initial commit

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+best-model.ckpt filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,340 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e0102cb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Global seed set to 100\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import T5Tokenizer, T5ForConditionalGeneration  \n",
+    "\n",
+    "from transformers import AdamW\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import pytorch_lightning as pl\n",
+    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
+    "from torch.nn.utils.rnn import pad_sequence\n",
+    "# from torch.utils.data import Dataset, DataLoader, random_split, RandomSampler, SequentialSampler\n",
+    "\n",
+    "pl.seed_everything(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1ec5ec2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_NAME='t5-base'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8044c622",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+    "INPUT_MAX_LEN = 128 \n",
+    "OUTPUT_MAX_LEN = 128"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6390f2de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME, model_max_length=512)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8eec35d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class T5Model(pl.LightningModule):\n",
+    "    \n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        self.model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME, return_dict = True)\n",
+    "\n",
+    "        \n",
+    "    def forward(self, input_ids, attention_mask, labels=None):\n",
+    "        \n",
+    "        output = self.model(\n",
+    "        input_ids=input_ids, \n",
+    "        attention_mask=attention_mask, \n",
+    "        labels=labels\n",
+    "        )\n",
+    "        return output.loss, output.logits\n",
+    "    \n",
+    "    def training_step(self, batch, batch_idx):\n",
+    "\n",
+    "        input_ids = batch[\"input_ids\"]\n",
+    "        attention_mask = batch[\"attention_mask\"]\n",
+    "        labels= batch[\"target\"]\n",
+    "        loss, logits = self(input_ids , attention_mask, labels)\n",
+    "\n",
+    "        \n",
+    "        self.log(\"train_loss\", loss, prog_bar=True, logger=True)\n",
+    "\n",
+    "        return {'loss': loss}\n",
+    "    \n",
+    "    def validation_step(self, batch, batch_idx):\n",
+    "        input_ids = batch[\"input_ids\"]\n",
+    "        attention_mask = batch[\"attention_mask\"]\n",
+    "        labels= batch[\"target\"]\n",
+    "        loss, logits = self(input_ids, attention_mask, labels)\n",
+    "\n",
+    "        self.log(\"val_loss\", loss, prog_bar=True, logger=True)\n",
+    "        \n",
+    "        return {'val_loss': loss}\n",
+    "\n",
+    "    def configure_optimizers(self):\n",
+    "        return AdamW(self.parameters(), lr=0.0001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9d96844",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Lightning automatically upgraded your loaded checkpoint from v1.9.3 to v2.0.2. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file F:\\Projects & Open_source\\Chatbot_T5_kaggle\\best-model.ckpt`\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_model = T5Model.load_from_checkpoint('best-model.ckpt',map_location=DEVICE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3449943f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_model.freeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0e9f1058",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_question(question):\n",
+    "\n",
+    "    inputs_encoding =  tokenizer(\n",
+    "        question,\n",
+    "        add_special_tokens=True,\n",
+    "        max_length= INPUT_MAX_LEN,\n",
+    "        padding = 'max_length',\n",
+    "        truncation='only_first',\n",
+    "        return_attention_mask=True,\n",
+    "        return_tensors=\"pt\"\n",
+    "        )\n",
+    "\n",
+    "    \n",
+    "    generate_ids = train_model.model.generate(\n",
+    "        input_ids = inputs_encoding[\"input_ids\"],\n",
+    "        attention_mask = inputs_encoding[\"attention_mask\"],\n",
+    "        max_length = INPUT_MAX_LEN,\n",
+    "        num_beams = 4,\n",
+    "        num_return_sequences = 1,\n",
+    "        no_repeat_ngram_size=2,\n",
+    "        early_stopping=True,\n",
+    "        )\n",
+    "\n",
+    "    preds = [\n",
+    "        tokenizer.decode(gen_id,\n",
+    "        skip_special_tokens=True, \n",
+    "        clean_up_tokenization_spaces=True)\n",
+    "        for gen_id in generate_ids\n",
