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Model_Training_onepiece.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import der benötigten Bibliotheken\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.applications import ResNet50\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout\n",
+    "from tensorflow.keras.models import Model\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vorbereitung der Daten\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 78 images belonging to 6 classes.\n",
+      "Found 16 images belonging to 6 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Daten-Vorbereitung\n",
+    "base_dir = 'C:\\Daten\\Studium Wirtschaftsinformatik\\Semester 6 TZ\\KI-Anwendungen\\Übungen\\Übung2\\Abschluss\\Datensätze\\Strohhüte'  # Pfad zum übergeordneten Ordner, der die Klassenordner enthält\n",
+    "datagen = ImageDataGenerator(\n",
+    "    rescale=1./255,\n",
+    "    rotation_range=40,\n",
+    "    width_shift_range=0.2,\n",
+    "    height_shift_range=0.2,\n",
+    "    shear_range=0.2,\n",
+    "    zoom_range=0.2,\n",
+    "    horizontal_flip=True,\n",
+    "    fill_mode='nearest',\n",
+    "    validation_split=0.2  # Behalte die Aufteilung für Training und Validation bei\n",
+    ")\n",
+    "train_generator = datagen.flow_from_directory(\n",
+    "    base_dir,\n",
+    "    target_size=(224, 224),  # Assuming using ResNet input dimensions\n",
+    "    batch_size=32,  # Adjust according to your system capability\n",
+    "    class_mode='categorical',\n",
+    "    subset='training'  # Use the 'subset' argument for splitting\n",
+    ")\n",
+    "\n",
+    "validation_generator = datagen.flow_from_directory(\n",
+    "    base_dir,\n",
+    "    target_size=(224, 224),\n",
+    "    batch_size=32,\n",
+    "    class_mode='categorical',\n",
+    "    subset='validation'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Modell Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Modell-Setup\n",
+    "base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
+    "base_model.trainable = False  # Zuerst wird das Basismodell eingefroren\n",
+    "\n",
+    "x = GlobalAveragePooling2D()(base_model.output)\n",
+    "x = Dense(1024, activation='relu')(x)\n",
+    "x = Dropout(0.5)(x)  # Dropout hinzugefügt, um Overfitting zu reduzieren\n",
+    "predictions = Dense(3, activation='softmax')(x)\n",
+    "\n",
+    "model = Model(inputs=base_model.input, outputs=predictions)\n",
+    "model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Training des Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Arguments `target` and `output` must have the same shape. Received: target.shape=(None, 6), output.shape=(None, 3)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[4], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Trainieren des Modells\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrain_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43msteps_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_generator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msamples\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtrain_generator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_generator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msamples\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mvalidation_generator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m        \u001b[49m\u001b[43mModelCheckpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbest_model.keras\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_best_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[43m        \u001b[49m\u001b[43mEarlyStopping\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmonitor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mval_loss\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpatience\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     11\u001b[0m \u001b[43m        \u001b[49m\u001b[43mReduceLROnPlateau\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmonitor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mval_loss\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfactor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpatience\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     12\u001b[0m \u001b[43m    \u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m     13\u001b[0m \u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Jeremy Kuwegu\\anaconda3\\envs\\kia\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+      "File \u001b[1;32mc:\\Users\\Jeremy Kuwegu\\anaconda3\\envs\\kia\\lib\\site-packages\\keras\\src\\backend\\tensorflow\\nn.py:554\u001b[0m, in \u001b[0;36mcategorical_crossentropy\u001b[1;34m(target, output, from_logits, axis)\u001b[0m\n\u001b[0;32m    552\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m e1, e2 \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(target\u001b[38;5;241m.\u001b[39mshape, output\u001b[38;5;241m.\u001b[39mshape):\n\u001b[0;32m    553\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m e1 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m e2 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m e1 \u001b[38;5;241m!=\u001b[39m e2:\n\u001b[1;32m--> 554\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    555\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mArguments `target` and `output` must have the same shape. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    556\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReceived: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    557\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtarget.shape=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtarget\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, output.shape=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    558\u001b[0m         )\n\u001b[0;32m    560\u001b[0m output, from_logits \u001b[38;5;241m=\u001b[39m _get_logits(\n\u001b[0;32m    561\u001b[0m     output, from_logits, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSoftmax\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcategorical_crossentropy\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    562\u001b[0m )\n\u001b[0;32m    563\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m from_logits:\n",
+      "\u001b[1;31mValueError\u001b[0m: Arguments `target` and `output` must have the same shape. Received: target.shape=(None, 6), output.shape=(None, 3)"
+     ]
+    }
+   ],
+   "source": [
+    "# Trainieren des Modells\n",
+    "history = model.fit(\n",
+    "    train_generator,\n",
+    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
+    "    validation_data=validation_generator,\n",
+    "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
+    "    epochs=20,\n",
+    "    callbacks=[\n",
+    "        ModelCheckpoint('best_model.keras', save_best_only=True),\n",
+    "        EarlyStopping(monitor='val_loss', patience=5),\n",
+    "        ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=2)\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fine Tuning des Modells"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m148s\u001b[0m 12s/step - accuracy: 0.7070 - loss: 1.0460 - val_accuracy: 0.6094 - val_loss: 0.9729\n",
+      "Epoch 2/10\n",
+      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 1.0000 - loss: 0.0769 - val_accuracy: 0.5714 - val_loss: 1.0434\n",
+      "Epoch 3/10\n",
+      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 10s/step - accuracy: 0.9677 - loss: 0.1108 - val_accuracy: 0.5469 - val_loss: 0.9639\n",
+      "Epoch 4/10\n",
+      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 111ms/step - accuracy: 1.0000 - loss: 0.0381 - val_accuracy: 0.7143 - val_loss: 0.9019\n",
+      "Epoch 5/10\n",
+      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 10s/step - accuracy: 0.9992 - loss: 0.0220 - val_accuracy: 0.2969 - val_loss: 1.1206\n",
+      "Epoch 6/10\n",
+      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 164ms/step - accuracy: 1.0000 - loss: 0.0226 - val_accuracy: 0.1429 - val_loss: 1.1233\n",
+      "Epoch 7/10\n",
+      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 10s/step - accuracy: 1.0000 - loss: 0.0062 - val_accuracy: 0.1719 - val_loss: 1.4363\n",
+      "Epoch 8/10\n",
+      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 103ms/step - accuracy: 0.9688 - loss: 0.0287 - val_accuracy: 0.1429 - val_loss: 1.4406\n",
+      "Epoch 9/10\n",
+      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 10s/step - accuracy: 0.9907 - loss: 0.0160 - val_accuracy: 0.2344 - val_loss: 1.4151\n",
+      "Epoch 10/10\n",
+      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 101ms/step - accuracy: 1.0000 - loss: 0.0038 - val_accuracy: 0.0000e+00 - val_loss: 1.6847\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Fine-Tuning des Modells\n",
+    "for layer in base_model.layers:\n",
+    "    layer.trainable = True\n",
+    "\n",
+    "model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "history_fine = model.fit(\n",
+    "    train_generator,\n",
+    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
+    "    validation_data=validation_generator,\n",
+    "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
+    "    epochs=10\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bewertung und Ergebnisse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━��━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3s/step - accuracy: 0.2708 - loss: 1.4508\n",
+      "Performance vor dem Fine-Tuning: 0.640625\n",
+      "Performance nach dem Fine-Tuning: 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Ergebnisse bewerten\n",
+    "eval_result = model.evaluate(validation_generator, steps=validation_generator.samples // validation_generator.batch_size)\n",
+    "print(f'Performance vor dem Fine-Tuning: {history.history[\"val_accuracy\"][-1]}')\n",
+    "print(f'Performance nach dem Fine-Tuning: {history_fine.history[\"val_accuracy\"][-1]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+     ]
+    }
+   ],
+   "source": [
+    "model.save('mein_modell.h5') "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kia",
+   "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": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

