Upload app.py

app.py

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+# =========================
+# تثبيت المكتبات المطلوبة
+# =========================
+!pip install -q gradio pandas numpy gdown sentence-transformers
+
+import gdown
+import pickle
+import pandas as pd
+import numpy as np
+from sentence_transformers import SentenceTransformer, util
+import gradio as gr
+import os
+import zipfile
+
+# =========================
+# روابط الملفات على Google Drive
+# =========================
+files = {
+    "books": "https://drive.google.com/uc?id=1v5UJy9ePQccyquFygHisFVBHS56KhlhQ",
+    "theses": "https://drive.google.com/uc?id=1i2Rrr9gfcNj4QIqfQ8AX8ZlO00whbnKJ",
+    "books_embeddings": "https://drive.google.com/uc?id=1uH5PVUGXYENeqGUaikQ55wtDVcU_04qC",
+    "theses_embeddings": "https://drive.google.com/uc?id=1cYZtgYPzoWxePOW9B3bIdqANOI890rs2",
+    "model_zip": "https://drive.google.com/uc?id=18D4xtDTKCWeGaZ3lsYW7_D79xo95daDL"
+}
+
+# =========================
+# تحميل الملفات
+# =========================
+for name, url in files.items():
+    output = f"{name}.pkl" if name != "model_zip" else "model.zip"
+    if not os.path.exists(output):
+        gdown.download(url, output, quiet=False)
+
+# =========================
+# فك ضغط الموديل
+# =========================
+if not os.path.exists("AI_Library_Model"):
+    with zipfile.ZipFile("model.zip", "r") as zip_ref:
+        zip_ref.extractall("AI_Library_Model")
+
+# =========================
+# قراءة البيانات وEmbeddings
+# =========================
+with open("books.pkl", "rb") as f:
+    books = pickle.load(f)
+
+with open("theses.pkl", "rb") as f:
+    theses = pickle.load(f)
+
+books["type"] = "Book"
+theses["type"] = "Thesis"
+library = pd.concat([books, theses], ignore_index=True)
+
+with open("books_embeddings.pkl", "rb") as f:
+    books_emb = pickle.load(f)
+
+with open("theses_embeddings.pkl", "rb") as f:
+    theses_emb = pickle.load(f)
+
+library_embeddings = np.vstack([books_emb, theses_emb])
+
+# =========================
+# تحميل موديل MiniLM من الفولدر المحلي
+# =========================
+model = SentenceTransformer("AI_Library_Model")
+
+# =========================
+# دالة البحث
+# =========================
+def search_library(query, source_type):
+    filtered = library[library["type"] == source_type]
+    filtered_emb = library_embeddings[filtered.index]
+
+    query_emb = model.encode(query)
+    scores = util.cos_sim(query_emb, filtered_emb)[0]
+    top_idx = np.argsort(scores.cpu().numpy())[::-1][:5]
+
+    results = filtered.iloc[top_idx][["title","author","year","field"]]
+    return results
+
+# =========================
+# واجهة Gradio تاب واحدة
+# =========================
+with gr.Blocks() as demo:
+    gr.Markdown("## مكتبة AI Explorer - البحث الحر المحلي")
+    with gr.Row():
+        query = gr.Textbox(label="اكتب سؤال البحث")
+        source = gr.Dropdown(choices=["Book", "Thesis"], label="نوع المصدر")
+        btn = gr.Button("بحث")
+    output = gr.Dataframe(headers=["العنوان","المؤلف","السنة","المجال"])
+    btn.click(search_library, inputs=[query, source], outputs=output)
+
+demo.launch()