Upload tahmin_modeli.py

tahmin_modeli.py

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f231fff1-3a4e-439d-adef-5da188d7ce58",
+   "metadata": {},
+   "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",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}