Delinted and did a minor restructure of html_output branch

readable.

.gitignore

@@ -113,4 +113,6 @@ venv.bak/
 *.model
 *.csv
 *.txt
+results.html
 !requirements.txt
+!requirements-gpu.txt

Makefile

@@ -1,95 +0,0 @@
-###############################################################################
-# Parts of this are borrowed from the HTTPie Makefile. Thanks!
-###############################################################################
-
-REQUIREMENTS="requirements-dev.txt"
-TAG="\n\n\033[0;32m\#\#\# "
-END=" \#\#\# \033[0m\n"
-
-PIP=$(shell which pip3)
-CONDA=$(shell which conda)
-CONDA_HOME="$(HOME)/anaconda3"
-VENV="eyeballer_venv"
-
-SHELL := /bin/bash
-.ONE_SHELL:
-
-all: test
-
-# Target-specific assignments
-conda: PIP := "$(CONDA_HOME)/envs/$(VENV)/bin/pip"
-conda init: uninstall-eyeballer
-
-ifeq ($(strip $(MAKECMDGOALS)),conda)
-	@echo -e $(TAG)Conda detected at: $(CONDA)$(END)
-	conda create -yn $(VENV) | true
-
-	source $(CONDA_HOME)/etc/profile.d/conda.sh; \
-	conda activate $(VENV); \
-	conda install -y pip
-endif
-	@echo -e $(TAG)Installing dev requirements$(END)
-	$(PIP) install --upgrade -r $(REQUIREMENTS)
-
-	@echo -e $(TAG)Installing Eyeballer$(END)
-	$(PIP) install --upgrade --editable .
-
-ifeq ($(strip $(MAKECMDGOALS)),conda)
-	@echo -e $(TAG)Activate virtualenv to run eyeballer: $(END)
-	@echo conda activate $(VENV)
-endif
-
-	@echo
-
-clean:
-	@echo -e $(TAG)Cleaning up$(END)
-	rm -rf *.egg dist build .cache .pytest_cache eyeballer.egg-info
-	find . -name '__pycache__' -delete -print -o -name '*.pyc' -delete -print
-ifneq ($(strip $(CONDA)),)
-	conda env remove -y --name eyeballer_venv | true
-endif
-	@echo
-
-
-###############################################################################
-# Testing
-###############################################################################
-
-
-test:
-	@echo -e $(TAG)Running tests on the current Python interpreter with coverage $(END)
-	python -m unittest tests
-	@echo
-
-
-# test-all is meant to test everything — even this Makefile
-test-all: uninstall-all clean init test pycodestyle
-	@echo
-
-
-pycodestyle:
-	which pycodestyle || $(PIP) install pycodestyle
-	pycodestyle
-	@echo
-
-
-###############################################################################
-# Uninstalling
-###############################################################################
-
-uninstall-eyeballer:
-	@echo using pip3 located at: $(PIP)
-	@echo -e $(TAG)Uninstalling eyeballer$(END)
-	- $(PIP) uninstall --yes eyeballer &2>/dev/null
-
-	@echo "Verifying…"
-	cd .. && ! python -m eyeballer --version &2>/dev/null
-
-	@echo "Done"
-	@echo
-
-
-uninstall-all: uninstall-eyeballer
-
-	@echo -e $(TAG)Uninstalling development requirements$(END)
-	- $(PIP) uninstall --yes -r $(REQUIREMENTS)

README.md

@@ -3,25 +3,16 @@ Give those screenshots of yours a quick eyeballing.
 
 Eyeballer is meant for large-scope network penetration tests where you need to find "interesting" targets from a huge set of web-based hosts. Go ahead and use your favorite screenshotting tool like normal (EyeWitness or GoWitness) and then run them through Eyeballer to tell you what's likely to contain vulnerabilities, and what isn't.
 
