prediction

app.py

@@ -34,8 +34,8 @@ def classify_text(inp):
     # X = vectorizer.fit_transform(inp)
     # reshape the input to 2D
     # convert the input to a numpy array
-    return model.predict( np.array(inp).reshape(1, -1) )
-    # return model.predict( np.array(inp).reshape(1, -1) )[0]
+    # return model.predict( np.array(inp).reshape(1, -1) )
+    return model.predict( np.array(inp).reshape(1, -1) )[0]
 
 
     

retrain.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.7"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}