Upload 4 files

app.py

@@ -0,0 +1,40 @@
+import streamlit as st
+import pandas as pd 
+import numpy as np
+from tensorflow import keras
+import tensorflow as tf
+from keras.models import load_model
+import matplotlib.pyplot as plt
+from PIL import Image
+
+st.title('Cancer Type Detection')
+
+# Load the saved model outside the prediction function
+loaded_model = load_model('cnn_model.h5')
+
+def prediction(file):
+    img = tf.keras.utils.load_img(file, target_size=(224, 224))
+    x = tf.keras.utils.img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+
+    # Predict the class probabilities
+    classes = loaded_model.predict(x)
+
+    # Get the predicted class label
+    classes = np.ravel(classes) # convert to 1D array
+    idx = np.argmax(classes)
+    clas = ['adenocarcinoma', 'large.cell.carcinoma', 'normal', 'squamous.cell.carcinoma'][idx]
+
+    return clas
+
+uploaded_file = st.file_uploader("Choose MRI file")
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    image = image.resize((224, 224))
+    image = tf.keras.preprocessing.image.img_to_array(image)
+    image = image / 255.0
+    image = tf.expand_dims(image, axis=0)
+  
+if st.button('Predict'): 
+    result = prediction(uploaded_file)
+    st.write('Prediction is {}'.format(result))

cnn_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e24965794f88afb83c503084b57f6cdf7615b833f29ed5776ffac9d9137f46be
+size 89306144

model_inferences.ipynb

@@ -0,0 +1,156 @@
+{
+  "cells": [
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FjDGwlCJYO2m"
+      },
+      "source": [
+        "### Model Inferences"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "DUXRPvLRxpJe"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd \n",
+        "import numpy as np\n",
+        "from tensorflow import keras\n",
+        "import tensorflow as tf\n",
+        "import pickle\n",
+        "from keras.models import load_model\n",
+        "from tensorflow.keras.preprocessing import image \n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WOLLnPnAuzKm",
+        "outputId": "644f852b-b695-40d8-db1c-3a8e502ca316"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9nTgKQUKu61s",
+        "outputId": "3105e278-99eb-468d-8890-4079916391b2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "/content/drive/MyDrive/cnn_model\n"
+          ]
+        }
+      ],
+      "source": [
+        "%cd /content/drive/MyDrive/cnn_model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "ij3PCeoCyU23"
+      },
+      "outputs": [],
+      "source": [
+        "def prediction(file):\n",
+        "    img = tf.keras.utils.load_img(file, target_size=(224, 224))\n",
+        "    x = tf.keras.utils.img_to_array(img)\n",
+        "    x = np.expand_dims(x, axis=0)\n",
+        "\n",
+        "    # Load the saved model\n",
+        "    loaded_model = load_model('cnn_model.h5')\n",
+        "\n",
+        "    # Predict the class probabilities\n",
+        "    classes = loaded_model.predict(x)\n",
+        "\n",
+        "    # Get the predicted class label\n",
+        "    classes = np.ravel(classes) # convert to 1D array\n",
+        "    idx = np.argmax(classes)\n",
+        "    clas = ['adenocarcinoma', 'large.cell.carcinoma', 'normal', 'squamous.cell.carcinoma']\n",
+        "\n",
+        "    # Print the predicted class label\n",
+        "    print('Prediction is a {}'.format(clas[idx]))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "h4xpRFtUw-OR",
+        "outputId": "6af2e2d1-3f78-4a9a-a7b2-b8e83db72e6b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "1/1 [==============================] - 2s 2s/step\n",
+            "Prediction is a normal\n"
+          ]
+        }
+      ],
+      "source": [
+        "prediction('Adenocarcinoma-in-situ-Axial-contrast-enhanced-chest-CT-scan-with-lung-window.png')"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VG4syGUcYjSK"
+      },
+      "source": [
+        "Prediction wrong, the model should maintained for next utilization"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.7.16"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}