Add application file

.gitattributes

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+*.p filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/classifier-checkpoint.ipynb

@@ -0,0 +1,427 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4e1efc4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: streamlit in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (1.13.0)\n",
+      "Requirement already satisfied: watchdog in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (2.1.9)\n",
+      "Requirement already satisfied: importlib-metadata>=1.4 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (5.0.0)\n",
+      "Requirement already satisfied: tzlocal>=1.1 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (4.2)\n",
+      "Requirement already satisfied: altair>=3.2.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (4.2.0)\n",
+      "Requirement already satisfied: semver in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (2.13.0)\n",
+      "Requirement already satisfied: gitpython!=3.1.19 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (3.1.29)\n",
+      "Requirement already satisfied: numpy in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (1.22.4)\n",
+      "Requirement already satisfied: pydeck>=0.1.dev5 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (0.8.0b4)\n",
+      "Requirement already satisfied: pympler>=0.9 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (1.0.1)\n",
+      "Requirement already satisfied: blinker>=1.0.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (1.5)\n",
+      "Requirement already satisfied: toml in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (0.10.2)\n",
+      "Requirement already satisfied: click>=7.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (8.0.3)\n",
+      "Requirement already satisfied: typing-extensions>=3.10.0.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (4.4.0)\n",
+      "Requirement already satisfied: pandas>=0.21.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (1.4.4)\n",
+      "Requirement already satisfied: cachetools>=4.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (4.2.4)\n",
+      "Requirement already satisfied: tornado>=5.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (6.2)\n",
+      "Requirement already satisfied: python-dateutil in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (2.8.2)\n",
+      "Requirement already satisfied: packaging>=14.1 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (21.3)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (8.4.0)\n",
+      "Requirement already satisfied: validators>=0.2 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (0.20.0)\n",
+      "Requirement already satisfied: protobuf!=3.20.2,<4,>=3.12 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (3.19.1)\n",
+      "Requirement already satisfied: requests>=2.4 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (2.26.0)\n",
+      "Requirement already satisfied: pyarrow>=4.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (9.0.0)\n",
+      "Requirement already satisfied: rich>=10.11.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (12.6.0)\n",
+      "Requirement already satisfied: jsonschema>=3.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from altair>=3.2.0->streamlit) (4.2.1)\n",
+      "Requirement already satisfied: toolz in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from altair>=3.2.0->streamlit) (0.12.0)\n",
+      "Requirement already satisfied: jinja2 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from altair>=3.2.0->streamlit) (3.0.3)\n",
+      "Requirement already satisfied: entrypoints in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from altair>=3.2.0->streamlit) (0.4)\n",
+      "Requirement already satisfied: colorama in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from click>=7.0->streamlit) (0.4.4)\n",
+      "Requirement already satisfied: gitdb<5,>=4.0.1 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from gitpython!=3.1.19->streamlit) (4.0.9)\n",
+      "Requirement already satisfied: zipp>=0.5 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from importlib-metadata>=1.4->streamlit) (3.9.0)\n",
+      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from packaging>=14.1->streamlit) (3.0.6)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from pandas>=0.21.0->streamlit) (2022.4)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from python-dateutil->streamlit) (1.15.0)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from requests>=2.4->streamlit) (2021.10.8)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from requests>=2.4->streamlit) (3.3)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from requests>=2.4->streamlit) (1.26.7)\n",
+      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from requests>=2.4->streamlit) (2.0.7)\n",
+      "Requirement already satisfied: commonmark<0.10.0,>=0.9.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from rich>=10.11.0->streamlit) (0.9.1)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.6.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from rich>=10.11.0->streamlit) (2.13.0)\n",
+      "Requirement already satisfied: pytz-deprecation-shim in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from tzlocal>=1.1->streamlit) (0.1.0.post0)\n",
+      "Requirement already satisfied: tzdata in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from tzlocal>=1.1->streamlit) (2022.5)\n",
+      "Requirement already satisfied: decorator>=3.4.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from validators>=0.2->streamlit) (5.1.1)\n",