app.py

@@ -0,0 +1,32 @@
+import gradio as gr
+from PIL import Image
+import numpy as np
+from tensorflow.keras.preprocessing import image as keras_image
+from tensorflow.keras.applications.resnet50 import preprocess_input
+from tensorflow.keras.models import load_model
+
+# Load your trained model
+model = load_model(r'C:\Daten\Studium Wirtschaftsinformatik\Semester 6 TZ\KI-Anwendungen\Übungen\Übung2\Abschluss\mein_modell.h5')
+
+def predict_character(img):
+    img = Image.fromarray(img.astype('uint8'), 'RGB')  # Ensure the image is in RGB
+    img = img.resize((224, 224))  # Resize the image to the input size of the model
+    img_array = keras_image.img_to_array(img)  # Convert the image to an array
+    img_array = np.expand_dims(img_array, axis=0)  # Expand dimensions to match model input
+    img_array = preprocess_input(img_array)  # Preprocess the input as expected by ResNet50
+    
+    prediction = model.predict(img_array)  # Predict using the model
+    classes = ['Chopper', 'Nami', 'Ruffy', 'Sanji', 'Usopp', 'Zoro']  # Character names as per your dataset
+    return {classes[i]: float(prediction[0][i]) for i in range(len(classes))}  # Return the prediction in a dictionary format
+
+# Define Gradio interface
+interface = gr.Interface(
+    fn=predict_character, 
+    inputs=gr.Image(),  # Gradio handles resizing automatically based on the model input
+    outputs=gr.Label(num_top_classes=6),  # Show top 3 predictions
+    title="One Piece Character Classifier",
+    description="Upload an image of a One Piece character and the classifier will predict which character it is."
+)
+
+# Launch the interface
+interface.launch()

requirements.txt

@@ -0,0 +1,18 @@
+blinker==1.7.0
+click==8.1.7
+Flask==3.0.2
+Flask-Cors==4.0.0
+itsdangerous==2.1.2
+Jinja2==3.1.3
+joblib==1.3.2
+MarkupSafe==2.1.5
+numpy==1.26.4
+pandas==2.2.1
+python-dateutil==2.8.2
+pytz==2024.1
+scikit-learn==1.4.1.post1
+scipy==1.12.0
+six==1.16.0
+threadpoolctl==3.3.0
+tzdata==2024.1
+Werkzeug==3.0.1