Add requirements.txt file.

.gitattributes

@@ -33,3 +33,5 @@ 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
+checkpoint-160/optimizer.pt filter=lfs diff=lfs merge=lfs -text
+checkpoint-160/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+ven_bloom_gradio/
+app.ipynb/

app.ipynb

@@ -0,0 +1,223 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mahnaz/mlprojects/bloom_classifier/ven_bloom_gradio/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "import json\n",
+    "from transformers import pipeline\n",
+    "from transformers import AutoImageProcessor\n",
+    "from PIL import Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize\n",
+    "import numpy as np\n",
+    "\n",
+    "def preprocess_input(input_data, image_processor):\n",
+    "    \"\"\"\n",
+    "    Preprocesses the input image for inference.\n",
+    "\n",
+    "    Parameters:\n",
+    "        input_data (str or np.ndarray): Path to the image file in .jpg format or a NumPy array.\n",
+    "        image_processor (AutoImageProcessor): An instance of AutoImageProcessor from the model's checkpoint.\n",
+    "\n",
+    "    Returns:\n",
+    "        processed_img (torch.Tensor): Preprocessed image ready for inference.\n",
+    "    \"\"\"\n",
+    "    # Load the image based on the input type\n",
+    "    if isinstance(input_data, str):\n",
+    "        img = Image.open(input_data).convert('RGB')\n",
+    "    elif isinstance(input_data, np.ndarray):\n",
+    "        img = Image.fromarray(input_data.astype('uint8'), 'RGB')\n",
+    "    else:\n",
+    "        raise ValueError(\"Unsupported input type. Only str and np.ndarray are supported.\")\n",
+    "    \n",
+    "    # Obtain the mean and std from image_processor\n",
+    "    mean = image_processor.image_mean\n",
+    "    std = image_processor.image_std\n",
+    "    \n",
+    "    # Obtain the image size from image_processor\n",
+    "    size = (\n",
+    "        image_processor.size[\"shortest_edge\"]\n",
+    "        if \"shortest_edge\" in image_processor.size\n",
+    "        else (image_processor.size[\"height\"], image_processor.size[\"width\"])\n",
+    "    )\n",
+    "    \n",
+    "    # Define the transformations\n",
+    "    preprocess = Compose([\n",
+    "        Resize(size),  # Resizing to the same size used during training\n",
+    "        CenterCrop(size),  # Center cropping to the same size used during training\n",
+    "        ToTensor(),\n",
+    "        Normalize(mean=mean, std=std)\n",
+    "    ])\n",
+    "    \n",
+    "    # Apply the transformations\n",
+    "    processed_img = preprocess(img)\n",
+    "    \n",
+    "    # Add a batch dimension\n",
+    "    processed_img = processed_img.unsqueeze(0)  # This is necessary because the model expects a batch\n",
+    "       to_pil = ToPILImage()\n",
+    "    processed_img = to_pil(processed_img)\n",
+    "\n",
+    "    return processed_img\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize\n",
+    "\n",
+    "def preprocess_input(image_path, image_processor):\n",
+    "    \"\"\"\n",
+    "    Preprocesses the input image for inference.\n",
+    "\n",
+    "    Parameters:\n",
+    "        image_path (str): Path to the image file in .jpg format.\n",
+    "        image_processor (AutoImageProcessor): An instance of AutoImageProcessor from the model's checkpoint.\n",
+    "\n",
+    "    Returns:\n",
+    "        processed_img (torch.Tensor): Preprocessed image ready for inference.\n",
+    "    \"\"\"\n",
+    "    # Load the image\n",
+    "    img = Image.open(image_path).convert('RGB')\n",
+    "    \n",
+    "    # Obtain the mean and std from image_processor\n",
+    "    mean = image_processor.image_mean\n",
+    "    std = image_processor.image_std\n",
+    "    \n",
+    "    # Obtain the image size from image_processor\n",
+    "    size = (\n",
+    "        image_processor.size[\"shortest_edge\"]\n",
+    "        if \"shortest_edge\" in image_processor.size\n",
+    "        else (image_processor.size[\"height\"], image_processor.size[\"width\"])\n",
+    "    )\n",
+    "    \n",
+    "    # Define the transformations\n",
+    "    preprocess = Compose([\n",
+    "        Resize(size),  # Resizing to the same size used during training\n",
+    "        CenterCrop(size),  # Center cropping to the same size used during training\n",
+    "        ToTensor(),\n",
+    "        Normalize(mean=mean, std=std)\n",
+    "    ])\n",
+    "    \n",
+    "    # Apply the transformations\n",
+    "    processed_img = preprocess(img)\n",
+    "    \n",
+    "    # Add a batch dimension\n",
+    "    processed_img = processed_img.unsqueeze(0)  # This is necessary because the model expects a batch\n",
+    "\n",
+    "    return processed_img\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mahnaz/mlprojects/bloom_classifier/ven_bloom_gradio/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "import json\n",
+    "from transformers import pipeline\n",
+    "\n",
+    "\n",
+    "def load_label_to_name_mapping(json_file_path):\n",
+    "    \"\"\"Load the label-to-name mapping from a JSON file.\"\"\"\n",
+    "    with open(json_file_path, 'r') as f:\n",
+    "        mapping = json.load(f)\n",
+    "    return {int(k): v for k, v in mapping.items()}\n",
+    "\n",
+    "def infer_flower_name(classifier, image):\n",
+    "    \"\"\"Perform inference on an image and return the flower name.\"\"\"\n",
+    "    # Perform inference\n",
+    "    # Load the model checkpoint for inference\n",
+    "    \n",
+    "    result = classifier(image)\n",
+    "    # Get the label from the inference result\n",
+    "    label = result[0]['label'].split('_')[-1]  # The label is usually in the format 'LABEL_#'\n",
+    "    label = int(label)\n",
+    "    \n",
+    "    # Map the integer label to the flower name\n",
+    "    json_file_path = 'label_to_name.json'\n",
+    "    label_to_name = load_label_to_name_mapping(json_file_path)\n",
+    "    flower_name = label_to_name.get(label, \"Unknown\")\n",
+    "    \n",
+    "    return flower_name\n",
+    "\n",
+    "\n",
+    "\n",
+    "def predict(prompt_img):# would call a model to make a prediction on an input and return the output.\n",
+    "\n",
+    "    # Instantiate the AutoImageProcessor\n",
+    "    #image_processor = AutoImageProcessor.from_pretrained(\"google/vit-base-patch16-224-in21k\")\n",
+    "\n",
+    "    # Preprocess the input image\n",
+    "    #image_path = 'path/to/your/image.jpg'\n",
+    "    #processed_img = preprocess_input(prompt_img, image_processor)\n",
+    "    processed_img= prompt_img \n",
+    "    classifier = pipeline(\"image-classification\", model=\"checkpoint-160\")\n",
+    "    flower_name = infer_flower_name(classifier, processed_img)\n",
+    "    return flower_name\n",
+    "demo = gr.Interface(fn=predict, \n",
+    "                    inputs=gr.Image(type=\"pil\"), \n",
+    "                    outputs=gr.Label(num_top_classes=3),\n",
+    "                    examples=[\"example.jpg\"])\n",
+    "\n",
+    "demo.launch()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv_bloom-classifier",
+   "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": "3.11.3"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

