Delete app.py

app.py

@@ -1,45 +0,0 @@
-import os
-import gradio as gr
-from langchain.document_loaders import PyPDFLoader
-from langchain.vectorstores import Chroma
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.llms import HuggingFaceHub
-from langchain.chains import RetrievalQA
-
-DB_DIR = "chroma_db"
-embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
-llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-Instruct-v0.2", model_kwargs={"temperature":0.3, "max_new_tokens":500})
-
-def load_and_index(files):
-    all_texts = []
-    for file in files:
-        loader = PyPDFLoader(file.name)
-        docs = loader.load()
-        splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-        texts = splitter.split_documents(docs)
-        all_texts.extend(texts)
-    vectordb = Chroma.from_documents(all_texts, embedding=embedding_model, persist_directory=DB_DIR)
-    vectordb.persist()
-    return "✅ تم تحميل وفهرسة الملفات."
-
-def answer_question(query):
-    vectordb = Chroma(persist_directory=DB_DIR, embedding_function=embedding_model)
-    qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=vectordb.as_retriever())
-    answer = qa_chain.run(query)
-    return answer
-
-with gr.Blocks(title="Smart PDF Assistant") as demo:
-    gr.Markdown("# 🤖 Smart PDF Assistant\nحمّل ملفات PDF واسأل أي سؤال 📚")
-    with gr.Row():
-        uploader = gr.File(file_types=[".pdf"], file_count="multiple", label="تحميل ملفات PDF")
-        index_btn = gr.Button("فهرسة الملفات")
-    index_output = gr.Textbox(label="حالة الفهرسة")
-    index_btn.click(load_and_index, inputs=[uploader], outputs=[index_output])
-
-    query = gr.Textbox(label="اكتب سؤالك")
-    answer_btn = gr.Button("أجب")
-    answer_output = gr.Textbox(label="الإجابة")
-    answer_btn.click(answer_question, inputs=[query], outputs=[answer_output])
-
-demo.launch()