+    "    ]\n",
+    "\n",
+    "    return \"\".join(preds)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ee38a88c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ques:  hi, how are you doing?\n",
+      "BOT:  i'm so glad you're doing well.\n"
+     ]
+    }
+   ],
+   "source": [
+    "ques = \"hi, how are you doing?\"\n",
+    "print(\"Ques: \",ques)\n",
+    "print(\"BOT: \",generate_question(ques))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "22aa4414",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7861\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7861/\" 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": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "import random\n",
+    "import time\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    chatbot = gr.Chatbot()\n",
+    "    gr.Chatbot.style(chatbot,height=400)\n",
+    "    msg = gr.Textbox(info=\"Press \\'Enter\\' to send\")\n",
+    "    clear = gr.Button(\"Clear\")\n",
+    "\n",
+    "    def user(user_message, history):\n",
+    "        return \"\", history + [[user_message, None]]\n",
+    "\n",
+    "    def bot(history):\n",
+    "        bot_message = generate_question(history[-1][0])\n",
+    "        history[-1][1] = \"\"\n",
+    "        for character in bot_message:\n",
+    "            history[-1][1] += character\n",
+    "            time.sleep(0.05)\n",
+    "            yield history\n",
+    "\n",
+    "    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=True).then(\n",
+    "        bot, chatbot, chatbot\n",
+    "    )\n",
+    "    clear.click(lambda: None, None, chatbot, queue=True)\n",
+    "\n",
+    "demo.queue(concurrency_count=2)\n",
+    "demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fef38bdc",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "import random\n",
+    "import time\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    chatbot = gr.Chatbot()\n",
+    "    msg = gr.Textbox(placeholder='Got any spare time...let\\'s chat!!!')\n",
+    "    gr.Textbox.style(msg,show_copy_button=True)\n",
+    "    clear = gr.Button(\"Clear\")\n",
+    "\n",
+    "    def respond(message, chat_history):\n",
+    "        bot_message = generate_question(message)\n",
+    "        bot_message = \"**\"+bot_message+\"**\"\n",
+    "        chat_history.append((message, bot_message))\n",
+    "        time.sleep(1)\n",
+    "        return \"\", chat_history\n",
+    "\n",
+    "    msg.submit(respond, [msg, chatbot], [msg, chatbot])\n",
+    "    clear.click(lambda: None, None, chatbot, queue=False)\n",
+    "\n",
+    "demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a86d446a",
+   "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": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Untitled.ipynb

@@ -0,0 +1,340 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e0102cb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Global seed set to 100\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import T5Tokenizer, T5ForConditionalGeneration  \n",
+    "\n",
+    "from transformers import AdamW\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import pytorch_lightning as pl\n",
+    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
+    "from torch.nn.utils.rnn import pad_sequence\n",
+    "# from torch.utils.data import Dataset, DataLoader, random_split, RandomSampler, SequentialSampler\n",
+    "\n",
+    "pl.seed_everything(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1ec5ec2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_NAME='t5-base'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8044c622",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+    "INPUT_MAX_LEN = 128 \n",
+    "OUTPUT_MAX_LEN = 128"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6390f2de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME, model_max_length=512)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8eec35d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class T5Model(pl.LightningModule):\n",
+    "    \n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        self.model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME, return_dict = True)\n",
+    "\n",
+    "        \n",
+    "    def forward(self, input_ids, attention_mask, labels=None):\n",
+    "        \n",
+    "        output = self.model(\n",
+    "        input_ids=input_ids, \n",
+    "        attention_mask=attention_mask, \n",
+    "        labels=labels\n",
+    "        )\n",
+    "        return output.loss, output.logits\n",
+    "    \n",
+    "    def training_step(self, batch, batch_idx):\n",
+    "\n",