-## Installation
+## Setup
 
-To install in a python venv (Recommended):
+Download required packages on pip:
 ```
-python3 -m venv venv
-source venv/bin/activate
-make init
+sudo pip3 install -r requirements.txt
 ```
 
-To install for Conda (automatically creates conda venv):
-
+Or if you want GPU support:
 ```
-make conda
-```
-
-## Running Tests
-
-```
-make test
+sudo pip3 install -r requirements-gpu.txt
 ```
 
 ## Predicting Labels

eyeballer.py

@@ -5,6 +5,7 @@ import csv
 
 from eyeballer.model import EyeballModel, DATA_LABELS
 from eyeballer.visualization import HeatMap
+from jinja2 import Template
 
 
 @click.group(invoke_without_command=True)
@@ -37,8 +38,9 @@ def train(ctx, graphs, batchsize, epochs):
 @cli.command()
 @click.argument('screenshot')
 @click.option('--heatmap', default=False, is_flag=True, help="Create a heatmap graphfor the prediction")
+@click.option('--threshold', default=.5, type=float, help="Threshold confidence for labeling")
 @click.pass_context
-def predict(ctx, screenshot, heatmap):
+def predict(ctx, screenshot, heatmap, threshold):
     model = EyeballModel(**ctx.obj['model_kwargs'])
     results = model.predict(screenshot)
 
@@ -56,18 +58,61 @@ def predict(ctx, screenshot, heatmap):
             labelwriter = csv.DictWriter(csvfile, fieldnames=fieldnames)
             labelwriter.writeheader()
             labelwriter.writerows(results)
+
         print("Output written to results.csv")
+        buildHTML(processResults(results))
+        print("HTML written to results.html")
+
+
+def processResults(results):
+    '''Filter the initial results dictionary and reformat it for use in JS.
+
+        Keyword arguments:
+        results -- dictionary output from predict function
+
+    '''
+    jsResults = {}
+
+    for result in results:
+        positiveTags = []
+
+        for label, label_info in result.items():
+            if (label == 'filename'):
+                pass
+
+            elif label_info:
+                positiveTags.append(label)
+
+        jsResults[result['filename']] = positiveTags
+    return(jsResults)
+
+
+def buildHTML(jsResults):
+    '''Build HTML around the JS Dictionary that is passed from processResults.
+
+        Keyword arguments:
+        jsResults -- dictionary output from processResults function
+    '''
+    html_output = ""
+    with open("prediction_output_template.html") as template_file:
+        template = Template(template_file.read())
+        html_output = template.render(jsResults=jsResults)
+
+    with open('results.html', 'w') as file:
+        file.write(html_output)
 
 
 def pretty_print_evaluation(results):
     """Print a human-readable summary of the evaluation"""
     # We use 4.2% to handle all the way from "  0.00%" (7chars) to "100.00%" (7chars)
     for label in DATA_LABELS:
-        print(f"{label} Precision Score: {results[label]['precision']:4.2%}")
-        print(f"{label} Recall Score: {results[label]['recall']:4.2%}")
-    print(f"Overall Binary Accuracy: {results['total_binary_accuracy']:4.2%}")
-    print(f"All or nothing accuracy: {results['all_or_nothing_accuracy']:4.2%}")
-    print(f"Top 10 worst predictions: {results['top_10_worst'][1]}")
+        print("{} Precision Score: {:4.2%}".format(label, results[label]['precision']))
+        print("{} Recall Score: {:4.2%}".format(label, results[label]['recall']))
+    print("'None of the above' Precision: {:4.2%}".format(results['none_of_the_above_precision']))
+    print("'None of the above' Recall: {:4.2%}".format(results['none_of_the_above_recall']))
+    print("All or nothing Accuracy: {:4.2%}".format(results['all_or_nothing_accuracy']))
+    print("Overall Binary Accuracy: {:4.2%}".format(results['total_binary_accuracy']))
+    print("Top 10 worst predictions: {}".format(results['top_10_worst'][1]))
 
