app.py

# Creating your fully corrected Hugging Face Space project

# app.py

import os
import tempfile
import gradio as gr
import faiss
import numpy as np
from transformers import AutoModel, AutoTokenizer
from langchain.text_splitter import RecursiveCharacterTextSplitter
from sentence_transformers import SentenceTransformer
from pdfminer.high_level import extract_text
from docx import Document

# Load Arabic embedding model
embedding_model = SentenceTransformer("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
index = None
texts = []

def extract_text_from_pdf(pdf_path):
    return extract_text(pdf_path)

def extract_text_from_docx(docx_path):
    doc = Document(docx_path)
    return "\n".join([para.text for para in doc.paragraphs])

def process_files(files, progress=gr.Progress()):
    global index, texts
    texts = []

    temp_dir = tempfile.mkdtemp()

    # Step 1: Extract text
    progress(0.1, desc="\u062c\u0627\u0631\u0650 \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u0627\u0644\u0646\u0635\u0648\u0635 \u0645\u0646 \u0627\u0644\u0643\u062a\u0628...")
    for file in files:
        file_path = os.path.join(temp_dir, file.name)
        with open(file_path, "wb") as f:
            f.write(file.file.read())

        if file.name.endswith(".pdf"):
            text = extract_text_from_pdf(file_path)
        elif file.name.endswith(".docx") or file.name.endswith(".doc"):
            text = extract_text_from_docx(file_path)
        else:
            continue

        texts.append(text)

    # Step 2: Chunk the text
    progress(0.4, desc="\u062a\u0642\u0637\u064a\u0639 \u0627\u0644\u0646\u0635\u0648\u0635 \u0625\u0644\u0649 \u0641\u0642\u0631\u0627\u062a...")
    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    chunks = []
    for text in texts:
        chunks.extend(splitter.split_text(text))

    # Step 3: Embed the text
    progress(0.7, desc="\u062a\u062d\u0648\u064a\u0644 \u0627\u0644\u0641\u0642\u0631\u0627\u062a \u0625\u0644\u0649 \u0645\u062a\u062c\u0647\u0627\u062a...")
    embeddings = embedding_model.encode(chunks, show_progress_bar=True)

    # Step 4: Build FAISS index
    progress(0.9, desc="\u0628\u0646\u0627\u0621 \u0642\u0627\u0639\u062f\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0628\u062d\u062b...")
    embeddings = np.array(embeddings).astype(np.float32)
    index = faiss.IndexFlatL2(embeddings.shape[1])
    index.add(embeddings)
    texts.clear()
    texts.extend(chunks)

    return "\u2705 \u0627\u0644\u0646\u0638\u0627\u0645 \u062c\u0627\u0647\u0632 \u0644\u0644\u0625\u062c\u0627\u0628\u0629 \u0639\u0644\u0649 \u0623\u0633\u0626\u0644\u062a\u0643"

def answer_question(question):
    if index is None:
        return "\u064a\u0631\u062c\u0649 \u062a\u062d\u0645\u064a\u0644 \u0643\u062a\u0628 \u0648\u0627\u0644\u0646\u0642\u0631 \u0639\u0644\u0649 \"\u0627\u0628\u062f\u0623 \u0627\u0644\u062a\u062f\u0631\u064a\u0628\" \u0623\u0648\u0644\u0627" 

    embedded_question = embedding_model.encode([question]).astype(np.float32)
    D, I = index.search(embedded_question, k=1)
    if len(I[0]) == 0:
        return "\u0644\u0645 \u064a\u062a\u0645 \u0627\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u0625\u062c\u0627\u0628\u0629."

    answer = texts[I[0][0]]
    return answer

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# \ud83d\udcda محاكاة دماغ المؤلف بناءً على الكتب المرفوعة")

    with gr.Row():
        files = gr.File(label="ارفع ملفات الكتب", file_types=[".pdf", ".docx", ".doc"], file_count="multiple")
        upload_button = gr.Button("ابدأ التدريب على الكتب")

    output_text = gr.Textbox(label="مخرجات التدريب", interactive=False)

    upload_button.click(fn=process_files, inputs=[files], outputs=[output_text])

    gr.Markdown("## اطرح سؤالك بعد إكمال التدريب:")
    question = gr.Textbox(label="سؤالك بالعربية")
    answer = gr.Textbox(label="الإجابة", interactive=False)
    ask_button = gr.Button("أجب عن سؤالي")

    ask_button.click(fn=answer_question, inputs=[question], outputs=[answer])

demo.launch(share=True)