Update app.py

app.py

@@ -14,20 +14,16 @@ user_name = "M. Galal"
 VECTORSTORE_DIR = "./vectorstores"
 os.makedirs(VECTORSTORE_DIR, exist_ok=True)
 
-# Embeddings for RAG (no new package needed)
+# Embeddings for RAG
 embeddings = HuggingFaceEmbeddings(
     model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
 )
 
 # Hugging Face Arabic model for generation
-HF_MODEL = "aubmindlab/aragpt2-mega"  # Arabic GPT-2 (Egyptian slang)
+HF_MODEL = "aubmindlab/aragpt2-mega"
 tokenizer = AutoTokenizer.from_pretrained(HF_MODEL, trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained(
-    HF_MODEL, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True
-)
-generator = pipeline(
-    "text-generation", model=model, tokenizer=tokenizer, device=0
-)
+model = AutoModelForCausalLM.from_pretrained(HF_MODEL, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True)
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
 
 # Prompt template
 prompt_template = """
@@ -92,7 +88,7 @@ def answer_question(file_key, question):
 # 3️⃣ Gradio interface
 # -----------------------------
 def gradio_interface(file, question):
-    file_key = file.name.replace(".txt", "")
+    file_key = file.name.replace(".txt","")
     file_path = os.path.join(VECTORSTORE_DIR, file.name)
     with open(file_path, "wb") as f:
         f.write(file.read())