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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Running on local URL:  http://127.0.0.1:7860\n",
      "* To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import joblib\n",
    "import pandas as pd\n",
    "\n",
    "# Model ve scaler yükleniyor\n",
    "model = joblib.load(\"xgb_model.pkl\")\n",
    "scaler = joblib.load(\"scaler.pkl\")\n",
    "\n",
    "# Tahmin fonksiyonu\n",
    "def predict_quality(pressure, temp_x_pressure, fusion_metric):\n",
    "    input_df = pd.DataFrame([[pressure, temp_x_pressure, fusion_metric]],\n",
    "                            columns=[\"Pressure (kPa)\", \"Temperature x Pressure\", \"Material Fusion Metric\"])\n",
    "    scaled_data = scaler.transform(input_df)\n",
    "    prediction = model.predict(scaled_data)\n",
    "    return f\"🔍 Tahmin Edilen Kalite Skoru: {prediction[0]:.2f}\"\n",
    "\n",
    "# Chatbot formatında cevap veren fonksiyon\n",
    "def respond(press, temp, fusion, chat_history):\n",
    "    result = predict_quality(press, temp, fusion)\n",
    "    chat_history.append({\"role\": \"user\", \"content\": \"Özellikleri girdim.\"})\n",
    "    chat_history.append({\"role\": \"assistant\", \"content\": result})\n",
    "    return chat_history\n",
    "\n",
    "# Arayüz\n",
    "with gr.Blocks() as demo:\n",
    "    gr.Markdown(\"## 🛠️ Kalite Tahmini Chatbotu\")\n",
    "\n",
    "    chatbot = gr.Chatbot(label=\"Kalite Tahmin Chatbotu\", type=\"messages\")\n",
    "    state = gr.State([])\n",
    "\n",
    "    with gr.Row():\n",
    "        pressure = gr.Number(label=\"Pressure (kPa)\")\n",
    "        temp_x_pressure = gr.Number(label=\"Temperature x Pressure\")\n",
    "        fusion_metric = gr.Number(label=\"Material Fusion Metric\")\n",
    "\n",
    "    send_btn = gr.Button(\"Tahmin Et\")\n",
    "\n",
    "    send_btn.click(\n",
    "        fn=respond,\n",
    "        inputs=[pressure, temp_x_pressure, fusion_metric, state],\n",
    "        outputs=[chatbot],\n",
    "        queue=False\n",
    "    ).then(lambda chat: chat, inputs=[chatbot], outputs=[state])\n",
    "\n",
    "demo.launch()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b19e3507-2452-47b3-b404-72706e500516",
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   "outputs": [],
   "source": []
  }
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