Upload 4 files

app.py

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+import os
+from flask import Flask, request, render_template
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+app = Flask(__name__)
+
+# Load the trained model
+model = load_model('bone_fracture/bone_model.h5')  # Update with your model's file path
+
+# Define class labels
+class_labels = ['Not Fractured', 'Fractured']
+
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    if request.method == 'POST':
+        # Get the uploaded file from the form
+        file = request.files['file']
+        if file:
+            # Save the file temporarily
+            temp_path = 'temp.jpg'
+            file.save(temp_path)
+            
+            # Load and preprocess the image
+            img = image.load_img(temp_path, target_size=(224, 224))
+            img_array = image.img_to_array(img)
+            img_array = np.expand_dims(img_array, axis=0)
+            img_array /= 255.0
+            
+            # Make a prediction
+            prediction = model.predict(img_array)
+            predicted_class = int(np.round(prediction)[0][0])
+            predicted_label = class_labels[predicted_class]
+            
+            # Delete the temporary file
+            os.remove(temp_path)
+            
+            return render_template('result.html', prediction=predicted_label)
+    
+    return render_template('index.html')
+
+if __name__ == '__main__':
+    app.run(debug=True)

bone.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout\n",
+    "from keras import Sequential\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.preprocessing import image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Zip file extracted successfully.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from zipfile import ZipFile\n",
+    "\n",
+    "zip_file_path = 'bone_frac.zip'\n",
+    "\n",
+    "with ZipFile(zip_file_path, 'r') as zip_ref:\n",
+    "    zip_ref.extractall()\n",
+    "\n",
+    "print(\"Zip file extracted successfully.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Training = 'archive (6)/train'\n",
+    "Validation = 'archive (6)/val'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_width, img_height = 224, 224\n",
+    "batch_size = 32"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 8863 images belonging to 2 classes.\n",
+      "Found 600 images belonging to 2 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Data Augmentation\n",
+    "\n",
+    "train_datagen = ImageDataGenerator(\n",
+    "    rescale=1.0/255,\n",
+    "    rotation_range=20,\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",
+    ")\n",
+    "\n",
+    "#Rescale validation images \n",
+    "validation_datagen = ImageDataGenerator(rescale=1.0/255)\n",
+    "\n",
+    "#loading train n val data:\n",
+    "\n",
+    "train_generator = train_datagen.flow_from_directory(\n",
+    "    Training,\n",
+    "    target_size=(img_width, img_height),\n",
+    "    batch_size=batch_size,\n",
+    "    class_mode='binary'\n",
+    ")\n",
+    "\n",
+    "validation_generator = validation_datagen.flow_from_directory(\n",
+    "    Validation,\n",
+    "    target_size=(img_width, img_height),\n",
+    "    batch_size=batch_size,\n",
+    "    class_mode='binary'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#building CNN model\n",
+    "\n",
+    "model = Sequential()\n",
+    "\n",
+    "model.add(Conv2D(32, (3,3), activation='relu', input_shape=(img_width, img_height, 3)))\n",
+    "model.add(MaxPooling2D((2,2)))\n",
+    "\n",
+    "model.add(Conv2D(64, (3,3), activation='relu'))\n",
+    "model.add(MaxPooling2D((2,2)))\n",
+    "\n",
+    "model.add(Conv2D(128, (3,3), activation='relu'))\n",
+    "model.add(MaxPooling2D((2,2)))\n",
+    "\n",
+    "model.add(Flatten())\n",
+    "\n",
+    "model.add(Dense(128, activation='relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "\n",
+    "model.add(Dense(1, activation='sigmoid'))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer='Adam', loss='binary_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "276/276 [==============================] - 450s 2s/step - loss: 0.6812 - accuracy: 0.5570 - val_loss: 0.6559 - val_accuracy: 0.5533\n",
+      "Epoch 2/5\n",
+      "276/276 [==============================] - 457s 2s/step - loss: 0.6691 - accuracy: 0.5919 - val_loss: 0.6212 - val_accuracy: 0.6000\n",
+      "Epoch 3/5\n",
+      "276/276 [==============================] - 301s 1s/step - loss: 0.6513 - accuracy: 0.5942 - val_loss: 0.5682 - val_accuracy: 0.6800\n",
+      "Epoch 4/5\n",
+      "276/276 [==============================] - 302s 1s/step - loss: 0.6283 - accuracy: 0.6159 - val_loss: 0.6609 - val_accuracy: 0.5000\n",
+      "Epoch 5/5\n",
+      "276/276 [==============================] - 303s 1s/step - loss: 0.6163 - accuracy: 0.6440 - val_loss: 0.5883 - val_accuracy: 0.6767\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit(\n",
+    "    train_generator,\n",
+    "    steps_per_epoch=train_generator.samples / batch_size,\n",
+    "    validation_data=validation_generator,\n",
+    "    validation_steps=(validation_generator.samples / batch_size),\n",
+    "    epochs=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19/19 [==============================] - 4s 203ms/step - loss: 0.5883 - accuracy: 0.6767\n",
+      "Test accuracy:  67.67%\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_loss, test_acc = model.evaluate(validation_generator)\n",
+    "print(f'Test accuracy: {test_acc * 100: .2f}%') #.2f means float no. upto 2 decimals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.save('bone_model.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = tf.keras.models.load_model('bone_model.h5')\n",
+    "\n",
+    "img_path = 'archive (6)/val/fractured\\9.jpg'\n",
+    "img = image.load_img(img_path, target_size=(224,224))\n",
+    "img_array = image.img_to_array(img)\n",
+    "img_array = np.expand_dims(img_array, axis=0)\n",
+    "img_array /= 255.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 76ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "#making prediction\n",
+    "\n",
+    "prediction = model.predict(img_array)\n",
+    "predicted_class=int(np.round(prediction)[0][0]) #[0][0]\n",
+    "\n",
+    "class_labels = ['Not Fractured', 'Fractured']\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class: Fractured (Confidence: 57.78%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Predicted class: {class_labels[predicted_class]} (Confidence: {prediction[0][0] * 100:.2f}%)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.7"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

bone_frac.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a3904d875f3e408293142724a4150819c0b6fce1da77d70211dfae9b780c859f
+size 180678817

bone_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:81fbc842860855112b3ea1eaacb1d46f0062956637377056d35b592b76df1541
+size 134080488