+      "Requirement already satisfied: smmap<6,>=3.0.1 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from gitdb<5,>=4.0.1->gitpython!=3.1.19->streamlit) (5.0.0)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from jinja2->altair>=3.2.0->streamlit) (2.0.1)\n",
+      "Requirement already satisfied: attrs>=17.4.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from jsonschema>=3.0->altair>=3.2.0->streamlit) (21.2.0)\n",
+      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from jsonschema>=3.0->altair>=3.2.0->streamlit) (0.18.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install streamlit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bd9bda9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Preprocessing\n",
+    "\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from skimage.io import imread\n",
+    "from skimage.transform import resize\n",
+    "\n",
+    "target = []\n",
+    "images = []\n",
+    "flat_data = []\n",
+    "\n",
+    "DataDir = r\"C:\\\\Users\\\\smile\\\\streamlit\\\\kerang\\images\\\\\"\n",
+    "\n",
+    "Categories = ['kerang bulu','kerang darah']\n",
+    "\n",
+    "for category in Categories:\n",
+    "  class_num = Categories.index(category) #label encoding\n",
+    "  path = os.path.join(DataDir,category)\n",
+    "  for img in os.listdir(path):\n",
+    "    img_array = imread(os.path.join(path,img))\n",
+    "    #print(img_array.shape)\n",
+    "    #plt.imshow(img_array)\n",
+    "    img_resized = resize(img_array,(150,150,3))\n",
+    "    flat_data.append(img_resized.flatten())\n",
+    "    images.append(img_resized)\n",
+    "    target.append(class_num)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9f678495",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flat_data = np.array(flat_data)\n",
+    "target = np.array(target)\n",
+    "images = np.array(images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b49a7796",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "21e8ec5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 2 artists>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique,count = np.unique(target,return_counts=True)\n",
+    "plt.bar(Categories,count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f38f75f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train,x_test,y_train,y_test = train_test_split(flat_data,target,test_size=0.3,random_state=109)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bb2fdb07",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVC(probability=True),\n",
+       "             param_grid=[{&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;kernel&#x27;: [&#x27;linear&#x27;]},\n",
+       "                         {&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;gamma&#x27;: [0.001, 0.0001],\n",
+       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=SVC(probability=True),\n",
+       "             param_grid=[{&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;kernel&#x27;: [&#x27;linear&#x27;]},\n",
+       "                         {&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;gamma&#x27;: [0.001, 0.0001],\n",
+       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True)</pre></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(estimator=SVC(probability=True),\n",
+       "             param_grid=[{'C': [1, 10, 100, 1000], 'kernel': ['linear']},\n",
+       "                         {'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001],\n",
+       "                          'kernel': ['rbf']}])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn import svm\n",
+    "\n",
+    "param_grid = [\n",
+    "              {'C':[1,10,100,1000],'kernel':['linear']},\n",
+    "              {'C':[1,10,100,1000],'gamma':[0.001,0.0001],'kernel':['rbf']},\n",
+    "]\n",
+    "\n",
+    "svc= svm.SVC(probability = True)\n",
+    "clf= GridSearchCV(svc,param_grid)\n",
+    "clf.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d0f2de2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0,\n",
+       "       1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1,\n",
+       "       0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
+       "       0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
+       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,\n",
+       "       1, 0, 0, 1, 1, 1, 0, 0, 1, 1])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = clf.predict(x_test)\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "38f8aaa6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Predictions ')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(y_test, y_pred)\n",
+    "plt.xlabel('True Values ')\n",
+    "plt.ylabel('Predictions ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "25eddb36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score,confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4db1eed0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.75"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_pred,y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d290475a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[33, 15],\n",
+       "       [15, 57]], dtype=int64)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_pred,y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "14aab603",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "pickle.dump(clf,open('img_model.p','wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "85cdab52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pickle.load(open('img_model.p','rb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d8c08e5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8a5641a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c10acb46",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "45e276b2269ca886a52a01c5a501673ce5f4d011e4c9858542c33b75979f0135"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Procfile

