adding app

app.py

@@ -0,0 +1,51 @@
+import gradio as gr
+
+from transformers import AutoConfig,ViTImageProcessor,ViTForImageClassification,AutoModel
+import base64
+import os
+
+processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
+model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
+images = 'room.jpg'
+
+
+def image_classifier(image):
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+
+    logits_np = logits.detach().cpu().numpy()
+    logits_args = logits_np.argsort()[0][-3:]
+
+    prediction_classes = [model.config.id2label[predicted_class_idx] for predicted_class_idx in logits_args ]
+    
+    result = {}
+    for i,item in enumerate(prediction_classes):
+        result[item] = logits_np[0][i]
+
+    return result
+
+
+with gr.Blocks(title="Image Classification using Google Vision Transformer") as demo :
+    gr.Markdown(
+    """ 
+    <center>
+    <h1>
+    The Vision Transformer (ViT) 
+    </h1>
+    Transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. 
+    Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.    
+    </center>
+    """
+    )
+    with gr.Row():
+        with gr.Column():
+            # inputt = gr.inputs.Image(shape=(200, 200)),
+            inputt = gr.Image(type="numpy", label="Input Image for Classification")
+            button = gr.Button(value="Classify")
+        with gr.Column():
+            output = gr.Label()
+        button.click(image_classifier,inputt,output)
+
+demo.launch()
+

google_vit.ipynb

@@ -0,0 +1,134 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jarvis/.local/lib/python3.10/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": [
+    "from transformers import ViTImageProcessor, ViTForImageClassification,FlaxViTForImageClassification\n",
+    "from PIL import Image\n",
+    "import requests\n",
+    "from matplotlib import pyplot as plt "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['tiger cat', 'tabby, tabby cat', 'Egyptian cat'] [282 281 285]\n"
+     ]
+    }
+   ],
+   "source": [
+    "url = 'http://images.cocodataset.org/val2017/000000039769.jpg'\n",
+    "image = Image.open(requests.get(url, stream=True).raw)\n",
+    "\n",
+    "processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')\n",
+    "model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')\n",
+    "\n",
+    "inputs = processor(images=image, return_tensors=\"pt\")\n",
+    "outputs = model(**inputs)\n",
+    "logits = outputs.logits\n",
+    "\n",
+    "logits_np = logits.detach().cpu().numpy()\n",
+    "logits_args = logits_np.argsort()[0][-3:]\n",
+    "\n",
+    "prediction_classes = [model.config.id2label[predicted_class_idx] for predicted_class_idx in logits_args ]\n",
+    "print(prediction_classes,logits_args)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'tiger cat': -0.27440035,\n",
+       " 'tabby, tabby cat': 0.8215165,\n",
+       " 'Egyptian cat': -0.08364794}"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result = {}\n",
+    "for i,item in enumerate(prediction_classes):\n",
+    "    result[item] = logits_np[0][i]\n",
+    "\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['tiger cat', 'tabby, tabby cat', 'Egyptian cat']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# model predicts one of the 1000 ImageNet classes\n",
+    "\n",
+    "prediction_classes = [model.config.id2label[predicted_class_idx] for predicted_class_idx in logits_args ]\n",
+    "\n",
+    "prediction_classes\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py_llm",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

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+gradio
+transformers
+torch
+sentencepiece
+huggingface_hub