Upload 3 files

.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
+pokemons-model_transferlearning.keras filter=lfs diff=lfs merge=lfs -text

app.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting gradio\n",
+      "  Using cached gradio-4.31.4-py3-none-any.whl.metadata (15 kB)\n",
+      "Collecting aiofiles<24.0,>=22.0 (from gradio)\n",
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+      "  Using cached ffmpy-0.3.2.tar.gz (5.5 kB)\n",
+      "  Preparing metadata (setup.py): started\n",
+      "  Preparing metadata (setup.py): finished with status 'done'\n",
+      "Collecting gradio-client==0.16.4 (from gradio)\n",
+      "  Using cached gradio_client-0.16.4-py3-none-any.whl.metadata (7.1 kB)\n",
+      "Collecting httpx>=0.24.1 (from gradio)\n",
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+      "Building wheels for collected packages: ffmpy\n",
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+      "Successfully installed aiofiles-23.2.1 altair-5.3.0 annotated-types-0.7.0 anyio-4.3.0 attrs-23.2.0 dnspython-2.6.1 email_validator-2.1.1 fastapi-0.111.0 fastapi-cli-0.0.4 ffmpy-0.3.2 filelock-3.14.0 gradio-4.31.4 gradio-client-0.16.4 h11-0.14.0 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.0 huggingface-hub-0.23.0 jinja2-3.1.4 jsonschema-4.22.0 jsonschema-specifications-2023.12.1 orjson-3.10.3 pandas-2.2.2 pydantic-2.7.1 pydantic-core-2.18.2 pydub-0.25.1 python-dotenv-1.0.1 python-multipart-0.0.9 pytz-2024.1 pyyaml-6.0.1 referencing-0.35.1 rpds-py-0.18.1 ruff-0.4.4 semantic-version-2.10.0 shellingham-1.5.4 sniffio-1.3.1 starlette-0.37.2 tomlkit-0.12.0 toolz-0.12.1 typer-0.12.3 tzdata-2024.1 ujson-5.10.0 uvicorn-0.29.0 watchfiles-0.21.0 websockets-11.0.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install gradio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\angel\\workspace\\testpls\\.conda\\Lib\\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 = \"pokemons-model_transferlearning.keras\"\n",
+    "model = tf.keras.models.load_model(model_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the core prediction function\n",
+    "def predict_pokemons(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}\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_bulbasaur = prediction[0][0]  # Probability for class 'articuno'\n",
+    "    p_dratini = prediction[0][1]   # Probability for class 'moltres'\n",
+    "    p_gengar = prediction[0][2]    # Probability for class 'zapdos'\n",
+    " \n",
+    "  #  return {'charmander':  p_charmander, 'mewtwo': p_mewtwo, 'squirtle': p_squirtle}\n",
+    "    return {'Bulbasaur':  p_bulbasaur, 'Dratini': p_dratini, 'Gengar': p_gengar}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7864\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7864/\" 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": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'numpy.ndarray'>\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create the Gradio interface\n",
+    "input_image = gr.Image()\n",
+    "iface = gr.Interface(\n",
+    "    fn=predict_pokemons,\n",
+    "    inputs=input_image, \n",
+    "    outputs=gr.Label(),\n",
+    "    examples=[\"images/bulbasaur1.png\", \"images/bulbasaur2.png\", \"images/dratini1.png\", \"images/dratini2.png\", \"images/dratini3.png\", \"images/gengar1.png\", \"images/gengar2.png\", \"images/gengar3.png\"],   \n",
+    "    description=\"A simple mlp classification model for image classification using the mnist dataset.\")\n",
+    "iface.launch()"
+   ]
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pokemons-model_transferlearning.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3300682eebffa402bd83eb7875f5fd94bfd22777a3d425ab10aa45ddfca4ad96
+size 250560147

requirements.txt

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+tensorflow