final code

models/.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

models/Untitled.ipynb

@@ -0,0 +1,94 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "58c4cfc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "with open(\"spam_model.pkl\", \"rb\") as f:\n",
+    "    loaded_model = pickle.load(f)\n",
+    "predictions = loaded_model.predict([\"You've won a gift!\", \"Hello, how are you?\"])\n",
+    "print(predictions)\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "800879f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run create_model.py\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ff9b9c41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['.ipynb_checkpoints', 'spam_model.pkl', 'Untitled.ipynb']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(os.listdir())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54f97e94",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10353dc5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

models/create_model.py

@@ -0,0 +1,12 @@
+#pickle module is used to serialize (convert a Python object into a byte stream) 
+#and deserialize (convert a byte stream back into a Python object) Python objects. Serialization is useful for saving the state of an object and later restoring it.
+import pickle
+
+
+class DummyModel:
+    def predict(self, texts):
+        return [1 if "win" in text or "gift" in text else 0 for text in texts]
+
+model = DummyModel()
+with open("spam_model.pkl", "wb") as f:
+    pickle.dump(model, f)