app.py

import os
import shutil
import tempfile
import gradio as gr

from langchain_community.vectorstores import Chroma
from langchain_community.document_loaders import PyPDFLoader, UnstructuredWordDocumentLoader
from langchain_community.embeddings import HuggingFaceEmbeddings

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import RetrievalQA
from langchain.llms import LiteLLM

DB_DIR = "chroma_db"
CHUNK_SIZE = 500
CHUNK_OVERLAP = 50

def load_documents(file_path):
    if file_path.endswith(".pdf"):
        loader = PyPDFLoader(file_path)
    elif file_path.endswith(".docx") or file_path.endswith(".doc"):
        loader = UnstructuredWordDocumentLoader(file_path)
    else:
        raise ValueError("Unsupported file type. Only PDF and DOCX are supported.")
    return loader.load()

def create_vector_store(documents):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)
    texts = text_splitter.split_documents(documents)

    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
    vectordb = Chroma.from_documents(texts, embedding=embeddings, persist_directory=DB_DIR)
    vectordb.persist()
    return vectordb

def process_file(file):
    temp_path = file.name
    target_path = os.path.join(tempfile.gettempdir(), os.path.basename(temp_path))

    if os.path.abspath(temp_path) != os.path.abspath(target_path):
        shutil.copy(temp_path, target_path)

    documents = load_documents(target_path)

    if os.path.exists(DB_DIR):
        shutil.rmtree(DB_DIR)

    vectordb = create_vector_store(documents)
    return "✅ تم معالجة الملف بنجاح. يمكنك الآن كتابة سؤالك."

def ask_question(question):
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
    vectordb = Chroma(persist_directory=DB_DIR, embedding_function=embeddings)

    retriever = vectordb.as_retriever()

    llm = LiteLLM(model="mistralai/Mistral-7B-Instruct-v0.2")  # لا حاجة لمفتاح API
    qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)

    result = qa_chain.run(question)
    return result

with gr.Blocks(title="Smart PDF Assistant") as demo:
    gr.Markdown("### 🤖 مساعد الكتب الذكي - اسأل أي سؤال بناءً على ملف PDF أو DOCX")
    
    with gr.Row():
        file_input = gr.File(label="📄 ارفع ملف PDF أو DOCX", file_types=[".pdf", ".docx", ".doc"])
        file_status = gr.Textbox(label="حالة الملف", interactive=False)

    with gr.Row():
        question_input = gr.Textbox(label="❓ اكتب سؤالك هنا", placeholder="ما هو إيمان الكنيسة؟")
        answer_output = gr.Textbox(label="📘 الإجابة", lines=8)

    file_input.change(process_file, inputs=file_input, outputs=file_status)
    question_input.submit(ask_question, inputs=question_input, outputs=answer_output)

demo.launch()