test

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+from model import SimpleNN, predict
+import torch
+from sklearn.preprocessing import LabelEncoder
+
+
+model = SimpleNN(input_size=4, hidden_size=64, output_size=5)
+model.load_state_dict(torch.load("your_model.pth"))
+model.eval()
+
+
+label_encoder = LabelEncoder()
+
+
+def classifier(rarity, size, color, country):
+    label_encoder.fit([rarity, color, country])
+
+    input_data = [
+        label_encoder.transform([rarity])[0],
+        size,
+        label_encoder.transform([color])[0],
+        label_encoder.transform([country])[0],
+    ]
+
+    predicted_class_index = predict(model, input_data)
+
+    predicted_species = label_encoder.inverse_transform([predicted_class_index])[0]
+
+    return (
+        f"{rarity} {size} {color} {country} => Predicted Species: {predicted_species}"
+    )
+
+
+iface = gr.Interface(
+    fn=classifier, inputs=["text", "number", "text", "text"], outputs="text"
+)
+
+
+iface.launch(share=True)

data.csv

@@ -0,0 +1,101 @@
+rarity,size,color,species,country
+Common,4.5,Green,TreeFrog,USA
+Uncommon,3.2,Brown,Bullfrog,Canada
+Common,5.1,Blue,TreeFrog,Mexico
+Rare,6.7,Red,RedEyedTreeFrog,Brazil
+Common,2.8,Yellow,BananaFrog,Australia
+Uncommon,5.5,Green,Bullfrog,USA
+Common,3.9,Brown,TreeFrog,Canada
+Rare,4.8,Blue,GreenTreeFrog,Mexico
+Rare,6.2,Red,RedEyedTreeFrog,Brazil
+Common,2.4,Yellow,BananaFrog,Australia
+Uncommon,5.3,Green,Bullfrog,USA
+Common,3.5,Brown,GreenTreeFrog,Canada
+Common,4.7,Blue,TreeFrog,Mexico
+Rare,6.1,Red,RedEyedTreeFrog,Brazil
+Common,2.2,Yellow,BananaFrog,Australia
+Uncommon,4.9,Green,TreeFrog,USA
+Uncommon,3.3,Brown,Bullfrog,Canada
+Rare,5.6,Blue,GreenTreeFrog,Mexico
+Rare,6.4,Red,RedEyedTreeFrog,Brazil
+Common,2.9,Yellow,BananaFrog,Australia
+Uncommon,5.2,Green,Bullfrog,USA
+Common,3.8,Brown,TreeFrog,Canada
+Common,4.6,Blue,GreenTreeFrog,Mexico
+Rare,6.5,Red,RedEyedTreeFrog,Brazil
+Common,2.7,Yellow,BananaFrog,Australia
+Uncommon,5.7,Green,Bullfrog,USA
+Common,3.1,Brown,GreenTreeFrog,Canada
+Rare,4.2,Blue,GreenTreeFrog,Mexico
+Rare,6.9,Red,RedEyedTreeFrog,Brazil
+Common,2.1,Yellow,BananaFrog,Australia
+Uncommon,5.0,Green,Bullfrog,USA
+Common,3.4,Brown,TreeFrog,Canada
+Uncommon,4.4,Blue,GreenTreeFrog,Mexico
+Rare,6.3,Red,RedEyedTreeFrog,Brazil
+Common,2.5,Yellow,BananaFrog,Australia
