fix gr

app.py

@@ -1,77 +1,82 @@
-import os
-from huggingface_hub import hf_hub_download
-from llama_cpp import Llama
-import gradio as gr
-
-# التأكد من أن مجلد كاش هوغينغ فيس قابل للكتابة
-os.makedirs("/home/user/app/data/cache", exist_ok=True)
-
-MODEL_REPO = "QuantFactory/Qwen2.5-7B-Instruct-GGUF"
-MODEL_FILE = "Qwen2.5-7B-Instruct.Q4_K_M.gguf"
-MODEL_PATH = f"/home/user/app/data/cache/{MODEL_FILE}"  # استخدام مجلد الكاش بدلاً من /tmp
-
-# تحميل النموذج إذا لم يكن موجودًا
-if not os.path.exists(MODEL_PATH):
-    hf_hub_download(
-        repo_id=MODEL_REPO,
-        filename=MODEL_FILE,
-        local_dir="/home/user/app/data/cache",
-    )
-
-llm = Llama(
-    model_path=MODEL_PATH,
-    n_ctx=16000,
-    n_threads=6,
-    n_gpu_layers=0,
-    verbose=False
-)
-
-SYSTEM_PROMPT = """<|im_start|>system
-You are Qwen, created by Alibaba Cloud. You are an AI development assistant. Follow these rules:
-1. If request is simple (single file, <50 lines), handle it directly
-2. For complex requests (multiple files, >50 lines), just respond with "CODER"
-3. Always check code for errors before sending
-4. Never execute unsafe code<|im_end|>
-"""
-
-def format_prompt(messages):
-    chat = []
-    for role, content in messages:
-        if role == "system":
-            chat.append(f"<|im_start|>system\n{content}<|im_end|>")
-        elif role == "user":
-            chat.append(f"<|im_start|>user\n{content}<|im_end|>")
-        else:
-            chat.append(f"<|im_start|>assistant\n{content}<|im_end|>")
-    chat.append("<|im_start|>assistant\n")
-    return "\n".join(chat)
-
-def generate_reply(message, history):
-    messages = [("system", SYSTEM_PROMPT.strip())]
-    for user_msg, bot_msg in history:
-        messages.append(("user", user_msg))
-        messages.append(("assistant", bot_msg))
-    messages.append(("user", message))
-    prompt = format_prompt(messages)
-    output = llm(
-        prompt,
-        max_tokens=1024,
-        temperature=0.7,
-        top_p=0.9,
-        repeat_penalty=1.05,
-        stop=["<|im_end|>"]
-    )
-    reply = output["choices"][0]["text"].split("<|im_end|>")[0].strip()
-    history.append((message, reply))
-    return "", history
-
-with gr.Blocks(title="Qwen 2.5 Chat (Q4_K_M)") as demo:
-    gr.Markdown("## 🤖 Qwen 2.5 Chat (Q4_K_M) - Arabic Ready")
-    chatbot = gr.Chatbot(label="المحادثة")
-    with gr.Row():
-        msg = gr.Textbox(placeholder="اكتب سؤالك هنا...", label="س跪الك")
-        clear = gr.Button("مسح المحادثة")
-    msg.submit(generate_reply, [msg, chatbot], [msg, chatbot])
-    clear.click(lambda: ("", []), None, [msg, chatbot])
-
+import os
+from huggingface_hub import hf_hub_download
+from llama_cpp import Llama
+import gradio as gr
+
+# التأكد من أن مجلد كاش هوغينغ فيس قابل للكتابة
+os.makedirs("/home/user/app/data/cache", exist_ok=True)
+
+MODEL_REPO = "QuantFactory/Qwen2.5-7B-Instruct-GGUF"
+MODEL_FILE = "Qwen2.5-7B-Instruct.Q4_K_M.gguf"
+MODEL_PATH = f"/home/user/app/data/cache/{MODEL_FILE}"  # استخدام مجلد الكاش بدلاً من /tmp
+
+# تحميل النموذج إذا لم يكن موجودًا
+if not os.path.exists(MODEL_PATH):
+    hf_hub_download(
+        repo_id=MODEL_REPO,
+        filename=MODEL_FILE,
+        local_dir="/home/user/app/data/cache",
+    )
+
+if os.path.exists(MODEL_PATH):
+    print(f"Model found at {MODEL_PATH}")
+else:
+    print(f"Model not found at {MODEL_PATH}")
+
+llm = Llama(
+    model_path=MODEL_PATH,
+    n_ctx=16000,
+    n_threads=6,
+    n_gpu_layers=0,
+    verbose=False
+)
+
+SYSTEM_PROMPT = """<|im_start|>system
+You are Qwen, created by Alibaba Cloud. You are an AI development assistant. Follow these rules:
+1. If request is simple (single file, <50 lines), handle it directly
+2. For complex requests (multiple files, >50 lines), just respond with "CODER"
+3. Always check code for errors before sending
+4. Never execute unsafe code<|im_end|>
+"""
+
+def format_prompt(messages):
+    chat = []
+    for role, content in messages:
+        if role == "system":
+            chat.append(f"<|im_start|>system\n{content}<|im_end|>")
+        elif role == "user":
+            chat.append(f"<|im_start|>user\n{content}<|im_end|>")
+        else:
+            chat.append(f"<|im_start|>assistant\n{content}<|im_end|>")
+    chat.append("<|im_start|>assistant\n")
+    return "\n".join(chat)
+
+def generate_reply(message, history):
+    messages = [("system", SYSTEM_PROMPT.strip())]
+    for user_msg, bot_msg in history:
+        messages.append(("user", user_msg))
+        messages.append(("assistant", bot_msg))
+    messages.append(("user", message))
+    prompt = format_prompt(messages)
+    output = llm(
+        prompt,
+        max_tokens=1024,
+        temperature=0.7,
+        top_p=0.9,
+        repeat_penalty=1.05,
+        stop=["<|im_end|>"]
+    )
+    reply = output["choices"][0]["text"].split("<|im_end|>")[0].strip()
+    history.append((message, reply))
+    return "", history
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🤖 Qwen 2.5 Chat (Q4_K_M) - Arabic Ready")
+    chatbot = gr.Chatbot(label="المحادثة")
+    with gr.Row():
+        msg = gr.Textbox(placeholder="اكتب سؤالك هنا...", label="س跪الك")
+        clear = gr.Button("مسح المحادثة")
+    msg.submit(generate_reply, [msg, chatbot], [msg, chatbot])
+    clear.click(lambda: ("", []), None, [msg, chatbot])
+
 demo.launch(server_name="0.0.0.0", server_port=7860)