checkpoint-160/config.json

@@ -0,0 +1,232 @@
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+  "architectures": [
+    "ViTForImageClassification"
+  ],
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+}

flagged/log.csv

@@ -0,0 +1,2 @@
+flower,output,flag,username,timestamp
+,/home/mahnaz/mlprojects/bloom_classifier/flagged/output/tmpeab6hesr.json,,,2023-09-05 10:48:54.077094

flagged/output/tmpeab6hesr.json

@@ -0,0 +1 @@
+{}

label_to_name.json

@@ -0,0 +1,104 @@
+{
+    "0": "pink primrose",
+    "1": "hard-leaved pocket orchid",
+    "2": "canterbury bells",
+    "3": "sweet pea",
+    "4": "english marigold",
+    "5": "tiger lily",
+    "6": "moon orchid",
+    "7": "bird of paradise",
+    "8": "monkshood",
+    "9": "globe thistle",
+    "10": "snapdragon",
+    "11": "colt's foot",
+    "12": "king protea",
+    "13": "spear thistle",
+    "14": "yellow iris",
+    "15": "globe-flower",
+    "16": "purple coneflower",
+    "17": "peruvian lily",
+    "18": "balloon flower",
+    "19": "giant white arum lily",
+    "20": "fire lily",
+    "21": "pincushion flower",
+    "22": "fritillary",
+    "23": "red ginger",
+    "24": "grape hyacinth",
+    "25": "corn poppy",
+    "26": "prince of wales feathers",
+    "27": "stemless gentian",
+    "28": "artichoke",
+    "29": "sweet william",
+    "30": "carnation",
+    "31": "garden phlox",
+    "32": "love in the mist",
+    "33": "mexican aster",
+    "34": "alpine sea holly",
+    "35": "ruby-lipped cattleya",
+    "36": "cape flower",
+    "37": "great masterwort",
+    "38": "siam tulip",
+    "39": "lenten rose",
+    "40": "barbeton daisy",
+    "41": "daffodil",
+    "42": "sword lily",
+    "43": "poinsettia",
+    "44": "bolero deep blue",
+    "45": "wallflower",
+    "46": "marigold",
+    "47": "buttercup",
+    "48": "oxeye daisy",
+    "49": "common dandelion",
+    "50": "petunia",
+    "51": "wild pansy",
+    "52": "primula",
+    "53": "sunflower",
+    "54": "pelargonium",
+    "55": "bishop of llandaff",
+    "56": "gaura",
+    "57": "geranium",
+    "58": "orange dahlia",
+    "59": "pink-yellow dahlia",
+    "60": "cautleya spicata",
+    "61": "japanese anemone",
+    "62": "black-eyed susan",
+    "63": "silverbush",
+    "64": "californian poppy",
+    "65": "osteospermum",
+    "66": "spring crocus",
+    "67": "bearded iris",
+    "68": "windflower",
+    "69": "tree poppy",
+    "70": "gazania",
+    "71": "azalea",
+    "72": "water lily",
+    "73": "rose",
+    "74": "thorn apple",
+    "75": "morning glory",
+    "76": "passion flower",
+    "77": "lotus lotus",
+    "78": "toad lily",
+    "79": "anthurium",
+    "80": "frangipani",
+    "81": "clematis",
+    "82": "hibiscus",
+    "83": "columbine",
+    "84": "desert-rose",
+    "85": "tree mallow",
+    "86": "magnolia",
+    "87": "cyclamen",
+    "88": "watercress",
+    "89": "canna lily",
+    "90": "hippeastrum",
+    "91": "bee balm",
+    "92": "ball moss",
+    "93": "foxglove",
+    "94": "bougainvillea",
+    "95": "camellia",
+    "96": "mallow",
+    "97": "mexican petunia",
+    "98": "bromelia",
+    "99": "blanket flower",
+    "100": "trumpet creeper",
+    "101": "blackberry lily"
+}