+Uncommon,5.4,Green,Bullfrog,USA
+Common,3.6,Brown,TreeFrog,Canada
+Rare,4.3,Blue,GreenTreeFrog,Mexico
+Rare,6.0,Red,RedEyedTreeFrog,Brazil
+Common,2.6,Yellow,BananaFrog,Australia
+Uncommon,4.1,Green,Bullfrog,USA
+Common,3.0,Brown,TreeFrog,Canada
+Common,5.8,Blue,GreenTreeFrog,Mexico
+Rare,6.8,Red,RedEyedTreeFrog,Brazil
+Common,2.3,Yellow,BananaFrog,Australia
+Uncommon,5.9,Green,Bullfrog,USA
+Common,3.7,Brown,TreeFrog,Canada
+Rare,4.0,Blue,GreenTreeFrog,Mexico
+Rare,6.6,Red,RedEyedTreeFrog,Brazil
+Common,2.0,Yellow,BananaFrog,Australia
+Uncommon,4.9,Green,Bullfrog,USA
+Uncommon,3.3,Brown,TreeFrog,Canada
+Rare,5.5,Blue,GreenTreeFrog,Mexico
+Rare,6.1,Red,RedEyedTreeFrog,Brazil
+Common,2.7,Yellow,BananaFrog,Australia
+Uncommon,5.2,Green,Bullfrog,USA
+Common,3.8,Brown,TreeFrog,Canada
+Rare,4.6,Blue,GreenTreeFrog,Mexico
+Rare,6.4,Red,RedEyedTreeFrog,Brazil
+Common,2.1,Yellow,BananaFrog,Australia
+Uncommon,4.8,Green,Bullfrog,USA
+Common,3.5,Brown,GreenTreeFrog,Canada
+Common,5.2,Blue,GreenTreeFrog,Mexico
+Rare,6.0,Red,RedEyedTreeFrog,Brazil
+Common,2.4,Yellow,BananaFrog,Australia
+Uncommon,5.6,Green,Bullfrog,USA
+Common,3.1,Brown,TreeFrog,Canada
+Rare,4.5,Blue,GreenTreeFrog,Mexico
+Rare,6.8,Red,RedEyedTreeFrog,Brazil
+Common,2.9,Yellow,BananaFrog,Australia
+Uncommon,5.3,Green,Bullfrog,USA
+Common,3.7,Brown,TreeFrog,Canada
+Common,4.2,Blue,GreenTreeFrog,Mexico
+Rare,6.6,Red,RedEyedTreeFrog,Brazil
+Common,2.2,Yellow,BananaFrog,Australia
+Uncommon,5.0,Green,Bullfrog,USA
+Common,3.4,Brown,GreenTreeFrog,Canada
+Rare,4.7,Blue,TreeFrog,Mexico
+Rare,6.3,Red,RedEyedTreeFrog,Brazil
+Common,2.5,Yellow,BananaFrog,Australia
+Uncommon,4.6,Green,Bullfrog,USA
+Common,3.0,Brown,TreeFrog,Canada
+Common,5.8,Blue,GreenTreeFrog,Mexico
+Rare,6.9,Red,RedEyedTreeFrog,Brazil
+Common,2.6,Yellow,BananaFrog,Australia
+Uncommon,5.7,Green,Bullfrog,USA
+Common,3.2,Brown,TreeFrog,Canada
+Rare,4.3,Blue,GreenTreeFrog,Mexico
+Rare,6.2,Red,RedEyedTreeFrog,Brazil
+Common,2.3,Yellow,BananaFrog,Australia
+Uncommon,5.1,Green,Bullfrog,USA
+Common,3.6,Brown,TreeFrog,Canada
+Rare,4.0,Blue,GreenTreeFrog,Mexico
+Rare,6.5,Red,RedEyedTreeFrog,Brazil
+Common,2.8,Yellow,BananaFrog,Australia
+Uncommon,5.4,Green,Bullfrog,USA
+Common,3.0,Brown,TreeFrog,Canada
+Rare,4.4,Blue,GreenTreeFrog,Mexico
+Rare,6.7,Red,RedEyedTreeFrog,Brazil
+Common,2.4,Yellow,BananaFrog,Australia