+    "        input_ids = batch[\"input_ids\"]\n",
+    "        attention_mask = batch[\"attention_mask\"]\n",
+    "        labels= batch[\"target\"]\n",
+    "        loss, logits = self(input_ids , attention_mask, labels)\n",
+    "\n",
+    "        \n",
+    "        self.log(\"train_loss\", loss, prog_bar=True, logger=True)\n",
+    "\n",
+    "        return {'loss': loss}\n",
+    "    \n",
+    "    def validation_step(self, batch, batch_idx):\n",
+    "        input_ids = batch[\"input_ids\"]\n",
+    "        attention_mask = batch[\"attention_mask\"]\n",
+    "        labels= batch[\"target\"]\n",
+    "        loss, logits = self(input_ids, attention_mask, labels)\n",
+    "\n",
+    "        self.log(\"val_loss\", loss, prog_bar=True, logger=True)\n",
+    "        \n",
+    "        return {'val_loss': loss}\n",
+    "\n",
+    "    def configure_optimizers(self):\n",
+    "        return AdamW(self.parameters(), lr=0.0001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9d96844",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Lightning automatically upgraded your loaded checkpoint from v1.9.3 to v2.0.2. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file F:\\Projects & Open_source\\Chatbot_T5_kaggle\\best-model.ckpt`\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_model = T5Model.load_from_checkpoint('best-model.ckpt',map_location=DEVICE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3449943f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_model.freeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0e9f1058",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_question(question):\n",
+    "\n",
+    "    inputs_encoding =  tokenizer(\n",
+    "        question,\n",
+    "        add_special_tokens=True,\n",
+    "        max_length= INPUT_MAX_LEN,\n",
+    "        padding = 'max_length',\n",
+    "        truncation='only_first',\n",
+    "        return_attention_mask=True,\n",
+    "        return_tensors=\"pt\"\n",
+    "        )\n",
+    "\n",
+    "    \n",
+    "    generate_ids = train_model.model.generate(\n",
+    "        input_ids = inputs_encoding[\"input_ids\"],\n",
+    "        attention_mask = inputs_encoding[\"attention_mask\"],\n",
+    "        max_length = INPUT_MAX_LEN,\n",
+    "        num_beams = 4,\n",
+    "        num_return_sequences = 1,\n",
+    "        no_repeat_ngram_size=2,\n",
+    "        early_stopping=True,\n",
+    "        )\n",
+    "\n",
+    "    preds = [\n",
+    "        tokenizer.decode(gen_id,\n",
+    "        skip_special_tokens=True, \n",
+    "        clean_up_tokenization_spaces=True)\n",
+    "        for gen_id in generate_ids\n",
+    "    ]\n",
+    "\n",
+    "    return \"\".join(preds)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ee38a88c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ques:  hi, how are you doing?\n",
+      "BOT:  i'm so glad you're doing well.\n"
+     ]
+    }
+   ],
+   "source": [
+    "ques = \"hi, how are you doing?\"\n",
+    "print(\"Ques: \",ques)\n",
+    "print(\"BOT: \",generate_question(ques))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "22aa4414",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7861\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7861/\" 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": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "import random\n",
+    "import time\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    chatbot = gr.Chatbot()\n",
+    "    gr.Chatbot.style(chatbot,height=400)\n",
+    "    msg = gr.Textbox(info=\"Press \\'Enter\\' to send\")\n",
+    "    clear = gr.Button(\"Clear\")\n",
+    "\n",
+    "    def user(user_message, history):\n",
+    "        return \"\", history + [[user_message, None]]\n",
+    "\n",
+    "    def bot(history):\n",
+    "        bot_message = generate_question(history[-1][0])\n",
+    "        history[-1][1] = \"\"\n",
+    "        for character in bot_message:\n",
+    "            history[-1][1] += character\n",
+    "            time.sleep(0.05)\n",
+    "            yield history\n",
+    "\n",
+    "    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=True).then(\n",
+    "        bot, chatbot, chatbot\n",
+    "    )\n",
+    "    clear.click(lambda: None, None, chatbot, queue=True)\n",
+    "\n",
+    "demo.queue(concurrency_count=2)\n",
+    "demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fef38bdc",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "import random\n",
+    "import time\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    chatbot = gr.Chatbot()\n",
