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diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..bafc84a91a27c8a4f0e983e97dddbabe82c0574b
--- /dev/null
+++ b/app.py
@@ -0,0 +1,60 @@
+
+import streamlit as st
+from torchvision import transforms
+import torch
+import PIL
+from streamlit_image_select import image_select
+
+#Transforming the Input Image
+img_transform = transforms.Compose([
+    transforms.Resize((224, 224)), 
+    transforms.ToTensor(), 
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), 
+])
+#Loading the model
+model = torch.load("model\MobileNet_v3.pt", map_location=torch.device('cpu'))
+model.eval()
+
+#Mushroom Classes
+classes = ['Boletus', 'Lactarius', 'Russula']
+
+#Function that returns the type of Mushrooom
+def get_type(image):
+    image = PIL.Image.open(image)
+    image = img_transform(image)
+    image = torch.unsqueeze(image, 0)
+    out = model(image)
+    ans = classes[out.argmax(-1)[0]]
+    return "The Mushrooom Type is "+ans
+
+st.title("Mushroom Classifier")
+st.text("Upload your Image and find the category of mushroom it belongs to")
+
+#Sidebar to upload and select Images
+with st.sidebar:
+    usr_img = st.file_uploader("Upload the Mushroom Image")
+    c = st.container()
+    img = image_select(label="Select From Examples",
+                        images=['samples\Russula.jpg','samples\Boletus.jpg','samples\Lactarius.jpg'],
+                        captions=["Russula", "Boletus", "Lactarius"],)
+#Displaying Image
+if usr_img:
+    st.image(usr_img, width=500)
+else:
+    st.image(img, width=500)
+
+#Button to Classify
+if st.button("Classify"):
+    text = get_type(usr_img if usr_img else img)
+    st.text(text)
+
+
+
+
+
+
+
+
+
+
+
diff --git a/model/MobileNet_v3.pt b/model/MobileNet_v3.pt
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index 0000000000000000000000000000000000000000..304daaae27ba56c380f0728f4766353242666393
--- /dev/null
+++ b/model/MobileNet_v3.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:40c7b611d1c9994c2912124e88398b31cb931ae6fa5c876d30b37b21788fb21e
+size 6256034
diff --git a/notebook/Mushroom_Classifier_MobileNetV3.ipynb b/notebook/Mushroom_Classifier_MobileNetV3.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9a97be90860708ff4045ff265765b4a7c87b5021
--- /dev/null
+++ b/notebook/Mushroom_Classifier_MobileNetV3.ipynb
@@ -0,0 +1,356 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Necessary Modules\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import transforms\n",
+    "from torchvision.datasets import ImageFolder\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torchvision\n",
+    "import torch.nn as nn\n",
+    "from torch import optim\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loading the Data\n",
+    "train = r\"..\\Dataset\\train\"\n",
+    "test = r\"..\\Dataset\\test\"\n",
+    "\n",
+    "train_transform = transforms.Compose([\n",
+    "    transforms.Resize((224, 224)),\n",
+    "    transforms.CenterCrop(224),\n",
+    "    transforms.ToTensor(),\n",
+    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
+    "])\n",
+    "\n",
+    "test_transform = transforms.Compose([\n",
+    "    transforms.Resize((224, 224)), \n",
+    "    transforms.ToTensor(), \n",
+    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), \n",
+    "])\n",
+    "\n",
+    "train_folder = ImageFolder(train, transform=train_transform)\n",
+    "test_folder = ImageFolder(test, transform = test_transform)\n",