@@ -0,0 +1 @@
+web: sh setup.sh && streamlit run imgapp.py

classifier.ipynb

@@ -0,0 +1,427 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4e1efc4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: streamlit in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (1.13.0)\n",
+      "Requirement already satisfied: watchdog in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (2.1.9)\n",
+      "Requirement already satisfied: importlib-metadata>=1.4 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (5.0.0)\n",
+      "Requirement already satisfied: tzlocal>=1.1 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (4.2)\n",
+      "Requirement already satisfied: altair>=3.2.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (4.2.0)\n",
+      "Requirement already satisfied: semver in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (2.13.0)\n",
+      "Requirement already satisfied: gitpython!=3.1.19 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (3.1.29)\n",
+      "Requirement already satisfied: numpy in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (1.22.4)\n",
+      "Requirement already satisfied: pydeck>=0.1.dev5 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (0.8.0b4)\n",
+      "Requirement already satisfied: pympler>=0.9 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (1.0.1)\n",
+      "Requirement already satisfied: blinker>=1.0.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (1.5)\n",
+      "Requirement already satisfied: toml in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (0.10.2)\n",
+      "Requirement already satisfied: click>=7.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (8.0.3)\n",
+      "Requirement already satisfied: typing-extensions>=3.10.0.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (4.4.0)\n",
+      "Requirement already satisfied: pandas>=0.21.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (1.4.4)\n",
+      "Requirement already satisfied: cachetools>=4.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (4.2.4)\n",
+      "Requirement already satisfied: tornado>=5.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (6.2)\n",
+      "Requirement already satisfied: python-dateutil in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (2.8.2)\n",
+      "Requirement already satisfied: packaging>=14.1 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (21.3)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (8.4.0)\n",
+      "Requirement already satisfied: validators>=0.2 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (0.20.0)\n",
+      "Requirement already satisfied: protobuf!=3.20.2,<4,>=3.12 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (3.19.1)\n",
+      "Requirement already satisfied: requests>=2.4 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (2.26.0)\n",
+      "Requirement already satisfied: pyarrow>=4.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (9.0.0)\n",
+      "Requirement already satisfied: rich>=10.11.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from streamlit) (12.6.0)\n",
+      "Requirement already satisfied: jsonschema>=3.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from altair>=3.2.0->streamlit) (4.2.1)\n",
+      "Requirement already satisfied: toolz in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from altair>=3.2.0->streamlit) (0.12.0)\n",
+      "Requirement already satisfied: jinja2 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from altair>=3.2.0->streamlit) (3.0.3)\n",
+      "Requirement already satisfied: entrypoints in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from altair>=3.2.0->streamlit) (0.4)\n",
+      "Requirement already satisfied: colorama in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from click>=7.0->streamlit) (0.4.4)\n",
+      "Requirement already satisfied: gitdb<5,>=4.0.1 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from gitpython!=3.1.19->streamlit) (4.0.9)\n",
+      "Requirement already satisfied: zipp>=0.5 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from importlib-metadata>=1.4->streamlit) (3.9.0)\n",
+      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from packaging>=14.1->streamlit) (3.0.6)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from pandas>=0.21.0->streamlit) (2022.4)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from python-dateutil->streamlit) (1.15.0)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from requests>=2.4->streamlit) (2021.10.8)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from requests>=2.4->streamlit) (3.3)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from requests>=2.4->streamlit) (1.26.7)\n",
+      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from requests>=2.4->streamlit) (2.0.7)\n",
+      "Requirement already satisfied: commonmark<0.10.0,>=0.9.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from rich>=10.11.0->streamlit) (0.9.1)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.6.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from rich>=10.11.0->streamlit) (2.13.0)\n",
+      "Requirement already satisfied: pytz-deprecation-shim in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from tzlocal>=1.1->streamlit) (0.1.0.post0)\n",
+      "Requirement already satisfied: tzdata in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from tzlocal>=1.1->streamlit) (2022.5)\n",
+      "Requirement already satisfied: decorator>=3.4.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from validators>=0.2->streamlit) (5.1.1)\n",
+      "Requirement already satisfied: smmap<6,>=3.0.1 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from gitdb<5,>=4.0.1->gitpython!=3.1.19->streamlit) (5.0.0)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from jinja2->altair>=3.2.0->streamlit) (2.0.1)\n",