+    "    msg = gr.Textbox(placeholder='Got any spare time...let\\'s chat!!!')\n",
+    "    gr.Textbox.style(msg,show_copy_button=True)\n",
+    "    clear = gr.Button(\"Clear\")\n",
+    "\n",
+    "    def respond(message, chat_history):\n",
+    "        bot_message = generate_question(message)\n",
+    "        bot_message = \"**\"+bot_message+\"**\"\n",
+    "        chat_history.append((message, bot_message))\n",
+    "        time.sleep(1)\n",
+    "        return \"\", chat_history\n",
+    "\n",
+    "    msg.submit(respond, [msg, chatbot], [msg, chatbot])\n",
+    "    clear.click(lambda: None, None, chatbot, queue=False)\n",
+    "\n",
+    "demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a86d446a",
+   "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": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.py

@@ -0,0 +1,123 @@
+from transformers import T5Tokenizer, T5ForConditionalGeneration  
+
+from transformers import AdamW
+import pandas as pd
+import torch
+import pytorch_lightning as pl
+from pytorch_lightning.callbacks import ModelCheckpoint
+from torch.nn.utils.rnn import pad_sequence
+# from torch.utils.data import Dataset, DataLoader, random_split, RandomSampler, SequentialSampler
+
+pl.seed_everything(100)
+
+MODEL_NAME='t5-base'
+DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+INPUT_MAX_LEN = 128 
+OUTPUT_MAX_LEN = 128
+
+tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME, model_max_length=512)
+
+class T5Model(pl.LightningModule):
+    
+    def __init__(self):
+        super().__init__()
+        self.model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME, return_dict = True)
+
+        
+    def forward(self, input_ids, attention_mask, labels=None):
+        
+        output = self.model(
+        input_ids=input_ids, 
+        attention_mask=attention_mask, 
+        labels=labels
+        )
+        return output.loss, output.logits
+    
+    def training_step(self, batch, batch_idx):
+
+        input_ids = batch["input_ids"]
+        attention_mask = batch["attention_mask"]
+        labels= batch["target"]
+        loss, logits = self(input_ids , attention_mask, labels)
+
+        
+        self.log("train_loss", loss, prog_bar=True, logger=True)
+
+        return {'loss': loss}
+    
+    def validation_step(self, batch, batch_idx):
+        input_ids = batch["input_ids"]
+        attention_mask = batch["attention_mask"]
+        labels= batch["target"]
+        loss, logits = self(input_ids, attention_mask, labels)
+
+        self.log("val_loss", loss, prog_bar=True, logger=True)
+        
+        return {'val_loss': loss}
+
+    def configure_optimizers(self):
+        return AdamW(self.parameters(), lr=0.0001)
+
+train_model = T5Model.load_from_checkpoint('best-model.ckpt',map_location=DEVICE)
+train_model.freeze()
+
+def generate_question(question):
+
+    inputs_encoding =  tokenizer(
+        question,
+        add_special_tokens=True,
+        max_length= INPUT_MAX_LEN,
+        padding = 'max_length',
+        truncation='only_first',
+        return_attention_mask=True,
+        return_tensors="pt"
+        )
+
+    
+    generate_ids = train_model.model.generate(
+        input_ids = inputs_encoding["input_ids"],
+        attention_mask = inputs_encoding["attention_mask"],
+        max_length = INPUT_MAX_LEN,
+        num_beams = 4,
+        num_return_sequences = 1,
+        no_repeat_ngram_size=2,
+        early_stopping=True,
+        )
+
+    preds = [
+        tokenizer.decode(gen_id,
+        skip_special_tokens=True, 
+        clean_up_tokenization_spaces=True)
+        for gen_id in generate_ids
+    ]
+
+    return "".join(preds)
+
+import gradio as gr
+import random
+import time
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    gr.Chatbot.style(chatbot,height=400)
+    msg = gr.Textbox(info="Press \'Enter\' to send")
+    clear = gr.Button("Clear")
+
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+
+    def bot(history):
+        bot_message = generate_question(history[-1][0])
+        history[-1][1] = ""
+        for character in bot_message:
+            history[-1][1] += character
+            time.sleep(0.05)
+            yield history
+
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=True).then(
+        bot, chatbot, chatbot
+    )
+    clear.click(lambda: None, None, chatbot, queue=True)
+
+demo.queue(concurrency_count=2)
+demo.launch()

best-model.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d21c48743863e3b7355f0a432cf82b794091fa3ff2ad94c630bb3e9e2975b13
+size 2675123255

requirements.txt

@@ -0,0 +1,5 @@
+transformers==4.27.4
+pandas==1.5.3
+torch==2.0.0
+pytorch-lightning==2.0.2
+gradio==3.24.1