Delete app.py

app.py

@@ -1,97 +0,0 @@
-import gradio as gr
-import pandas as pd
-import numpy as np
-import pickle
-from sentence_transformers import SentenceTransformer, util
-import gdown
-import os
-
-# --------- روابط Google Drive ----------
-DRIVE_LINKS = {
-    "books": "https://drive.google.com/uc?export=download&id=1FElHiASfiVLeuHWYaqd2Q5foxWRlJT-O",
-    "theses": "https://drive.google.com/uc?export=download&id=1K2Mtze6ZdvfKUsFMCOWlRBjDq-ZnJNrv"
-}
-
-BOOKS_FILE = "book.xlsx"
-THESES_FILE = "theses.xlsx"
-
-# --------- تنزيل الملفات لو مش موجودة ----------
-def download_from_drive(link, output):
-    if not os.path.exists(output):
-        gdown.download(link, output, quiet=False)
-
-download_from_drive(DRIVE_LINKS["books"], BOOKS_FILE)
-download_from_drive(DRIVE_LINKS["theses"], THESES_FILE)
-
-# --------- قراءة البيانات ----------
-def load_data(file):
-    df = pd.read_excel(file).fillna("غير متوافر")
-    if "Title" not in df.columns and "العنوان" in df.columns:
-        df["Title"] = df["العنوان"].astype(str)
-    elif "Title" not in df.columns:
-        df["Title"] = df.iloc[:,0].astype(str)
-    return df
-
-books_df = load_data(BOOKS_FILE)
-theses_df = load_data(THESES_FILE)
-
-# --------- نموذج Semantic ----------
-MODEL_NAME = "all-MiniLM-L6-v2"
-model = SentenceTransformer(MODEL_NAME)
-
-# --------- إنشاء Embeddings مرة واحدة ----------
-def build_or_load_embeddings(df, name):
-    path = f"{name}_embeddings.pkl"
-    if os.path.exists(path):
-        with open(path, "rb") as f:
-            emb = pickle.load(f)
-        if len(emb) == len(df):
-            return emb
-    texts = df["Title"].astype(str).tolist()
-    emb = model.encode(texts, convert_to_numpy=True, show_progress_bar=True)
-    with open(path, "wb") as f:
-        pickle.dump(emb, f)
-    return emb
-
-books_embeddings = build_or_load_embeddings(books_df, "books")
-theses_embeddings = build_or_load_embeddings(theses_df, "theses")
-
-# --------- دالة البحث ----------
-def search(query, category, mode):
-    if not query.strip():
-        return "⚠️ اكتب كلمة أو جملة للبحث"
-
-    df = books_df if category=="Books" else theses_df
-    emb = books_embeddings if category=="Books" else theses_embeddings
-
-    if mode == "نصي":
-        results = df[df["Title"].str.contains(query, case=False, na=False)]
-    else:
-        q_emb = model.encode([query], convert_to_numpy=True)
-        scores = util.cos_sim(q_emb, emb)[0].cpu().numpy()
-        idx = np.argsort(-scores)
-        results = df.iloc[idx]
-
-    if results.empty:
-        return "❌ لم يتم العثور على نتائج"
-
-    html = "<table border=1 style='border-collapse:collapse;width:100%;'>"
-    html += "<tr>" + "".join([f"<th>{col}</th>" for col in results.columns]) + "</tr>"
-    for _, row in results.iterrows():
-        html += "<tr>" + "".join([f"<td>{val}</td>" for val in row.values]) + "</tr>"
-    html += "</table>"
-    return html
-
-# --------- واجهة Gradio ----------
-iface = gr.Interface(
-    fn=search,
-    inputs=[
-        gr.Textbox(label="اكتب كلمة البحث"),
-        gr.Dropdown(["Books","Theses"], label="الفئة"),
-        gr.Radio(["نصي","دلالي"], label="نوع البحث")
-    ],
-    outputs="html",
-    title="البحث في المكتبة الرقمية"
-)
-
-iface.launch()