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@@ -33,3 +33,6 @@ 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-model_transferlearning.keras filter=lfs diff=lfs merge=lfs -text
+pokemon/train/Jolteon/00000036.jpg filter=lfs diff=lfs merge=lfs -text
+pokemon/val/Jolteon/00000036.jpg filter=lfs diff=lfs merge=lfs -text

app.ipynb

@@ -0,0 +1,229 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the core prediction function\n",
+    "def predict_cat_dog(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",
+    "    # Because the output layer was dense(0) without an activation function, we need to apply sigmoid to get the probability\n",
+    "    # we could also change the output layer to dense(1, activation='sigmoid')\n",
+    "    prediction = np.round(float(tf.sigmoid(prediction)), 2)\n",
+    "    p_jolteon = prediction\n",
+    "    p_eevee = (1 - prediction)\n",
+    "    p_dratini = (2 - prediction)\n",
+    "    return {'jolteon': p_jolteon, 'eevee': p_eevee, 'dratini': p_dratini}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7862\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7862/\" 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": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'numpy.ndarray'>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\queueing.py\", line 527, in process_events\n",
+      "    response = await route_utils.call_process_api(\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\route_utils.py\", line 270, in call_process_api\n",
+      "    output = await app.get_blocks().process_api(\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\blocks.py\", line 1847, in process_api\n",
+      "    result = await self.call_function(\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\blocks.py\", line 1433, in call_function\n",
+      "    prediction = await anyio.to_thread.run_sync(\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
+      "    return await get_async_backend().run_sync_in_worker_thread(\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2134, in run_sync_in_worker_thread\n",
+      "    return await future\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 851, in run\n",
+      "    result = context.run(func, *args)\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\utils.py\", line 805, in wrapper\n",
+      "    response = f(*args, **kwargs)\n",
+      "  File \"C:\\Users\\dom-k\\AppData\\Local\\Temp\\ipykernel_33600\\3230146116.py\", line 11, in predict_cat_dog\n",
+      "    prediction = model.predict(image)\n",
+      "NameError: name 'model' is not defined\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create the Gradio interface\n",
+    "input_image = gr.Image()\n",
+    "iface = gr.Interface(\n",
+    "    fn=predict_cat_dog,\n",
+    "    inputs=input_image, \n",
+    "    outputs=gr.Label(),\n",
+    "    examples=[\"images_pokemon/Dratini1.png\", \"images_pokemon/Dratini2.png\", \"images_pokemon/Dratini3.png\", \"images_pokemon/Dratini4.png\", \"images_pokemon/Dratini5.png\",\n",
+    "              \"images_pokemon/Eevee1.png\", \"images_pokemon/Eevee2.png\", \"images_pokemon/Eevee3.png\", \"images_pokemon/Eevee4.png\", \"images_pokemon/Eevee5.png\",\n",
+    "              \"images_pokemon/Jolteon1.png\", \"images_pokemon/Jolteon2.png\", \"images_pokemon/Jolteon3.png\", \"images_pokemon/Jolteon4.png\", \"images_pokemon/Jolteon5.png\"],       \n",
+    "    description=\"A simple mlp classification model for image classification using the mnist dataset.\")\n",
+    "iface.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7863\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7863/\" 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": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'numpy.ndarray'>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\queueing.py\", line 527, in process_events\n",
+      "    response = await route_utils.call_process_api(\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\route_utils.py\", line 270, in call_process_api\n",
+      "    output = await app.get_blocks().process_api(\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\blocks.py\", line 1847, in process_api\n",
+      "    result = await self.call_function(\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\blocks.py\", line 1433, in call_function\n",
+      "    prediction = await anyio.to_thread.run_sync(\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
+      "    return await get_async_backend().run_sync_in_worker_thread(\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2134, in run_sync_in_worker_thread\n",
+      "    return await future\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 851, in run\n",
+      "    result = context.run(func, *args)\n",
+      "  File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\utils.py\", line 805, in wrapper\n",
+      "    response = f(*args, **kwargs)\n",
+      "  File \"C:\\Users\\dom-k\\AppData\\Local\\Temp\\ipykernel_33600\\3230146116.py\", line 11, in predict_cat_dog\n",
+      "    prediction = model.predict(image)\n",
+      "NameError: name 'model' is not defined\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "\n",
+    "def greet(name, intensity):\n",
+    "    return \"Hello, \" + name + \"!\" * int(intensity)\n",
+    "\n",
+    "demo = gr.Interface(\n",
+    "    fn=greet,\n",
+    "    inputs=[\"text\", \"slider\"],\n",
+    "    outputs=[\"text\"],\n",
+    ")\n",
+    "\n",
+    "demo.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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pokemon-model_transferlearning.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4db26ce9bd748b77178d57bdc74acbd4e39e4e14f5a7e54a13caf0ba5953619
+size 250560147