Update app.py

app.py

@@ -1,28 +1,32 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-# اربط بالـ model عبر HuggingFace
+# نعمل client للموديل من Hugging Face
 client = InferenceClient(model="deepseek-ai/DeepSeek-V3.2-Exp", provider="novita")
 
-# دي الدالة اللي هتشتغل كـ API
-def chat_fn(prompt, max_new_tokens=200):
-    try:
-        response = client.text_generation(prompt, max_new_tokens=max_new_tokens)
-        return response
-    except Exception as e:
-        return f"Error: {e}"
+# الفانكشن اللي هتشتغل كـ backend
+def chat_fn(message, history):
+    # نحول الهستوري لنص مرتب
+    context = "\n".join([f"User: {u}\nAI: {a}" for u, a in history])
+    prompt = f"{context}\nUser: {message}\nAI:"
+    
+    response = client.text_generation(prompt, max_new_tokens=200)
+    return response
 
+# واجهة Gradio
 with gr.Blocks() as demo:
-    gr.Markdown("# 🚀 DeepSeek V3.2 Experimental — Chat API")
+    gr.Markdown("## 🤖 DeepSeek Chatbot (API Enabled)")
+    
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="اكتب رسالتك هنا")
+    clear = gr.Button("مسح المحادثة")
 
-    with gr.Row():
-        with gr.Column(scale=2):
-            prompt = gr.Textbox(label="💬 اكتب سؤالك هنا", placeholder="مثال: اشرحلي ميكانيكا الكم ببساطة...")
-            max_tokens = gr.Slider(50, 500, value=200, step=10, label="🔧 Max New Tokens")
-            submit = gr.Button("تشغيل")
-        with gr.Column(scale=3):
-            output = gr.Textbox(label="📝 الرد", interactive=False)
+    def respond(message, history):
+        response = chat_fn(message, history)
+        history.append((message, response))
+        return history, ""
 
-    submit.click(chat_fn, inputs=[prompt, max_tokens], outputs=output)
+    msg.submit(respond, [msg, chatbot], [chatbot, msg])
+    clear.click(lambda: None, None, chatbot, queue=False)
 
 demo.launch()