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app.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -10,67 +10,67 @@
      "output_type": "stream",
      "text": [
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+      "Requirement already satisfied: markdown-it-py>=2.2.0 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (3.0.0)\n",
       "Requirement already satisfied: exceptiongroup>=1.0.2 in /Users/matis/Library/Python/3.10/lib/python/site-packages (from anyio->httpx>=0.24.1->gradio) (1.1.3)\n",
       "Requirement already satisfied: charset-normalizer<4,>=2 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from requests->huggingface-hub>=0.19.3->gradio) (3.3.0)\n",
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@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -152,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,15 +182,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7864\n",
-      "Running on public URL: https://625988e4933385a0d9.gradio.live\n",
+      "Running on local URL:  http://127.0.0.1:7865\n",
+      "Running on public URL: https://f965835b2ebd72ebef.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"
      ]
@@ -198,7 +198,7 @@
     {
      "data": {
       "text/html": [
-       "<div><iframe src=\"https://625988e4933385a0d9.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+       "<div><iframe src=\"https://f965835b2ebd72ebef.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>"
@@ -211,9 +211,17 @@
      "data": {
       "text/plain": []
      },
-     "execution_count": 11,
+     "execution_count": 15,
      "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 572ms/step\n"
+     ]
     }
    ],
    "source": [

pokemon-deploy.py

@@ -3,6 +3,10 @@ import tensorflow as tf
 import numpy as np
 from PIL import Image
 
+
+model_path = "pokemon-predict-model_transferlearning.keras"
+model = tf.keras.models.load_model(model_path)
+
 # Define the core prediction function
 def predict_pokemon(image):
     # Preprocess image