model.py

@@ -0,0 +1,32 @@
+from torch import nn
+import torch
+
+input_size = 4
+hidden_size = 64
+output_size = 5
+
+
+class SimpleNN(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(SimpleNN, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        x = self.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+
+def predict(model, input):
+    model.eval()
+    input_tensors = torch.tensor(input, dtype=torch.float32).unsqueeze(0)
+
+    with torch.no_grad():
+        output = model(input_tensors)
+
+    probabilities = torch.softmax(output, dim=1)
+
+    predicted_class_index = torch.argmax(probabilities, dim=1).item()
+    return predicted_class_index

notebook.ipynb

@@ -0,0 +1,359 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from torch import nn\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "import torch.optim as optim\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.preprocessing import StandardScaler\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rarity</th>\n",
+       "      <th>size</th>\n",
+       "      <th>color</th>\n",
+       "      <th>species</th>\n",
+       "      <th>country</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Common</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>Green</td>\n",
+       "      <td>TreeFrog</td>\n",
+       "      <td>USA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Uncommon</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>Brown</td>\n",
+       "      <td>Bullfrog</td>\n",
+       "      <td>Canada</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Common</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>Blue</td>\n",
+       "      <td>TreeFrog</td>\n",
+       "      <td>Mexico</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Rare</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>Red</td>\n",
+       "      <td>RedEyedTreeFrog</td>\n",
+       "      <td>Brazil</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Common</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>Yellow</td>\n",
+       "      <td>BananaFrog</td>\n",
+       "      <td>Australia</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     rarity  size   color          species    country\n",
+       "0    Common   4.5   Green         TreeFrog        USA\n",
+       "1  Uncommon   3.2   Brown         Bullfrog     Canada\n",
+       "2    Common   5.1    Blue         TreeFrog     Mexico\n",
+       "3      Rare   6.7     Red  RedEyedTreeFrog     Brazil\n",
+       "4    Common   2.8  Yellow       BananaFrog  Australia"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('data.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Preprocess data\n",
+    "label_encoder = LabelEncoder()\n",
+    "data['species'] = label_encoder.fit_transform(data['species'])\n",
+    "data['rarity'] = label_encoder.fit_transform(data['rarity'])  # Encode the 'rarity' column\n",
+    "data['color'] = label_encoder.fit_transform(data['color'])\n",
+    "data['country'] = label_encoder.fit_transform(data['country'])\n",
+    "\n",
+    "\n",
+    "X = data.drop(['species'], axis=1).values\n",
+    "y = data['species'].values\n",
+    "\n",
+    "# Split data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_tensor = torch.tensor(X_train, dtype=torch.float32)\n",
+    "y_train_tensor = torch.tensor(y_train, dtype=torch.int64)\n",
+    "X_test_tensor = torch.tensor(X_test, dtype=torch.float32)\n",
+    "y_test_tensor = torch.tensor(y_test, dtype=torch.int64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# deep nn\n",
+    "\n",
+    "class SimpleNN(nn.Module):\n",
+    "    def __init__(self,input_size,hidden_size,output_size):\n",
+    "        super(SimpleNN,self).__init__()\n",
+    "        self.fc1 = nn.Linear(input_size,hidden_size)\n",
+    "        self.relu = nn.ReLU()\n",
+    "        self.fc2 = nn.Linear(hidden_size,output_size)\n",
+    "\n",
+    "    def forward(self,x):\n",
+    "        x = self.relu(self.fc1(x))\n",
+    "        x = self.fc2(x)\n",
+    "        return x\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_size = 4\n",
+    "hidden_size = 64 \n",
+    "output_size = len(label_encoder.classes_)\n",
+    "\n",
+    "model = SimpleNN(input_size=input_size,hidden_size=hidden_size,output_size=output_size)\n",
+    "optimizer = optim.Adam(model.parameters(),lr=0.001)\n",
+    "loss_fn = nn.CrossEntropyLoss()\n",
+    "\n",
+    "output_size\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch [1/10], Loss: 1.5100\n",
+      "Epoch [2/10], Loss: 1.4834\n",
+      "Epoch [3/10], Loss: 1.4557\n",
+      "Epoch [4/10], Loss: 1.4273\n",
+      "Epoch [5/10], Loss: 1.3985\n",
+      "Epoch [6/10], Loss: 1.3693\n",
+      "Epoch [7/10], Loss: 1.3398\n",
+      "Epoch [8/10], Loss: 1.3105\n",
+      "Epoch [9/10], Loss: 1.2812\n",
+      "Epoch [10/10], Loss: 1.2522\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 10\n",
+    "batch_size = 32\n",
+    "\n",
+    "for epoch in range(num_epochs):\n",
+    "    for i in range(0, len(X_train_tensor), batch_size):\n",
+    "        inputs = X_train_tensor[i:i + batch_size]\n",
+    "        labels = y_train_tensor[i:i + batch_size]\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "        outputs = model(inputs)\n",
+    "        loss = loss_fn(outputs, labels)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "    print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the model\n",
+    "torch.save(model.state_dict(), 'your_model.pth')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_model = SimpleNN(input_size=input_size, hidden_size=hidden_size, output_size=output_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loaded_model.load_state_dict(torch.load('your_model.pth'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SimpleNN(\n",
+       "  (fc1): Linear(in_features=4, out_features=64, bias=True)\n",
+       "  (relu): ReLU()\n",
+       "  (fc2): Linear(in_features=64, out_features=5, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loaded_model.eval()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Accuracy: 80.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "with torch.no_grad():\n",
+    "    # Make predictions on the test set\n",
+    "    loaded_outputs = loaded_model(X_test_tensor)\n",
+    "\n",
+    "    # Convert the model outputs to class predictions\n",
+    "    _, predicted_labels = torch.max(loaded_outputs, 1)\n",
+    "\n",
+    "    # Evaluate accuracy\n",
+    "    accuracy = (predicted_labels == y_test_tensor).sum().item() / len(y_test_tensor)\n",
+    "    print(f'Test Accuracy: {accuracy * 100:.2f}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ai_env",
+   "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

your_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52208f5a32479973544eed29cb6c6d5e9a7e57a58298b7a8548d8404defbe29a
+size 4648