Delete app.py

app.py

@@ -1,122 +0,0 @@
-import gradio as gr
-import os
-import tempfile
-import faiss
-import torch
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.prompts import PromptTemplate
-from langchain.chains import RetrievalQA
-from langchain.llms import HuggingFacePipeline
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-from pdfminer.high_level import extract_text as extract_pdf_text
-import docx
-import nltk
-
-nltk.download('punkt')
-from nltk.tokenize import sent_tokenize
-
-uploaded_texts = []
-vector_store = None
-qa_chain = None
-
-embedding_model_name = "CAMeL-Lab/bert-base-arabic-camelbert-mix"
-embeddings = HuggingFaceEmbeddings(model_name=embedding_model_name)
-
-model_name = "csebuetnlp/mT5_small"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer, max_length=512)
-llm = HuggingFacePipeline(pipeline=pipe)
-
-ARABIC_PROMPT_TEMPLATE = """
-أنت نظام ذكي يجيب بناءً فقط على المعلومات المستخرجة من الكتب.
-لا تستخدم أي معلومات خارجية.
-السؤال: {question}
-الإجابة:
-"""
-
-def format_arabic_prompt(question):
-    return ARABIC_PROMPT_TEMPLATE.format(question=question)
-
-def extract_text_from_file(file_path):
-    if file_path.endswith(".pdf"):
-        return extract_pdf_text(file_path)
-    elif file_path.endswith(".docx") or file_path.endswith(".doc"):
-        doc = docx.Document(file_path)
-        return "\n".join([para.text for para in doc.paragraphs])
-    else:
-        raise ValueError("Unsupported file format")
-
-def arabic_split_text(text):
-    sentences = sent_tokenize(text, language='arabic')
-    chunks = []
-    chunk = ""
-    for sentence in sentences:
-        if len(chunk) + len(sentence) <= 500:
-            chunk += " " + sentence
-        else:
-            chunks.append(chunk.strip())
-            chunk = sentence
-    if chunk:
-        chunks.append(chunk.strip())
-    return chunks
-
-def train_from_texts(texts):
-    global vector_store, qa_chain
-
-    splitter = RecursiveCharacterTextSplitter(
-        chunk_size=500,
-        chunk_overlap=100,
-        length_function=len,
-    )
-    
-    all_chunks = []
-    for text in texts:
-        chunks = arabic_split_text(text)
-        all_chunks.extend(chunks)
-
-    vectors = embeddings.embed_documents(all_chunks)
-    dimension = len(vectors[0])
-    index = faiss.IndexFlatL2(dimension)
-    vector_store = FAISS(embedding_function=embeddings, index=index, documents=all_chunks)
-
-    retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 10})
-    qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
-
-def upload_book(file, progress=gr.Progress()):
-    with tempfile.NamedTemporaryFile(delete=False) as tmp:
-        tmp.write(file.read())
-        tmp_path = tmp.name
-
-    progress(0.2, desc="تحميل الملف...")
-    extracted_text = extract_text_from_file(tmp_path)
-    uploaded_texts.append(extracted_text)
-    progress(0.5, desc="معالجة النص...")
-
-    train_from_texts(uploaded_texts)
-    progress(1.0, desc="اكتمل التدريب!")
-    return "النظام جاهز للإجابة على أسئلتك"
-
-def answer_question(user_question):
-    if qa_chain is None:
-        return "الرجاء رفع كتاب أولاً."
-    prompt = format_arabic_prompt(user_question)
-    result = qa_chain.run(prompt)
-    return result
-
-with gr.Blocks() as demo:
-    with gr.Tab("تحميل الكتب"):
-        upload_button = gr.File(label="ارفع كتابك (.pdf .docx .doc)", file_types=[".pdf", ".docx", ".doc"])
-        upload_output = gr.Textbox(label="حالة النظام")
-        upload_button.upload(upload_book, inputs=upload_button, outputs=upload_output)
-
-    with gr.Tab("اسأل الكتاب"):
-        question = gr.Textbox(label="اكتب سؤالك بالعربية")
-        answer = gr.Textbox(label="الإجابة")
-        ask_button = gr.Button("إرسال السؤال")
-        ask_button.click(answer_question, inputs=question, outputs=answer)
-
-demo.launch(share=True)