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Update app.py
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app.py
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# app.py
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import os
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import shutil
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import
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from ctransformers import AutoModelForCausalLM
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from langchain.embeddings import SentenceTransformerEmbeddings
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from langchain.vectorstores import Chroma
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from langchain.chains import RetrievalQA
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from
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from
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#
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llm =
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model_file="mistral-7b-instruct-v0.2.Q4_K_M.gguf",
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model_type="mistral",
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config={
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)
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#
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if os.path.exists(CHROMA_DIR):
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shutil.rmtree(CHROMA_DIR)
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# 3. تحميل الملفات وتقسيمها
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SUPPORTED_TYPES = {"pdf": PyPDFLoader, "docx": Docx2txtLoader, "txt": TextLoader}
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documents = []
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for
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return documents
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# 4. تقسيم النصوص وإنشاء المتجهات
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
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embedding = SentenceTransformerEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
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def create_vectorstore(docs):
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#
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qa_chain = None
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def
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global
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db = create_vectorstore(docs)
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retriever = db.as_retriever(search_kwargs={"k": 5})
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qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
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return "تم تحميل الملفات وبناء قاعدة المعرفة بنجاح ✅"
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result = qa_chain.run(question)
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return result
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with gr.Row():
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if __name__ == "__main__":
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demo.launch()
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import os
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import shutil
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import tempfile
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from langchain_community.llms import CTransformers
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from langchain.chains import RetrievalQA
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from langchain.prompts import PromptTemplate
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from langchain.vectorstores import Chroma
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from langchain.embeddings import SentenceTransformerEmbeddings
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from langchain.document_loaders import PyPDFLoader, Docx2txtLoader, TextLoader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from fastapi import FastAPI, UploadFile, File
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from fastapi.responses import JSONResponse
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import uvicorn
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import gradio as gr
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# إعداد نموذج اللغة
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llm = CTransformers(
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model="TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
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model_file="mistral-7b-instruct-v0.2.Q4_K_M.gguf",
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model_type="mistral",
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config={
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"max_new_tokens": 512,
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"temperature": 0.7,
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"context_length": 4096,
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"gpu_layers": 20,
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}
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)
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# إعداد المطابقة الدلالية
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embedding_function = SentenceTransformerEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
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# تحميل المستندات وإنشاء قاعدة معرفية
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def load_documents_from_folder(folder_path):
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documents = []
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for filename in os.listdir(folder_path):
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full_path = os.path.join(folder_path, filename)
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if filename.endswith(".pdf"):
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loader = PyPDFLoader(full_path)
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elif filename.endswith(".docx"):
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loader = Docx2txtLoader(full_path)
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elif filename.endswith(".txt"):
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loader = TextLoader(full_path)
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else:
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continue
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docs = loader.load()
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documents.extend(docs)
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return documents
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def create_vectorstore(docs):
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splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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chunks = splitter.split_documents(docs)
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return Chroma.from_documents(chunks, embedding_function)
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# إعداد واجهة الإجابة
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retriever = None
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qa = None
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def setup_qa(folder_path):
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global retriever, qa
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docs = load_documents_from_folder(folder_path)
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vectordb = create_vectorstore(docs)
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retriever = vectordb.as_retriever()
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prompt_template = """
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أنت مساعد ذكي تجيب باللغة العربية، تستند فقط إلى محتوى الوثائق المقدمة.
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لا تقم بإضافة أي معلومات من عندك.
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السؤال: {question}
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=========
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الوثائق:
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{context}
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=========
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الإجاب�� المفصلة باللغة العربية:
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"""
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prompt = PromptTemplate(template=prompt_template, input_variables=["question", "context"])
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qa = RetrievalQA.from_chain_type(llm=llm, retriever=retriever, chain_type_kwargs={"prompt": prompt})
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# تحميل الملفات من Gradio
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def process_uploaded_files(files):
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temp_dir = tempfile.mkdtemp()
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for file in files:
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dest_path = os.path.join(temp_dir, file.name)
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with open(dest_path, "wb") as f:
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f.write(file.read())
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setup_qa(temp_dir)
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shutil.rmtree(temp_dir)
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# الإجابة على الأسئلة
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def answer_question(question):
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if qa is None:
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return "الرجاء رفع ملفاتك أولًا."
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response = qa.run(question)
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return response
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# واجهة Gradio
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with gr.Blocks(css=".gradio-container { direction: rtl; text-align: right; font-family: 'Cairo', sans-serif; }") as demo:
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gr.Markdown("## مساعد الوثائق الذكي")
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with gr.Row():
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file_input = gr.File(file_types=[".pdf", ".docx", ".txt"], file_count="multiple", label="ارفع ملفاتك")
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load_button = gr.Button("ابدأ التحليل")
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question_input = gr.Textbox(label="اكتب سؤالك هنا")
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answer_output = gr.Textbox(label="الإجابة")
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load_button.click(fn=process_uploaded_files, inputs=[file_input], outputs=[])
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question_input.submit(fn=answer_question, inputs=[question_input], outputs=[answer_output])
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# تشغيل Gradio
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if __name__ == "__main__":
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demo.launch()
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