+    "\n",
+    "train_loader = DataLoader(train_folder, batch_size=8, shuffle=True)\n",
+    "test_loader = DataLoader(test_folder, batch_size=8)\n",
+    "\n",
+    "# Class Names\n",
+    "class_names = train_folder.classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def imshow(inp, title=None):\n",
+    "    \"\"\"Display image for Tensor.\"\"\"\n",
+    "    inp = inp.numpy().transpose((1, 2, 0))\n",
+    "    inp = np.clip(inp, 0, 1)\n",
+    "    plt.imshow(inp)\n",
+    "    if title is not None:\n",
+    "        plt.title(title)\n",
+    "    plt.pause(0.001) \n",
+    "\n",
+    "\n",
+    "# Getting a batch of training data\n",
+    "inputs, classes = next(iter(train_loader))\n",
+    "\n",
+    "# Making a grid from batch\n",
+    "out = torchvision.utils.make_grid(inputs)\n",
+    "\n",
+    "imshow(out, title=[class_names[x] for x in classes])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Defining the Training Loop\n",
+    "def train(train_loader ,test_loader, model, optimizer, loss_fn, num_epochs = 1):\n",
+    "    for epoch in range(num_epochs):\n",
+    "        train_loss, test_loss = 0, 0\n",
+    "        train_acc, test_acc = 0, 0\n",
+    "        model.train()\n",
+    "        for x, y in train_loader:\n",
+    "            x = x.to(device)\n",
+    "            y = y.to(device)\n",
+    "\n",
+    "            optimizer.zero_grad()\n",
+    "            out = model(x)\n",
+    "            loss = loss_fn(out, y)\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "            train_loss+=loss.item()\n",
+    "            train_acc += torch.eq(out.argmax(-1), y).sum().item()\n",
+    "        \n",
+    "        model.eval()\n",
+    "        for x, y in test_loader:\n",
+    "            x = x.to(device)\n",
+    "            y = y.to(device)\n",
+    "\n",
+    "            out = model(x)\n",
+    "            loss = loss_fn(out, y)\n",
+    "            \n",
+    "            test_loss+=loss.item()\n",
+    "            test_acc += torch.eq(out.argmax(-1), y).sum().item()\n",
+    "        \n",
+    "\n",
+    "        train_loss/= len(train_loader.dataset)\n",
+    "        train_acc /=len(train_loader.dataset)\n",
+    "\n",
+    "        test_loss/= len(test_loader.dataset)\n",
+    "        test_acc /=len(test_loader.dataset)\n",
+    "        #if (epoch+1)%5==0:\n",
+    "        print(f\"Epoch:{epoch+1}\")\n",
+    "        print(f\"Train Accuracy: {train_acc:.2f}   Train Loss : {train_loss:.2f} \")\n",
+    "        print(f\"Test Accuracy:  {test_acc:.2f}   Test Loss: {test_loss:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loading the MobileNetV3 Model as a Fixed Feature Extractor\n",
+    "model = torchvision.models.mobilenet_v3_small(weights='DEFAULT')\n",
+    "for param in model.parameters():\n",
+    "    param.requires_grad = False\n",
+    "\n",
+    "model.classifier[3] = nn.Linear(in_features=1024, out_features=3, bias=True)\n",
+    "model.to(device)\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "optimizer = optim.SGD(model.parameters(), lr=0.001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:1\n",
+      "Train Accuracy: 0.40   Train Loss : 0.13 \n",
+      "Test Accuracy:  0.56   Test Loss: 0.13\n",
+      "Epoch:2\n",
+      "Train Accuracy: 0.57   Train Loss : 0.12 \n",
+      "Test Accuracy:  0.68   Test Loss: 0.11\n",
+      "Epoch:3\n",
+      "Train Accuracy: 0.66   Train Loss : 0.11 \n",
