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@@ -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-model_transferlearning.keras filter=lfs diff=lfs merge=lfs -text

app.ipynb

@@ -0,0 +1,272 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
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+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install tensorflow tensorflow-datasets gradio pillow matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_path = \"pokemon-model_transferlearning.keras\"\n",
+    "model = tf.keras.models.load_model(model_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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",
+    "    # 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_cat = (1 - prediction)\n",
+    "    p_dog = prediction\n",
+    "    return {'cat': p_cat, 'dog': p_dog}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Define the core prediction function\n",
+    "def predict_pokemon(image):\n",
+    "    # Preprocess image\n",
+    "    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image\n",
+    "    image = image.resize((150, 150)) # Resize the image to 150x150\n",
+    "    image = np.array(image)\n",
+    "    image = np.expand_dims(image, axis=0) # Add batch dimension\n",
+    "    \n",
+    "    # Predict\n",
+    "    prediction = model.predict(image)\n",
+    "    \n",
+    "    # Apply softmax to get probabilities for each class\n",
+    "    probabilities = tf.nn.softmax(prediction)\n",
+    "    \n",
+    "    # Map probabilities to Pokemon classes\n",
+    "    pokemon_classes = ['Articuno', 'Bulbasaur', 'Charmander']  \n",
+    "    probabilities_dict = {pokemon_class: round(float(probability), 2) for pokemon_class, probability in zip(pokemon_classes, probabilities[0])}\n",
+    "    \n",
+    "    return probabilities_dict\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7863\n",
+      "IMPORTANT: You are using gradio version 4.27.0, however version 4.29.0 is available, please upgrade.\n",
+      "--------\n",
+      "Running on public URL: https://a1f42e2a7f46b68a92.gradio.live\n",
+      "\n",
+      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"https://a1f42e2a7f46b68a92.gradio.live\" 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": 23,
+     "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/cat1.jpeg\", \"images/cat2.jpeg\", \"images/cat3.jpeg\", \"images/cat4.jpeg\", \"images/dog1.jpeg\", \"images/dog2.jpeg\", \"images/dog3.jpeg\"],   \n",
+    "    description=\"A simple mlp classification model for image classification using the mnist dataset.\")\n",
+    "iface.launch(share=True)"
+   ]
+  }
+ ],
+ "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": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pokemon-model_transferlearning.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9913d9bc354276469a6d192ee94f58e910c2788e3e56b9011dba8d177177482
+size 250560192

requirements.txt

@@ -0,0 +1,5 @@
+tensorflow
+numpy
+matplotlib
+pillow
+gradio