+      "Requirement already satisfied: attrs>=17.4.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from jsonschema>=3.0->altair>=3.2.0->streamlit) (21.2.0)\n",
+      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in c:\\users\\smile\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from jsonschema>=3.0->altair>=3.2.0->streamlit) (0.18.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install streamlit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bd9bda9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Preprocessing\n",
+    "\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from skimage.io import imread\n",
+    "from skimage.transform import resize\n",
+    "\n",
+    "target = []\n",
+    "images = []\n",
+    "flat_data = []\n",
+    "\n",
+    "DataDir = r\"C:\\\\Users\\\\smile\\\\streamlit\\\\kerang\\images\\\\\"\n",
+    "\n",
+    "Categories = ['kerang bulu','kerang darah']\n",
+    "\n",
+    "for category in Categories:\n",
+    "  class_num = Categories.index(category) #label encoding\n",
+    "  path = os.path.join(DataDir,category)\n",
+    "  for img in os.listdir(path):\n",
+    "    img_array = imread(os.path.join(path,img))\n",
+    "    #print(img_array.shape)\n",
+    "    #plt.imshow(img_array)\n",
+    "    img_resized = resize(img_array,(150,150,3))\n",
+    "    flat_data.append(img_resized.flatten())\n",
+    "    images.append(img_resized)\n",
+    "    target.append(class_num)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9f678495",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flat_data = np.array(flat_data)\n",
+    "target = np.array(target)\n",
+    "images = np.array(images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b49a7796",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "21e8ec5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 2 artists>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique,count = np.unique(target,return_counts=True)\n",
+    "plt.bar(Categories,count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f38f75f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train,x_test,y_train,y_test = train_test_split(flat_data,target,test_size=0.3,random_state=109)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bb2fdb07",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVC(probability=True),\n",
+       "             param_grid=[{&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;kernel&#x27;: [&#x27;linear&#x27;]},\n",
+       "                         {&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;gamma&#x27;: [0.001, 0.0001],\n",
+       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=SVC(probability=True),\n",
+       "             param_grid=[{&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;kernel&#x27;: [&#x27;linear&#x27;]},\n",
+       "                         {&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;gamma&#x27;: [0.001, 0.0001],\n",
+       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True)</pre></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(estimator=SVC(probability=True),\n",
+       "             param_grid=[{'C': [1, 10, 100, 1000], 'kernel': ['linear']},\n",
+       "                         {'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001],\n",
+       "                          'kernel': ['rbf']}])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn import svm\n",
+    "\n",
+    "param_grid = [\n",
+    "              {'C':[1,10,100,1000],'kernel':['linear']},\n",
+    "              {'C':[1,10,100,1000],'gamma':[0.001,0.0001],'kernel':['rbf']},\n",
+    "]\n",
+    "\n",
+    "svc= svm.SVC(probability = True)\n",
+    "clf= GridSearchCV(svc,param_grid)\n",
+    "clf.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d0f2de2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0,\n",
+       "       1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1,\n",
+       "       0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
+       "       0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
+       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,\n",
+       "       1, 0, 0, 1, 1, 1, 0, 0, 1, 1])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = clf.predict(x_test)\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "38f8aaa6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Predictions ')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(y_test, y_pred)\n",
+    "plt.xlabel('True Values ')\n",
+    "plt.ylabel('Predictions ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "25eddb36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score,confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4db1eed0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.75"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_pred,y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d290475a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[33, 15],\n",
+       "       [15, 57]], dtype=int64)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_pred,y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "14aab603",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "pickle.dump(clf,open('img_model.p','wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "85cdab52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pickle.load(open('img_model.p','rb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d8c08e5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8a5641a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c10acb46",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
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+}