+      "Test Accuracy:  0.74   Test Loss: 0.11\n",
+      "Epoch:4\n",
+      "Train Accuracy: 0.68   Train Loss : 0.10 \n",
+      "Test Accuracy:  0.76   Test Loss: 0.10\n",
+      "Epoch:5\n",
+      "Train Accuracy: 0.73   Train Loss : 0.10 \n",
+      "Test Accuracy:  0.77   Test Loss: 0.09\n",
+      "Epoch:6\n",
+      "Train Accuracy: 0.71   Train Loss : 0.10 \n",
+      "Test Accuracy:  0.79   Test Loss: 0.09\n",
+      "Epoch:7\n",
+      "Train Accuracy: 0.74   Train Loss : 0.09 \n",
+      "Test Accuracy:  0.80   Test Loss: 0.09\n",
+      "Epoch:8\n",
+      "Train Accuracy: 0.75   Train Loss : 0.09 \n",
+      "Test Accuracy:  0.81   Test Loss: 0.08\n",
+      "Epoch:9\n",
+      "Train Accuracy: 0.76   Train Loss : 0.09 \n",
+      "Test Accuracy:  0.81   Test Loss: 0.08\n",
+      "Epoch:10\n",
+      "Train Accuracy: 0.76   Train Loss : 0.08 \n",
+      "Test Accuracy:  0.82   Test Loss: 0.08\n",
+      "Epoch:11\n",
+      "Train Accuracy: 0.79   Train Loss : 0.08 \n",
+      "Test Accuracy:  0.82   Test Loss: 0.08\n",
+      "Epoch:12\n",
+      "Train Accuracy: 0.79   Train Loss : 0.08 \n",
+      "Test Accuracy:  0.82   Test Loss: 0.07\n",
+      "Epoch:13\n",
+      "Train Accuracy: 0.77   Train Loss : 0.08 \n",
+      "Test Accuracy:  0.81   Test Loss: 0.07\n",
+      "Epoch:14\n",
+      "Train Accuracy: 0.78   Train Loss : 0.08 \n",
+      "Test Accuracy:  0.81   Test Loss: 0.07\n",
+      "Epoch:15\n",
+      "Train Accuracy: 0.77   Train Loss : 0.08 \n",
+      "Test Accuracy:  0.82   Test Loss: 0.07\n",
+      "Epoch:16\n",
+      "Train Accuracy: 0.80   Train Loss : 0.07 \n",
+      "Test Accuracy:  0.82   Test Loss: 0.07\n",
+      "Epoch:17\n",
+      "Train Accuracy: 0.81   Train Loss : 0.07 \n",
+      "Test Accuracy:  0.82   Test Loss: 0.07\n",
+      "Epoch:18\n",
+      "Train Accuracy: 0.82   Train Loss : 0.07 \n",
+      "Test Accuracy:  0.83   Test Loss: 0.06\n",
+      "Epoch:19\n",
+      "Train Accuracy: 0.79   Train Loss : 0.07 \n",
+      "Test Accuracy:  0.83   Test Loss: 0.06\n",
+      "Epoch:20\n",
+      "Train Accuracy: 0.80   Train Loss : 0.07 \n",
+      "Test Accuracy:  0.83   Test Loss: 0.06\n",
+      "Epoch:21\n",
+      "Train Accuracy: 0.81   Train Loss : 0.07 \n",
+      "Test Accuracy:  0.83   Test Loss: 0.06\n",
+      "Epoch:22\n",
+      "Train Accuracy: 0.81   Train Loss : 0.07 \n",
+      "Test Accuracy:  0.84   Test Loss: 0.06\n",
+      "Epoch:23\n",
+      "Train Accuracy: 0.80   Train Loss : 0.07 \n",
+      "Test Accuracy:  0.85   Test Loss: 0.06\n",
+      "Epoch:24\n",
+      "Train Accuracy: 0.82   Train Loss : 0.07 \n",
+      "Test Accuracy:  0.85   Test Loss: 0.06\n",
+      "Epoch:25\n",
+      "Train Accuracy: 0.80   Train Loss : 0.07 \n",
+      "Test Accuracy:  0.85   Test Loss: 0.06\n",
+      "Epoch:26\n",
+      "Train Accuracy: 0.82   Train Loss : 0.07 \n",
+      "Test Accuracy:  0.85   Test Loss: 0.06\n",
+      "Epoch:27\n",
+      "Train Accuracy: 0.80   Train Loss : 0.07 \n",
+      "Test Accuracy:  0.85   Test Loss: 0.06\n",
+      "Epoch:28\n",
+      "Train Accuracy: 0.79   Train Loss : 0.07 \n",
+      "Test Accuracy:  0.85   Test Loss: 0.06\n",
+      "Epoch:29\n",
+      "Train Accuracy: 0.84   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.85   Test Loss: 0.05\n",
+      "Epoch:30\n",
+      "Train Accuracy: 0.82   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.86   Test Loss: 0.05\n",
