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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Pokemon_transfer_learning.keras filter=lfs diff=lfs merge=lfs -text

Pokemon_transfer_learning.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:afe180bd42b6f226d6a8554dcc9231f01e4d0fa85604f534581b321b6e9b86af
+size 250560250

app.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "from PIL import Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_path = \"Pokemon_transfer_learning.keras\"\n",
+    "model = tf.keras.models.load_model(model_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the core prediction function\n",
+    "def predict_pokemon(image):\n",
+    "    # Preprocess image\n",
+    "    print(type(image))\n",
+    "    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image\n",
+    "    image = image.resize((150, 150)) #resize the image to 28x28 and converts it to gray scale\n",
+    "    image = np.array(image)\n",
+    "    image = np.expand_dims(image, axis=0) # same as image[None, ...]\n",
+    "    \n",
+    "    # Predict\n",
+    "    prediction = model.predict(image)\n",
+    "    \n",
+    "     # No need to apply sigmoid, as the output layer already uses softmax\n",
+    "    # Convert the probabilities to rounded values\n",
+    "    prediction = np.round(prediction, 2)\n",
+    " \n",
+    "    # Separate the probabilities for each class\n",
+    "    p_pikachu = prediction[0][0]  # Probability for class 'articuno'\n",
+    "    p_rattata = prediction[0][1]   # Probability for class 'moltres'\n",
+    "    p_zubat = prediction[0][2]    # Probability for class 'zapdos'\n",
+    " \n",
+    "    return {'pikachu':  p_pikachu, 'rattata': p_rattata, 'zubat': p_zubat}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7861\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7861/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create the Gradio interface\n",
+    "input_image = gr.Image()\n",
+    "iface = gr.Interface(\n",
+    "    fn=predict_pokemon,\n",
+    "    inputs=input_image,\n",
+    "    outputs=gr.Label(),\n",
+    "    examples=[\"images/pikachu1.jpg\", \"images/pikachu2.jpg\", \"images/pikachu3.jpg\", \"images/rattata1.jpg\", \"images/rattata2.jpg\", \"images/rattata3.jpg\", \"images/zubat1.jpg\", \"images/zubat2.jpg\", \"images/zubat3.jpg\"],  \n",
+    "    description=\"TEST.\")\n",
+    "\n",
+    "iface.launch()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv_new",
+   "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": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

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+tensorflow