 
 @cli.command()

eyeballer/__init__.py

@@ -1 +0,0 @@
-from eyeballer.model import EyeballModel  # noqa: F401

eyeballer/model.py

@@ -1,5 +1,6 @@
 import os
 import random
+import sys
 
 # Prevent Tkinter Dependency
 import matplotlib
@@ -18,27 +19,13 @@ from keras.layers import Dropout, Dense
 from keras.layers import GlobalAveragePooling2D
 from keras.models import Model
 from keras.optimizers import Adam
-from keras.preprocessing import image
 from keras.preprocessing.image import ImageDataGenerator
 from sklearn.metrics import classification_report, accuracy_score, hamming_loss
 from eyeballer.augmentation import EyeballerAugmentation
-from keras.callbacks import Callback
 
 DATA_LABELS = ["custom404", "login", "homepage", "oldlooking"]
 
 
-class PredictionManager(Callback):
-    def __init__(self):
-        super(PredictionManager, self).__init__()
-        self.predictions = []
-
-    def getPredictions(self):
-        return np.array(self.predictions)
-
-    def on_predict_batch_end(self, batch, logs=None):
-        self.predictions.append(logs['outputs'][0].tolist()[0])
-
-
 class EyeballModel:
     """The primary model class of Eyeballer.
 
@@ -50,7 +37,7 @@ class EyeballModel:
     image_width, image_height = 224, 224
     input_shape = (image_width, image_height, 3)
 
-    def __init__(self, print_summary=False, weights_file="weights.h5", seed=None):
+    def __init__(self, weights_file, print_summary=False, seed=None):
         """Constructor for model class.
 
         Keyword arguments:
@@ -90,15 +77,21 @@ class EyeballModel:
             self.random_seed = True
             self.seed = random.randint(0, 999999)
             print("No seed set, ", end='')
-        print(f"using seed: {self.seed}")
+        print("using seed: {}".format(self.seed))
         random.seed(self.seed)
         self.training_labels = self.training_labels.sample(frac=1)
 
         if weights_file is not None and os.path.isfile(weights_file):
-            self.model.load_weights(weights_file)
+            try:
+                self.model.load_weights(weights_file)
+            except OSError:
+                print("ERROR: Unable to open weights file '{}'".foramt(weights_file))
+                sys.exit(-1)
             print("Loaded model from file.")
         else:
-            print("No model loaded from file")
+            if weights_file is not None:
+                raise FileNotFoundError
+            print("WARN: No model loaded from file. Generating random model")
 
         # Data augmentation
         augmentor = Augmentor.Pipeline()
@@ -207,11 +200,12 @@ class EyeballModel:
         result["oldlooking"] = prediction[0][3]
         return result
 
-    def predict(self, path):
+    def predict(self, path, threshold=0.5):
         """Predict the labels for a single file
 
         Keyword arguments:
-        path -- The path to the file that we'll be evaluating
+        path -- The path to the file that we'll be evaluating.
+            NOTE: This actually has to be a directory containing a directory with our files. Because Keras is dumb. Don't ask.
         """
         # Is this a single file, or a directory?
         screenshots = []
@@ -223,23 +217,30 @@ class EyeballModel:
         else:
             raise FileNotFoundError
 
+        # Prepare the data
+        data_generator = ImageDataGenerator(
+            preprocessing_function=preprocess_input)
+        prediction_generator = data_generator.flow_from_directory(
+            directory=path,
+            target_size=(self.image_width, self.image_height),
+            shuffle=False,
+            class_mode=None)
+
+        predictions = self.model.predict_generator(
+            prediction_generator,
+            verbose=1,
+            steps=len(prediction_generator))
+
+        predictions = predictions > threshold
         results = []
-        for screenshot in screenshots:
-            # Load the image into memory
-            img = image.load_img(screenshot, target_size=(self.image_width, self.image_height))
-            img = image.img_to_array(img)
-            img = np.expand_dims(img, axis=0)
-            img = preprocess_input(img)
-
-            prediction = self.model.predict(img, batch_size=1)
+        for filename, prediction in zip(prediction_generator.filenames, predictions):
             result = dict()
-            result["filename"] = screenshot
-            result["custom404"] = prediction[0][0]
-            result["login"] = prediction[0][1]
-            result["homepage"] = prediction[0][2]
-            result["oldlooking"] = prediction[0][3]
+            result["filename"] = path + filename
+            result["custom404"] = prediction[0]
+            result["login"] = prediction[1]
+            result["homepage"] = prediction[2]
+            result["oldlooking"] = prediction[3]
             results.append(result)
-
         return results
 
     def evaluate(self, threshold=0.5):
@@ -282,44 +283,71 @@ class EyeballModel:
         else:
             print("Using evaluation set...")
 
