الميزات: إضافة أدوات جديدة لمعالجة ملفات PDF، تشمل التلخيص والترجمة واستخراج الجداول.
- تفعيل مكون SummarizePdf لإنشاء ملخصات PDF باستخدام الذكاء الاصطناعي. - تفعيل مكون TranslatePdf لترجمة محتوى PDF إلى لغات متعددة. - تفعيل مكون TableExtractor لاستخراج الجداول من ملفات PDF. - تحديث الصفحة الرئيسية والتوجيه ليشمل الأدوات الجديدة. - إضافة ترجمات للأدوات الجديدة باللغات الإنجليزية والعربية والفرنسية. - توسيع أنواع واجهة برمجة التطبيقات (API) لدعم الميزات الجديدة المتعلقة بمعالجة ملفات PDF. --feat: Initialize frontend with React, Vite, and Tailwind CSS - Set up main entry point for React application. - Create About, Home, NotFound, Privacy, and Terms pages with SEO support. - Implement API service for file uploads and task management. - Add global styles using Tailwind CSS. - Create utility functions for SEO and text processing. - Configure Vite for development and production builds. - Set up Nginx configuration for serving frontend and backend. - Add scripts for cleanup of expired files and sitemap generation. - Implement deployment script for production environment.
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backend/app/services/pdf_ai_service.py
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266
backend/app/services/pdf_ai_service.py
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"""PDF AI services — Chat, Summarize, Translate, Table Extract."""
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import os
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import json
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import logging
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import requests
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logger = logging.getLogger(__name__)
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# Configuration
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OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY", "")
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OPENROUTER_MODEL = os.getenv("OPENROUTER_MODEL", "meta-llama/llama-3-8b-instruct")
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OPENROUTER_BASE_URL = os.getenv(
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"OPENROUTER_BASE_URL", "https://openrouter.ai/api/v1/chat/completions"
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)
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class PdfAiError(Exception):
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"""Custom exception for PDF AI service failures."""
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pass
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def _extract_text_from_pdf(input_path: str, max_pages: int = 50) -> str:
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"""Extract text content from a PDF file."""
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try:
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from PyPDF2 import PdfReader
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reader = PdfReader(input_path)
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pages = reader.pages[:max_pages]
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texts = []
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for i, page in enumerate(pages):
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text = page.extract_text() or ""
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if text.strip():
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texts.append(f"[Page {i + 1}]\n{text}")
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return "\n\n".join(texts)
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except Exception as e:
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raise PdfAiError(f"Failed to extract text from PDF: {str(e)}")
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def _call_openrouter(system_prompt: str, user_message: str, max_tokens: int = 1000) -> str:
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"""Send a request to OpenRouter API and return the reply."""
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if not OPENROUTER_API_KEY:
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raise PdfAiError(
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"AI service is not configured. Set OPENROUTER_API_KEY environment variable."
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)
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_message},
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]
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try:
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response = requests.post(
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OPENROUTER_BASE_URL,
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headers={
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"Authorization": f"Bearer {OPENROUTER_API_KEY}",
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"Content-Type": "application/json",
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},
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json={
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"model": OPENROUTER_MODEL,
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"messages": messages,
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"max_tokens": max_tokens,
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"temperature": 0.5,
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},
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timeout=60,
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)
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response.raise_for_status()
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data = response.json()
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reply = (
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data.get("choices", [{}])[0]
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.get("message", {})
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.get("content", "")
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.strip()
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)
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if not reply:
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raise PdfAiError("AI returned an empty response. Please try again.")
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return reply
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except requests.exceptions.Timeout:
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raise PdfAiError("AI service timed out. Please try again.")
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except requests.exceptions.RequestException as e:
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logger.error(f"OpenRouter API error: {e}")
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raise PdfAiError("AI service is temporarily unavailable.")
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# ---------------------------------------------------------------------------
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# 1. Chat with PDF
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# ---------------------------------------------------------------------------
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def chat_with_pdf(input_path: str, question: str) -> dict:
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"""
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Answer a question about a PDF document.
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Args:
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input_path: Path to the PDF file
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question: User's question about the document
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Returns:
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{"reply": "...", "pages_analyzed": int}
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"""
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if not question or not question.strip():
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raise PdfAiError("Please provide a question.")
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text = _extract_text_from_pdf(input_path)
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if not text.strip():
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raise PdfAiError("Could not extract any text from the PDF.")
