{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from transformers import RobertaModel, RobertaTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the model architecture\n",
    "class RobertaClass(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(RobertaClass, self).__init__()\n",
    "        self.l1 = RobertaModel.from_pretrained(\"roberta-base\")\n",
    "        self.pre_classifier = torch.nn.Linear(768, 768)\n",
    "        self.dropout = torch.nn.Dropout(0.3)\n",
    "        self.classifier = torch.nn.Linear(768, 5)  # Assuming 5 classes\n",
    "\n",
    "    def forward(self, input_ids, attention_mask, token_type_ids):\n",
    "        output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)\n",
    "        hidden_state = output_1[0]\n",
    "        pooler = hidden_state[:, 0]\n",
    "        pooler = self.pre_classifier(pooler)\n",
    "        pooler = torch.nn.ReLU()(pooler)\n",
    "        pooler = self.dropout(pooler)\n",
    "        output = self.classifier(pooler)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of RobertaModel were not initialized from the model checkpoint at roberta-base and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "# Instantiate the model\n",
    "model = RobertaClass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the state dictionary\n",
    "model.load_state_dict(torch.load(r'C:\\Users\\Jash\\OneDrive\\Desktop\\finalmodel2\\pytorch_roberta_sentiment.bin', map_location=torch.device('cpu')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define your inference function to accept multiple inputs\n",
    "def predict(texts):\n",
    "    tokenizer = RobertaTokenizer.from_pretrained('roberta-base')\n",
    "    inputs = tokenizer.batch_encode_plus(\n",
    "        texts,\n",
    "        add_special_tokens=True,\n",
    "        max_length=256,\n",
    "        pad_to_max_length=True,\n",
    "        return_token_type_ids=True,\n",
    "        return_tensors='pt',\n",
    "        truncation=True\n",
    "    )\n",
    "    with torch.no_grad():\n",
    "        outputs = model(inputs['input_ids'], inputs['attention_mask'], inputs['token_type_ids'])\n",
    "    _, predicted = torch.max(outputs, 1)\n",
    "    return predicted.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\raju_\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\transformers\\tokenization_utils_base.py:2619: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Labels: [0, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "# Test your model with multiple inputs'\n",
    "texts_to_predict = [\"check us out\",\"hurry up only 10 pieces left\", \"Congrats, you have won a 50 million dollars, sign up to redeem\"]\n",
    "predicted_labels = predict(texts_to_predict)\n",
    "print(\"Predicted Labels:\", predicted_labels)"
   ]
  }
 ],
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