Delete app.ipynb

app.ipynb

@@ -1,272 +0,0 @@
-{
- "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",
-      "Requirement already satisfied: tensorflow in /Users/chiarazbinden/Library/Python/3.9/lib/python/site-packages (2.16.1)\n",
-      "Requirement already satisfied: tensorflow-datasets in /Users/chiarazbinden/Library/Python/3.9/lib/python/site-packages (4.9.3)\n",
-      "Requirement already satisfied: gradio in /Users/chiarazbinden/Library/Python/3.9/lib/python/site-packages (4.27.0)\n",
-      "Requirement already satisfied: pillow in /Users/chiarazbinden/Library/Python/3.9/lib/python/site-packages (10.2.0)\n",
-      "Requirement already satisfied: matplotlib in /Users/chiarazbinden/Library/Python/3.9/lib/python/site-packages (3.8.3)\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)"
-   ]
-  }
- ],
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