-        prediction_manager = PredictionManager()
-
-        try:
-            self.model.predict_generator(
-                evaluation_generator,
-                callbacks=[prediction_manager],
-                verbose=1,
-                steps=len(evaluation_generator))
-        except KeyboardInterrupt:
-            print('\r\nJob interrupted (Ctrl+C): Outputting completed results')
-
-        predictions = prediction_manager.getPredictions()
-
-        # Truncates evaluation generator if test was interrupted
-        evaluation_generator._data = evaluation_generator.data[:predictions.shape[0]]
+        predictions = self.model.predict_generator(
+            evaluation_generator,
+            verbose=1,
+            steps=len(evaluation_generator))
 
         self._save_prediction_histograms(predictions)
+        print("predictions: ", type(predictions))
         predictions = predictions > threshold
-        ground_truth = evaluation_generator.data
+        ground_truth = self.evaluation_labels[DATA_LABELS].to_numpy()
+        filenames = self.evaluation_labels[["filename"]].to_numpy()
         stats = classification_report(ground_truth, predictions, target_names=DATA_LABELS, output_dict=True)
         stats["total_binary_accuracy"] = 1 - hamming_loss(ground_truth, predictions)
         stats["all_or_nothing_accuracy"] = accuracy_score(ground_truth, predictions)
-        stats["top_10_best"] = self._top_images(evaluation_generator, predictions, best=True)
-        stats["top_10_worst"] = self._top_images(evaluation_generator, predictions, best=False)
+        stats["top_10_best"] = self._top_images(filenames, ground_truth, predictions, best=True)
+        stats["top_10_worst"] = self._top_images(filenames, ground_truth, predictions, best=False)
+        stats["none_of_the_above_recall"] = self._none_of_the_above_recall(ground_truth, predictions)
+        stats["none_of_the_above_precision"] = self._none_of_the_above_precision(ground_truth, predictions)
         return stats
 
-    def _top_images(self, labels, predictions, top_k=10, best=False):
+    def _none_of_the_above_recall(self, labels, predictions):
+        """Returns the recall score for the 'none of the above' images.
+        That means, all the images that don't have a category.
+        """
+        total_count = 0
+        correct_count = 0
+        for item in zip(labels.astype(int), predictions.astype(int)):
+            # Is this a none of the above?
+            if not item[0].any():
+                total_count += 1
+                if not item[1].any():
+                    correct_count += 1
+        if total_count == 0:
+            print("WARNING: None of the Above Recall is NaN")
+            return 0
+        return correct_count / total_count
+
+    def _none_of_the_above_precision(self, labels, predictions):
+        """Returns the precision score for the 'none of the above' images.
+        That means, all the images that don't have a category.
+        """
+        total_count = 0
+        correct_count = 0
+        for item in zip(labels.astype(int), predictions.astype(int)):
+            # Is this a none of the above prediction?
+            if not item[1].any():
+                total_count += 1
+                if not item[0].any():
+                    correct_count += 1
+        if total_count == 0:
+            print("WARNING: None of the Above Precision is NaN")
+            return 0
+        return correct_count / total_count
+
+    def _top_images(self, filenames, ground_truth, predictions, top_k=10, best=False):
         """Collect top-k best or top-k worst predicted images
 
         Keyword arguments:
-        labels -- The keras generator that contains the filenames and data labels
+        ground_truth -- The correct labels
         predictions -- The numpy array of predictions
         top_k -- Top k elements
         best -- True/False. Calculate either the best or worst images
 
