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Add: keras file, app.ipynb

.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
+Abra-vs-Pikachu-vs-Zubat-model_transferlearning.keras filter=lfs diff=lfs merge=lfs -text

Abra-vs-Pikachu-vs-Zubat-model_transferlearning.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a4190ad1a47e2afd5cebe970c0de99894433f688ff2782dc6358f6338dc248c3
+size 250560147

app.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sonah/Library/CloudStorage/OneDrive-ZHAW/AI/Task2/myvenv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "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": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_path = \"Abra-vs-Pikachu-vs-Zubat-model_transferlearning.keras\"\n",
+    "model = tf.keras.models.load_model(model_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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",
+    "    # Apply sigmoid to get probabilities\n",
+    "    prediction_prob = tf.sigmoid(prediction).numpy()\n",
+    "\n",
+    "\n",
+    "    # Extract probabilities for Abra and Pikachu and Zubat\n",
+    "    p_Abra = round(prediction_prob[0][0], 2)\n",
+    "    p_Pikachu = round(prediction_prob[0][1], 2)\n",
+    "    p_Zubat = round(prediction_prob[0][2], 2)\n",
+    "    \n",
+    "    \n",
+    "    return {'Abra': p_Abra, 'Pikachu': p_Pikachu, 'Zubat': p_Zubat}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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": 4,
+     "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/Abra1.png\", \"images/Abra2.jpg\", \"images/Abra3.jpg\", \"images/Pikachu1.jpg\", \"images/Pikachu2.png\", \"images/Pikachu3.jpg\", \"images/Zubat1.jpg\",\"images/Zubat2.jpg\",\"images/Zubat3.jpg\"],   \n",
+    "    description=\"A simple mlp classification model for image classification using the mnist dataset.\")\n",
+    "iface.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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
+}

my_app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+model_path = "Abra-vs-Pikachu-vs-Zubat-model_transferlearning.keras"
+model = tf.keras.models.load_model(model_path)
+
+# Define the core prediction function
+
+
+def predict_pokemon(image):
+    # Preprocess image
+    print(type(image))
+    # Convert numpy array to PIL image
+    image = Image.fromarray(image.astype('uint8'))
+    # resize the image to 28x28 and converts it to gray scale
+    image = image.resize((150, 150))
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0)  # same as image[None, ...]
+
+    # Predict
+    prediction = model.predict(image)
+
+    # Apply sigmoid to get probabilities
+    prediction_prob = tf.sigmoid(prediction).numpy()
+
+    # Extract probabilities for Abra and Pikachu and Zubat
+    p_Abra = round(prediction_prob[0][0], 2)
+    p_Pikachu = round(prediction_prob[0][1], 2)
+    p_Zubat = round(prediction_prob[0][2], 2)
+
+    return {'Abra': p_Abra, 'Pikachu': p_Pikachu, 'Zubat': p_Zubat}
+
+
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_pokemon,
+    inputs=input_image,
+    outputs=gr.Label(),
+    examples=["images/Abra1.png", "images/Abra2.jpg", "images/Abra3.jpg", "images/Pikachu1.jpg",
+              "images/Pikachu2.png", "images/Pikachu3.jpg", "images/Zubat1.jpg", "images/Zubat2.jpg", "images/Zubat3.jpg"],
+    description="A simple mlp classification model for image classification using the mnist dataset.")
+iface.launch()