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{
"cells": [
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import json\n",
"from PIL import Image\n",
"import numpy as np\n",
"import gradio as gr "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"from pathlib import Path\n",
"\n",
"import torch\n",
"import torch.nn.functional as F\n",
"\n",
"from src.data.embs import ImageDataset\n",
"from src.model.blip_embs import blip_embs\n",
"\n",
"from demo_chat import Chat"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from src.data.transforms import transform_test\n",
"\n",
"transform = transform_test(384)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def get_blip_config(model=\"base\"):\n",
" config = dict()\n",
" if model == \"base\":\n",
" config[\n",
" \"pretrained\"\n",
" ] = \"https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth \"\n",
" config[\"vit\"] = \"base\"\n",
" config[\"batch_size_train\"] = 32\n",
" config[\"batch_size_test\"] = 16\n",
" config[\"vit_grad_ckpt\"] = True\n",
" config[\"vit_ckpt_layer\"] = 4\n",
" config[\"init_lr\"] = 1e-5\n",
" elif model == \"large\":\n",
" config[\n",
" \"pretrained\"\n",
" ] = \"https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_retrieval_coco.pth\"\n",
" config[\"vit\"] = \"large\"\n",
" config[\"batch_size_train\"] = 16\n",
" config[\"batch_size_test\"] = 32\n",
" config[\"vit_grad_ckpt\"] = True\n",
" config[\"vit_ckpt_layer\"] = 12\n",
" config[\"init_lr\"] = 5e-6\n",
"\n",
" config[\"image_size\"] = 384\n",
" config[\"queue_size\"] = 57600\n",
" config[\"alpha\"] = 0.4\n",
" config[\"k_test\"] = 256\n",
" config[\"negative_all_rank\"] = True\n",
"\n",
" return config"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating model\n",
"load checkpoint from https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_retrieval_coco.pth\n",
"missing keys:\n",
"[]\n"
]
},
{
"data": {
"text/plain": [
"BLIPEmbs(\n",
" (visual_encoder): VisionTransformer(\n",
" (patch_embed): PatchEmbed(\n",
" (proj): Conv2d(3, 1024, kernel_size=(16, 16), stride=(16, 16))\n",
" (norm): Identity()\n",
" )\n",
" (pos_drop): Dropout(p=0.0, inplace=False)\n",
" (blocks): ModuleList(\n",
" (0): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): Identity()\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (1): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): DropPath(drop_prob=0.004)\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (2): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): DropPath(drop_prob=0.009)\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (3): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): DropPath(drop_prob=0.013)\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (4): Block(\n",
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" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): DropPath(drop_prob=0.017)\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (5): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): DropPath(drop_prob=0.022)\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (6): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): DropPath(drop_prob=0.026)\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (7): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
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" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
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" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
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" (8): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
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" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
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" (mlp): Mlp(\n",
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" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
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" (attn): Attention(\n",
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" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
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" (mlp): Mlp(\n",
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" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
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" )\n",
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" )\n",
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" )\n",
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" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (18): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): DropPath(drop_prob=0.078)\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (19): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): DropPath(drop_prob=0.083)\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (20): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): DropPath(drop_prob=0.087)\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (21): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): DropPath(drop_prob=0.091)\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (22): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): DropPath(drop_prob=0.096)\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (23): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): DropPath(drop_prob=0.100)\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU(approximate='none')\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" )\n",
