mymemory, linguee, pollinations)")

class TranslationResponse(BaseModel):
    status: str
    message: str
    images: List[str] = []

# Basic homepage route - now serves our UI
@app.get("/", response_class=HTMLResponse)
async def root():
    # Serve the UI instead of the API response
    return FileResponse("static/ui/index.html")

# API info endpoint
@app.get("/api/info")
async def api_info():
    return {
        "message": "Manga OCR Translator API",
        "endpoints": {
            "/translate/url": "Translate manga from a URL",
            "/translate/pdf": "Translate manga from a PDF file",
            "/docs": "API documentation"
        }
    }

# Route for processing manga URL with streaming response
@app.post("/translate/url")
async def translate_manga_url(request: TranslationRequest):
    """
    Process a manga URL and return translated images with streaming response.
    Each image is processed and returned as soon as it's ready.
    """
    print(f"Received request to translate URL: {request.url}")
    
    # Create a generator function that yields translated images
    async def process_images():
        try:
            # Scrape image URLs from the manga page
            image_urls = scrape_comic_images(str(request.url))
            if not image_urls:
                yield f"data: {{'status': 'error', 'message': 'No images found at the URL', 'images': []}}\n\n"
                return
                
            print(f"Found {len(image_urls)} images to process")
            
            # Limit to first 5 images if too many
            if len(image_urls) > 5:
                print("Limiting to first 5 images")
                image_urls = image_urls[:5]
            
            # Process each image
            for i, image_url in enumerate(image_urls):
                try:
                    # Download image
                    print(f"Processing image {i+1}/{len(image_urls)}: {image_url}")
                    
                    # Update client with status
                    yield f"data: {{'status': 'processing', 'message': 'Processing image {i+1}/{len(image_urls)}', 'image_url': '{image_url}'}}\n\n"
                    
                    # Download the image
                    image_content = await download_image(image_url)
                    if not image_content:
                        print(f"Failed to download image {i+1}")
                        continue
                        
                    # Detect text regions
                    text_regions = detect_text(image_content, request.src_lang)
                    if not text_regions:
                        print(f"No text detected in image {i+1}")
                        continue
                        
                    # Group text regions
                    grouped_regions = group_text_regions(text_regions)
                    if not grouped_regions:
                        print(f"No text groups formed in image {i+1}")
                        continue
                        
                    # Translate grouped regions
                    use_pollinations = request.translator == "pollinations"
                    free_translator = request.translator if not use_pollinations else "google"
                    
                    translated_regions = translate_grouped_regions(
                        grouped_regions,
                        request.src_lang,
                        request.tgt_lang,
                        use_pollinations,
                        free_translator
                    )
                    
                    # Overlay translated text on image
                    translated_image = overlay_grouped_text(image_content, translated_regions)
                    
                    # Save image and get path
                    image_path = save_image(translated_image, TRANSLATED_IMAGE_DIR)
                    
                    # Create a URL to the saved image
                    image_url = f"/static/translated/{os.path.basename(image_path)}"
                    
                    # Stream the result back to the client
                    json_response = {
                        "status": "success",
                        "message": f"Processed image {i+1}/{len(image_urls)}",
                        "image_url": image_url
                    }
                    
                    # Send this single image result
                    yield f"data: {json_response}\n\n"
                    
                except Exception as e:
                    print(f"Error processing image {i+1}: {e}")
                    yield f"data: {{'status': 'error', 'message': 'Error processing image {i+1}: {str(e)}'}}\n\n"
            
            # Final message
            yield f"data: {{'status': 'complete', 'message': 'All images processed'}}\n\n"
            
        except Exception as e:
            print(f"Error in process_images: {e}")
            yield f"data: {{'status': 'error', 'message': 'Error: {str(e)}'}}\n\n"
    
    # Return a streaming response
    return StreamingResponse(
        process_images(),
        media_type="text/event-stream",
        headers={
            "Cache-Control": "no-cache",
            "Connection": "keep-alive",
            "X-Accel-Buffering": "no"  # Disable buffering for Nginx
        }
    )

