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"cells": [
{
"cell_type": "code",
"execution_count": 21,
"id": "d7710e8b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n",
"c:\\Users\\User\\Chatbot\\.venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\User\\.cache\\huggingface\\hub\\models--facebook--bart-base. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
"To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
" warnings.warn(message)\n"
]
}
],
"source": [
"from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
"\n",
"facebook_model_name = \"facebook/blenderbot-400M-distill\"\n",
"google_model_name = \"google/flan-t5-base\"\n",
"bart_model_name = \"facebook/bart-base\"\n",
"\n",
"\n",
"# Load the models and tokenizers\n",
"fb_model = AutoModelForSeq2SeqLM.from_pretrained(facebook_model_name)\n",
"google_model = AutoModelForSeq2SeqLM.from_pretrained(google_model_name)\n",
"bart_model = AutoModelForSeq2SeqLM.from_pretrained(bart_model_name)\n",
"\n",
"# Tokenizers\n",
"fb_tokenizer = AutoTokenizer.from_pretrained(facebook_model_name)\n",
"google_tokenizer = AutoTokenizer.from_pretrained(google_model_name)\n",
"bart_tokenizer= AutoTokenizer.from_pretrained(bart_model_name)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "2f852aa5",
"metadata": {},
"outputs": [],
"source": [
"# define chat function\n",
"def chat_with_bots(input_text, model_name=\"google\"):\n",
" if model_name == \"google\":\n",
" inputs = google_tokenizer.encode(input_text, return_tensors='pt')\n",
" outputs = google_model.generate(inputs, max_new_tokens=1000)\n",
" response = google_tokenizer.decode(\n",
" outputs[0], \n",
" skip_special_tokens=True\n",
" ).strip()\n",
" \n",
" return response\n",
" elif model_name == \"facebook\":\n",
" inputs = fb_tokenizer.encode(input_text, return_tensors='pt')\n",
" outputs = fb_model.generate(inputs, max_new_tokens=1000)\n",
" response = fb_tokenizer.decode(\n",
" outputs[0], \n",
" skip_special_tokens=True\n",
" ).strip()\n",
" return response\n",
" \n",
" elif model_name == \"bart\":\n",
" inputs = bart_tokenizer.encode(input_text, return_tensors='pt')\n",
" outputs = bart_model.generate(inputs, max_new_tokens=1000)\n",
" response = bart_tokenizer.decode(\n",
" outputs[0], \n",
" skip_special_tokens=True\n",
" ).strip()\n",
" return response\n",
" else: \n",
" response = \"No model selected\"\n",
" return response"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "568e3555",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hi, my name is samantha. How are you doing this fine evening?\n"
]
}
],
"source": [
"print(chat_with_bots(\"hi, what your name\", model_name=\"facebook\"))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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