Upload 5 files

Custom_CNN_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6f54181c11298ee4f4b6715703375b6c05a9b47c5ead4d04e1bc947ab9d2d5f3
+size 286930648

ResNet50_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f2a5663bda0f150db9e72cc6ef2224c878e8b9ae81dd31310f8d4d52663c9931
+size 98091912

VGG16_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e0e2ef9c9aa645a295dcc48d39801df472e19dee4d1ec16e15308fe5727a3d71
+size 59745168

app_py.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jaqWRbrAqXI2"
+      },
+      "outputs": [],
+      "source": [
+        "import gradio as gr\n",
+        "import tensorflow as tf\n",
+        "import numpy as np\n",
+        "from PIL import Image\n",
+        "\n",
+        "# ขนาดภาพที่ใช้ในโมเดล\n",
+        "IMG_SIZE = (224, 224)\n",
+        "\n",
+        "# สร้าง Dictionary ที่เก็บชื่อโมเดลและ path ไฟล์ .h5\n",
+        "model_paths = {\n",
+        "    \"Custom CNN\": \"Custom_CNN_model.h5\",\n",
+        "    \"VGG16\": \"VGG16_model.h5\",\n",
+        "    \"ResNet50\": \"ResNet50_model.h5\"\n",
+        "}\n",
+        "\n",
+        "# ฟังก์ชันเตรียมข้อมูลภาพ\n",
+        "def preprocess_image(image):\n",
+        "    image = image.resize(IMG_SIZE)  # Resize\n",
+        "    image = np.array(image) / 255.0  # Normalize\n",
+        "    image = np.expand_dims(image, axis=0)  # เพิ่ม batch dimension\n",
+        "    return image\n",
+        "\n",
+        "# ฟังก์ชันทำนาย โดยเลือกโมเดล\n",
+        "def predict_with_model(image, model_name):\n",
+        "    # โหลดโมเดลที่เลือก\n",
+        "    model = tf.keras.models.load_model(model_paths[model_name])\n",
+        "\n",
+        "    # เตรียมภาพ\n",
+        "    processed_image = preprocess_image(image)\n",
+        "\n",
+        "    # ทำนายผล\n",
+        "    prediction = model.predict(processed_image)[0][0]  # ได้ค่าความน่าจะเป็น\n",
+        "    class_name = \"Stroke\" if prediction > 0.5 else \"Non-Stroke\"\n",
+        "    confidence = round(float(prediction if prediction > 0.5 else 1 - prediction) * 100, 2)\n",
+        "\n",
+        "    # คืนผลลัพธ์\n",
+        "    return f\"Class: {class_name} (Confidence: {confidence}%)\"\n",
+        "\n",
+        "# Gradio Interface\n",
+        "interface = gr.Interface(\n",
+        "    fn=predict_with_model,\n",
+        "    inputs=[\n",
+        "        gr.Image(type=\"pil\", label=\"Upload Face Image\"),\n",
+        "        gr.Dropdown(choices=[\"Custom CNN\", \"VGG16\", \"ResNet50\"], label=\"Select Model\")\n",
+        "    ],\n",
+        "    outputs=\"text\",\n",
+        "    title=\"Stroke Face Classification\",\n",
+        "    description=\"Upload a face image to predict whether the person has stroke or not. Select model to classify.\"\n",
+        ")\n",
+        "\n",
+        "# Run app\n",
+        "if __name__ == \"__main__\":\n",
+        "    interface.launch()\n"
+      ]
+    }
+  ]
+}

requirements.txt

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+tensorflow
+gradio
+numpy
+pillow