# Route for processing PDF file with streaming response
@app.post("/translate/pdf")
async def translate_manga_pdf(
    file: UploadFile = File(...),
    src_lang: str = Form("auto"),
    tgt_lang: str = Form("en"),
    translator: str = Form("google")
):
    """
    Process a manga PDF file and return translated images with streaming response.
    Each image is processed and returned as soon as it's ready.
    """
    print(f"Received PDF file: {file.filename}, size: {file.size} bytes")
    
    # Create a generator function that yields translated images
    async def process_pdf():
        try:
            # Read the PDF file
            pdf_content = await file.read()
            
            # Convert PDF to images
            yield f"data: {{'status': 'processing', 'message': 'Converting PDF to images...'}}\n\n"
            
            # Convert PDF to images in memory
            pdf_images = await pdf_stream_to_images(pdf_content)
            
            if not pdf_images:
                yield f"data: {{'status': 'error', 'message': 'Failed to extract images from PDF', 'images': []}}\n\n"
                return
                
            print(f"Extracted {len(pdf_images)} pages from PDF")
            
            # Limit to first 5 images if too many
            if len(pdf_images) > 5:
                print("Limiting to first 5 pages")
                pdf_images = pdf_images[:5]
            
            # Process each image
            for i, image_content in enumerate(pdf_images):
                try:
                    # Update client with status
                    print(f"Processing PDF page {i+1}/{len(pdf_images)}")
                    yield f"data: {{'status': 'processing', 'message': 'Processing PDF page {i+1}/{len(pdf_images)}'}}\n\n"
                    
                    # Detect text regions
                    text_regions = detect_text(image_content, src_lang)
                    if not text_regions:
                        print(f"No text detected in PDF page {i+1}")
                        continue
                        
                    # Group text regions
                    grouped_regions = group_text_regions(text_regions)
                    if not grouped_regions:
                        print(f"No text groups formed in PDF page {i+1}")
                        continue
                        
                    # Translate grouped regions
                    use_pollinations = translator == "pollinations"
                    free_translator = translator if not use_pollinations else "google"
                    
                    translated_regions = translate_grouped_regions(
                        grouped_regions,
                        src_lang,
                        tgt_lang,
                        use_pollinations,
                        free_translator
                    )
                    
                    # Overlay translated text on image
                    pil_image = Image.open(io.BytesIO(image_content))
                    translated_image = overlay_grouped_text(image_content, translated_regions)
                    
                    # Save image and get path
                    image_path = save_image(translated_image, TRANSLATED_IMAGE_DIR)
                    
                    # Create a URL to the saved image
                    image_url = f"/static/translated/{os.path.basename(image_path)}"
                    
                    # Stream the result back to the client
                    json_response = {
                        "status": "success",
                        "message": f"Processed PDF page {i+1}/{len(pdf_images)}",
                        "image_url": image_url
                    }
                    
                    # Send this single image result
                    yield f"data: {json_response}\n\n"
                    
                except Exception as e:
                    print(f"Error processing PDF page {i+1}: {e}")
                    yield f"data: {{'status': 'error', 'message': 'Error processing PDF page {i+1}: {str(e)}'}}\n\n"
            
            # Final message
            yield f"data: {{'status': 'complete', 'message': 'All PDF pages processed'}}\n\n"
            
        except Exception as e:
            print(f"Error in process_pdf: {e}")
            yield f"data: {{'status': 'error', 'message': 'Error: {str(e)}'}}\n\n"
    
    # Return a streaming response
    return StreamingResponse(
        process_pdf(),
        media_type="text/event-stream",
        headers={
            "Cache-Control": "no-cache",
            "Connection": "keep-alive",
            "X-Accel-Buffering": "no"  # Disable buffering for Nginx
        }
    )

# Main entry point
if __name__ == "__main__":
    print("Starting Manga OCR Translator API server...")
    uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)