example pushed

.ipynb_checkpoints/classifier-checkpoint.ipynb

@@ -1,6 +1,117 @@
 {
- "cells": [],
- "metadata": {},
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6afcec5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fastbook\n",
+    "fastbook.setup_book()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4e4f004b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastai.vision.all import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c27d35cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.io\n",
+    "import os\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "1698b023",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Load a MAT file\n",
+    "mat_p = os.path.join(os.getcwd(),'data','cars_annos.mat')\n",
+    "mat = scipy.io.loadmat(mat_p)\n",
+    "annotations = mat['annotations']\n",
+    "class_names = mat['class_names']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "b4402e00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "annotations = np.array(annotations[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "ef6b02ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (7, 1) + inhomogeneous part.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[31], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m annotations \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvectorize(np\u001b[38;5;241m.\u001b[39marray)(annotations)\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\numpy\\lib\\function_base.py:2372\u001b[0m, in \u001b[0;36mvectorize.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2369\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_stage_2(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   2370\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[1;32m-> 2372\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_as_normal(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\numpy\\lib\\function_base.py:2365\u001b[0m, in \u001b[0;36mvectorize._call_as_normal\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2362\u001b[0m     vargs \u001b[38;5;241m=\u001b[39m [args[_i] \u001b[38;5;28;01mfor\u001b[39;00m _i \u001b[38;5;129;01min\u001b[39;00m inds]\n\u001b[0;32m   2363\u001b[0m     vargs\u001b[38;5;241m.\u001b[39mextend([kwargs[_n] \u001b[38;5;28;01mfor\u001b[39;00m _n \u001b[38;5;129;01min\u001b[39;00m names])\n\u001b[1;32m-> 2365\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_vectorize_call(func\u001b[38;5;241m=\u001b[39mfunc, args\u001b[38;5;241m=\u001b[39mvargs)\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\numpy\\lib\\function_base.py:2455\u001b[0m, in \u001b[0;36mvectorize._vectorize_call\u001b[1;34m(self, func, args)\u001b[0m\n\u001b[0;32m   2452\u001b[0m \u001b[38;5;66;03m# Convert args to object arrays first\u001b[39;00m\n\u001b[0;32m   2453\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [asanyarray(a, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mobject\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m args]\n\u001b[1;32m-> 2455\u001b[0m outputs \u001b[38;5;241m=\u001b[39m ufunc(\u001b[38;5;241m*\u001b[39minputs)\n\u001b[0;32m   2457\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ufunc\u001b[38;5;241m.\u001b[39mnout \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   2458\u001b[0m     res \u001b[38;5;241m=\u001b[39m asanyarray(outputs, dtype\u001b[38;5;241m=\u001b[39motypes[\u001b[38;5;241m0\u001b[39m])\n",
+      "\u001b[1;31mValueError\u001b[0m: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (7, 1) + inhomogeneous part."
+     ]
+    }
+   ],
+   "source": [
+    "annotations = np.where(annotations, np.arary(annotations), np.arary(annotations))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73630f27",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:hf]",
+   "language": "python",
+   "name": "conda-env-hf-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

app.py

@@ -14,18 +14,20 @@ def classify_image(image):
     
     # Decode the prediction and get the class name
     class_name = np.random.randint(0,3)
-    classes = ["cat", "dog", "flower"]
+    classes = ["Acura", "Audi", "Dodge"]
     return classes[class_name]
 
 # Sample images for user to choose from
-sample_images = ["cat.jpg", "dog.jpg", "flower.jpg"]
+sample_images = ["AcuraTLType-S2008.jpg", "AudiR8Coupe2012.jpg", "DodgeMagnumWagon2008.jpg"]
 
 iface = gr.Interface(
     fn=classify_image,
-    inputs=gr.inputs.Image(label="Select an image", type="file"),
+    inputs=gr.Image(label="Select an image", type="file",shape=(200, 200)),
     outputs="text",
     live=True,
+    title="Identify the car"
     examples=sample_images
 )
 
+
 iface.launch()