+      "Epoch:31\n",
+      "Train Accuracy: 0.82   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.86   Test Loss: 0.05\n",
+      "Epoch:32\n",
+      "Train Accuracy: 0.81   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.86   Test Loss: 0.05\n",
+      "Epoch:33\n",
+      "Train Accuracy: 0.82   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.87   Test Loss: 0.05\n",
+      "Epoch:34\n",
+      "Train Accuracy: 0.82   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.87   Test Loss: 0.05\n",
+      "Epoch:35\n",
+      "Train Accuracy: 0.81   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.87   Test Loss: 0.05\n",
+      "Epoch:36\n",
+      "Train Accuracy: 0.83   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.87   Test Loss: 0.05\n",
+      "Epoch:37\n",
+      "Train Accuracy: 0.83   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.87   Test Loss: 0.05\n",
+      "Epoch:38\n",
+      "Train Accuracy: 0.81   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.87   Test Loss: 0.05\n",
+      "Epoch:39\n",
+      "Train Accuracy: 0.83   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.86   Test Loss: 0.05\n",
+      "Epoch:40\n",
+      "Train Accuracy: 0.83   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.86   Test Loss: 0.05\n",
+      "Epoch:41\n",
+      "Train Accuracy: 0.83   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.87   Test Loss: 0.05\n",
+      "Epoch:42\n",
+      "Train Accuracy: 0.83   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.86   Test Loss: 0.05\n",
+      "Epoch:43\n",
+      "Train Accuracy: 0.83   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.87   Test Loss: 0.05\n",
+      "Epoch:44\n",
+      "Train Accuracy: 0.83   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.87   Test Loss: 0.05\n",
+      "Epoch:45\n",
+      "Train Accuracy: 0.84   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.87   Test Loss: 0.05\n",
+      "Epoch:46\n",
+      "Train Accuracy: 0.83   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.88   Test Loss: 0.05\n",
+      "Epoch:47\n",
+      "Train Accuracy: 0.82   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.87   Test Loss: 0.05\n",
+      "Epoch:48\n",
+      "Train Accuracy: 0.84   Train Loss : 0.05 \n",
+      "Test Accuracy:  0.87   Test Loss: 0.05\n",
+      "Epoch:49\n",
+      "Train Accuracy: 0.81   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.88   Test Loss: 0.05\n",
+      "Epoch:50\n",
+      "Train Accuracy: 0.82   Train Loss : 0.06 \n",
+      "Test Accuracy:  0.87   Test Loss: 0.05\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Training the Model\n",
+    "train(train_loader, test_loader, model, optimizer, criterion, num_epochs=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Saving the Model\n",
+    "torch.save(model, \"MobileNet_v3.pt\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a9e48d07ba8c5d81bd122f68c000442597bb068a
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,5 @@
+Pillow==10.3.0
+streamlit==1.31.1
+streamlit_image_select==0.6.0
+torch==2.2.0+cu121
+torchvision==0.17.0+cu121
diff --git a/samples/Boletus.jpg b/samples/Boletus.jpg
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diff --git a/samples/Lactarius.jpg b/samples/Lactarius.jpg
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diff --git a/samples/Russula.jpg b/samples/Russula.jpg
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