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# Truncate to fit context window
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max_chars = 12000
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truncated = text[:max_chars]
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system_prompt = (
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"You are a helpful document assistant. The user has uploaded a PDF document. "
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"Answer questions about the document based only on the content provided. "
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"If the answer is not in the document, say so. "
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"Reply in the same language the user uses."
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)
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user_msg = f"Document content:\n{truncated}\n\nQuestion: {question}"
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reply = _call_openrouter(system_prompt, user_msg, max_tokens=800)
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page_count = text.count("[Page ")
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return {"reply": reply, "pages_analyzed": page_count}
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# ---------------------------------------------------------------------------
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# 2. Summarize PDF
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# ---------------------------------------------------------------------------
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def summarize_pdf(input_path: str, length: str = "medium") -> dict:
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"""
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Generate a summary of a PDF document.
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Args:
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input_path: Path to the PDF file
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length: Summary length — "short", "medium", or "long"
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Returns:
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{"summary": "...", "pages_analyzed": int}
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"""
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text = _extract_text_from_pdf(input_path)
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if not text.strip():
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raise PdfAiError("Could not extract any text from the PDF.")
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length_instruction = {
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"short": "Provide a brief summary in 2-3 sentences.",
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"medium": "Provide a summary in 1-2 paragraphs covering the main points.",
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"long": "Provide a detailed summary covering all key points, arguments, and conclusions.",
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}.get(length, "Provide a summary in 1-2 paragraphs covering the main points.")
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max_chars = 12000
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truncated = text[:max_chars]
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system_prompt = (
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"You are a professional document summarizer. "
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"Summarize the document accurately and concisely. "
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"Reply in the same language as the document."
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)
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user_msg = f"{length_instruction}\n\nDocument content:\n{truncated}"
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summary = _call_openrouter(system_prompt, user_msg, max_tokens=1000)
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page_count = text.count("[Page ")
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return {"summary": summary, "pages_analyzed": page_count}
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# ---------------------------------------------------------------------------
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# 3. Translate PDF
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# ---------------------------------------------------------------------------
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def translate_pdf(input_path: str, target_language: str) -> dict:
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"""
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Translate the text content of a PDF to another language.
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Args:
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input_path: Path to the PDF file
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target_language: Target language name (e.g. "English", "Arabic", "French")
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Returns:
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{"translation": "...", "pages_analyzed": int, "target_language": str}
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"""
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if not target_language or not target_language.strip():
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raise PdfAiError("Please specify a target language.")
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text = _extract_text_from_pdf(input_path)
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if not text.strip():
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raise PdfAiError("Could not extract any text from the PDF.")
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max_chars = 10000
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truncated = text[:max_chars]
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system_prompt = (
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f"You are a professional translator. Translate the following document "
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f"content into {target_language}. Preserve the original formatting and "
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f"structure as much as possible. Only output the translation, nothing else."
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)
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translation = _call_openrouter(system_prompt, truncated, max_tokens=2000)
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page_count = text.count("[Page ")
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return {
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"translation": translation,
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"pages_analyzed": page_count,
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"target_language": target_language,
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}
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# ---------------------------------------------------------------------------
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# 4. Extract Tables from PDF
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# ---------------------------------------------------------------------------
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def extract_tables(input_path: str) -> dict:
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"""
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Extract tables from a PDF and return them as structured data.
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Args:
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input_path: Path to the PDF file
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Returns:
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{"tables": [...], "tables_found": int}
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"""
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try:
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import tabula
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tables = tabula.read_pdf(
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input_path, pages="all", multiple_tables=True, silent=True
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)
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if not tables:
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raise PdfAiError(
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"No tables found in the PDF. This tool works best with PDFs containing tabular data."
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)
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result_tables = []
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for idx, df in enumerate(tables):
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# Convert DataFrame to list of dicts
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records = []
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for _, row in df.iterrows():
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record = {}
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for col in df.columns:
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val = row[col]
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if isinstance(val, float) and str(val) == "nan":
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record[str(col)] = ""
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else:
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record[str(col)] = str(val)
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records.append(record)
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result_tables.append({
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"index": idx + 1,
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"columns": [str(c) for c in df.columns],
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"rows": len(records),
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"data": records,
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})
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logger.info(f"Extracted {len(result_tables)} tables from PDF")
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return {
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"tables": result_tables,
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"tables_found": len(result_tables),
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}
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except PdfAiError:
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raise
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except ImportError:
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raise PdfAiError("tabula-py library is not installed.")
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except Exception as e:
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raise PdfAiError(f"Failed to extract tables: {str(e)}")
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