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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
+transferlearning_pokemon.keras filter=lfs diff=lfs merge=lfs -text

app.ipynb

@@ -0,0 +1,184 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_path = \"transferlearning_pokemon.keras\"\n",
+    "model = tf.keras.models.load_model(model_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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 150x150\n",
+    "    image = np.array(image)\n",
+    "    image = np.expand_dims(image, axis=0)  # Expand dimensions to match the model input shape\n",
+    "    \n",
+    "    # Predict\n",
+    "    prediction = model.predict(image)\n",
+    "    \n",
+    "    # Print the shape of the prediction to debug\n",
+    "    print(f\"Prediction shape: {prediction.shape}\")\n",
+    "    \n",
+    "    # Assuming the output is already softmax probabilities\n",
+    "    probabilities = prediction[0]\n",
+    "    \n",
+    "    # Print the probabilities array to debug\n",
+    "    print(f\"Probabilities: {probabilities}\")\n",
+    "    \n",
+    "    # Assuming your model was trained with these class names\n",
+    "    class_names = ['charmander', 'eevee', 'pikachuu']  # Replace 'another_pokemon' with your third class name\n",
+    "    \n",
+    "    # Create a dictionary of class probabilities\n",
+    "    result = {class_names[i]: float(probabilities[i]) for i in range(len(class_names))}\n",
+    "    \n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7866\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7866/\" 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": 19,
+     "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[1m0s\u001b[0m 140ms/step\n",
+      "Prediction shape: (1, 3)\n",
+      "Probabilities: [9.1263162e-31 1.1169604e-30 1.0000000e+00]\n",
+      "<class 'numpy.ndarray'>\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step\n",
+      "Prediction shape: (1, 3)\n",
+      "Probabilities: [4.4493477e-06 8.4401548e-01 1.5598010e-01]\n",
+      "<class 'numpy.ndarray'>\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step\n",
+      "Prediction shape: (1, 3)\n",
+      "Probabilities: [9.9999964e-01 1.0916104e-07 1.8336594e-07]\n",
+      "<class 'numpy.ndarray'>\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step\n",
+      "Prediction shape: (1, 3)\n",
+      "Probabilities: [5.0329237e-04 8.8987160e-01 1.0962512e-01]\n",
+      "<class 'numpy.ndarray'>\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step\n",
+      "Prediction shape: (1, 3)\n",
+      "Probabilities: [9.1263162e-31 1.1169604e-30 1.0000000e+00]\n",
+      "<class 'numpy.ndarray'>\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
+      "Prediction shape: (1, 3)\n",
+      "Probabilities: [4.4493477e-06 8.4401548e-01 1.5598010e-01]\n",
+      "<class 'numpy.ndarray'>\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step\n",
+      "Prediction shape: (1, 3)\n",
+      "Probabilities: [5.0329237e-04 8.8987160e-01 1.0962512e-01]\n",
+      "<class 'numpy.ndarray'>\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
+      "Prediction shape: (1, 3)\n",
+      "Probabilities: [5.0329237e-04 8.8987160e-01 1.0962512e-01]\n",
+      "<class 'numpy.ndarray'>\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step\n",
+      "Prediction shape: (1, 3)\n",
+      "Probabilities: [9.9999964e-01 1.0916104e-07 1.8336594e-07]\n",
+      "<class 'numpy.ndarray'>\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step\n",
+      "Prediction shape: (1, 3)\n",
+      "Probabilities: [4.0465540e-22 8.3268744e-22 1.0000000e+00]\n",
+      "<class 'numpy.ndarray'>\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step\n",
+      "Prediction shape: (1, 3)\n",
+      "Probabilities: [9.9999964e-01 1.0916104e-07 1.8336594e-07]\n",
+      "<class 'numpy.ndarray'>\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
+      "Prediction shape: (1, 3)\n",
+      "Probabilities: [5.0329237e-04 8.8987160e-01 1.0962512e-01]\n"
+     ]
+    }
+   ],
+   "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=[\"pokemon_examples/charmander.png\", \"pokemon_examples/charmander1.jpg\", \"pokemon_examples/eevee.png\", \"pokemon_examples/eevee1.jpg\", \"pokemon_examples/pika.png\", \"pokemon_examples/pika1.jpg\"],   \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": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

@@ -0,0 +1 @@
+tensorflow

transferlearning_pokemon.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:563a61546dce4eaa9f2f0e206f709de37518ecc0fd81baea33a299b27e598f95
+size 250560275