" (text_encoder): BertModel(\n",
" (embeddings): BertEmbeddings(\n",
" (word_embeddings): Embedding(30524, 768, padding_idx=0)\n",
" (position_embeddings): Embedding(512, 768)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (encoder): BertEncoder(\n",
" (layer): ModuleList(\n",
" (0-11): 12 x BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (crossattention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=1024, out_features=768, bias=True)\n",
" (value): Linear(in_features=1024, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" (intermediate_act_fn): GELUActivation()\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (vision_proj): Linear(in_features=1024, out_features=256, bias=True)\n",
" (text_proj): Linear(in_features=768, out_features=256, bias=True)\n",
")"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"Creating model\")\n",
"config = get_blip_config(\"large\")\n",
"\n",
"model = blip_embs(\n",
" pretrained=config[\"pretrained\"],\n",
" image_size=config[\"image_size\"],\n",
" vit=config[\"vit\"],\n",
" vit_grad_ckpt=config[\"vit_grad_ckpt\"],\n",
" vit_ckpt_layer=config[\"vit_ckpt_layer\"],\n",
" queue_size=config[\"queue_size\"],\n",
" negative_all_rank=config[\"negative_all_rank\"],\n",
" )\n",
"\n",
"model = model.to(device)\n",
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_json(\"datasets/sidechef/my_recipes.json\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading Target Embedding\n"
]
}
],
"source": [
"print(\"Loading Target Embedding\")\n",
"tar_img_feats = []\n",
"for _id in df[\"id_\"].tolist(): \n",
" tar_img_feats.append(torch.load(\"datasets/sidechef/blip-embs-large/{:07d}.pth\".format(_id)).unsqueeze(0))\n",
"\n",
"tar_img_feats = torch.cat(tar_img_feats, dim=0)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7866\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7866/\" width=\"100%\" height=\"650px\" 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": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"\n",
"# Define the custom CSS to add a footer\n",
"custom_css = \"\"\"\n",
"/* Footer style */\n",
".gradio-footer {\n",
" display: flex;\n",
" justify-content: center;\n",
" align-items: center;\n",
" padding: 10px;\n",
" background-color: #f8f9fa;\n",
" color: #333;\n",
" font-size: 0.9em;\n",
"}\n",
"\n",
".custom-header {\n",
" text-align: center;\n",
" padding: 12px;\n",
" background-color: #333; \n",
" color: white;\n",
" position: bottom;\n",
" bottom: 0;\n",
" width: 100%;\n",
" font-size: 0.8em;\n",
"}\n",
"\n",
".footer {\n",
" width: 100%;\n",
" background-color: #f2f2f2;\n",
" color: #555;\n",
" text-align: center;\n",
" padding: 10px 0;\n",
" position: absolute;\n",
" bottom: 0;\n",
" left: 0;\n",
"}\n",
"\n",
"/* Make sure the interface leaves space for the footer */\n",
".body {\n",
" margin-bottom: 50px;\n",
"}\n",
"\"\"\"\n",
"\n",
"# Add a custom footer by injecting HTML into the description\n",
"custom_footer_html = \"\"\"\n",
"<footer> <p> Reach out to us at {omkar.thawakar, muzammal.naseer}@mbzuai.ac.ae </p> </footer>\n",
"\"\"\"\n",
"\n",
"custom_header_html = \"\"\"\n",
"<div class='custom-header'>Nutrition-GPT Demo</div>\n",
"\"\"\"\n",
"\n",
"def respond_to_user(image, message):\n",
" # Process the image and message here\n",
" # For demonstration, I'll just return a simple text response\n",
" chat = Chat(model,transform,df,tar_img_feats)\n",
" chat.encode_image(image)\n",
" response = chat.ask(message)\n",
" return response\n",
"\n",
"iface = gr.Interface(\n",
" fn=respond_to_user,\n",
" inputs=[gr.Image(height=\"70%\"), gr.Textbox(label=\"Ask Query\"),],\n",
" outputs=[gr.Textbox(label=\"Nutrition-GPT\")],\n",
" title=custom_header_html, \n",
" # description=\"Upload an food image and ask queries!\",\n",
" css=custom_css,\n",
" # description=custom_footer_html \n",
")\n",
"\n",
"iface.launch(show_error=True, height=\"650px\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# example_texts = gr.Dataset(components=[gr.Textbox(visible=False)],\n",
" # label=\"Prompt Examples\",\n",
" # samples=[\n",
" # [\"Provide nutritional information for given food image.\"],\n",
" # [\"What are the nutrients available in given food image.\"],\n",
" # [\"Could you provide a detailed nutritional data of the given food image?\"],\n",
" # [\"Describe the instructions to prepare given food.\"],\n",
" # [\"What are the key ingredients in this food image?\"],\n",
" # [\"Could you highlight the dietary tags for this food image?\"],\n",
" # ],)\n",
"\n",
"# example_images = gr.Dataset(components=[image], label=\"Food Examples\",\n",
"# samples=[\n",
"# [os.path.join(os.path.dirname(\"./\"), \"./datasets/sidechef/sample_images/0000018.png\")],\n",
"# [os.path.join(os.path.dirname(\"./\"), \"./datasets/sidechef/sample_images/0000021.png\")],\n",
"# [os.path.join(os.path.dirname(\"./\"), \"./datasets/sidechef/sample_images/0000035.png\")],\n",
"# [os.path.join(os.path.dirname(\"./\"), \"./datasets/sidechef/sample_images/0000038.png\")],\n",
"# [os.path.join(os.path.dirname(\"./\"), \"./datasets/sidechef/sample_images/0000090.png\")],\n",
"# [os.path.join(os.path.dirname(\"./\"), \"./datasets/sidechef/sample_images/0000122.png\")],\n",
"# ])\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "covr",
"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": "3.8.18"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|