         :Return -- Tuple of top-k indicies and top-k filenames
         """
-        true_labels = np.array(labels.data).astype(float)
+        true_labels = np.array(ground_truth).astype(float)
         predictions = predictions.astype(float)
 
         differences = np.abs(predictions - true_labels).sum(axis=1)
@@ -332,7 +360,7 @@ class EyeballModel:
             indicies = np.flipud(indicies)
 
         for i in indicies[:top_k]:
-            top_file_list.append(labels.filenames[i])
+            top_file_list.append(filenames[i][0])
 
         return indicies[:top_k], top_file_list
 

eyeballer/visualization.py

@@ -36,7 +36,7 @@ class HeatMap():
                     heatmap, worst_score = self._get_heatmap(label, boxsize)
                     if worst_score > 0.5:
                         boxsize += 28
-                        print(f"Didn't get a good image for {label}. Trying again with a bigger boxsize: {boxsize}")
+                        print("Didn't get a good image for {}. Trying again with a bigger boxsize: {}".format(label, boxsize))
                     else:
                         heatmaps.append(heatmap)
                         labels.append(label)
@@ -59,7 +59,7 @@ class HeatMap():
             subplots.imshow(heatmap, alpha=0.5, interpolation='none')
             subplots.set_title(labels[0])
         plt.savefig(output_file)
-        print(f"Heatmap image written to: {output_file}")
+        print("Heatmap image written to: {}".format(output_file))
 
     def _get_heatmap(self, label, boxsize):
         worst_score = 1

prediction_output_template.html

@@ -0,0 +1,189 @@
+<!DOCTYPE HTML>
+<meta charset="utf-8"/>
+<html>
+<title>Eyeballer Results</title>
+<body>
+    <div id="btnsCont">
+      <button class="btn all" onclick="filterSelection('all')"> Show all</button>
+      <button class="btn" onclick="filterSelection('custom404')"> 404</button>
+      <button class="btn" onclick="filterSelection('login')"> Login</button>
+      <button class="btn" onclick="filterSelection('homepage')"> Home Page</button>
+      <button class="btn" onclick="filterSelection('oldlooking')"> Old Looking</button>
+        <select onchange="numOnPage=this.value; filterSelection([]);" style="float:right; margin:5px; margin-right:30px; height:30px; width:auto;">
+          <option value="25">25</option>
+          <option value="50">50</option>
+          <option value="100">100</option>
+          <option value="250">250</option>
+        </select>
+    </div>
+    <div id="btnsPage">
+    </div>
+    <div id="container">
+    </div>
+    <button onclick="topFunction()" id="topBtn" title="Go to top">Top</button>
+<script>
+    var results = {{jsResults}};
+    var selectedTags = new Array();
+    var btnContainer = document.getElementById("btnsCont");
+    var categoryBtns = btnContainer.getElementsByClassName("btn");
+    var numOnPage = 100;
+    var indexesToDisplay = [];
+    filterSelection([]);
+    for (var i = 0; i < categoryBtns.length; i++) {
+        categoryBtns[i].addEventListener("click", function () {
+            if (this.className.indexOf("active") > -1) {
+                this.className = this.className.replace(" active", "");
+            }
+            else {
+            this.className += " active";
+            }
+        });
+    }
+    function filterSelection(c) {
+        indexesToDisplay = [];
+        var currentIndex = -1;
+        if (c == "all") location.reload();
+        if (selectedTags.includes(c)!=true) selectedTags=selectedTags.concat(c);
+        else selectedTags.splice(selectedTags.indexOf(c),1);
+        for (var filename in results){
+                currentIndex++;
+                var tags = results[filename];
+                var isMissingTag = false;
+                for (var i in selectedTags){
+                    if (!tags.includes(selectedTags[i])){
+                        isMissingTag = true;
+                        break;
+                    }
+                }
+                if (isMissingTag === false){
+                    indexesToDisplay = indexesToDisplay.concat(currentIndex);
+                }
+            }
+        buildPageButtons(indexesToDisplay);
+        buildHTML(0, numOnPage);
+    }
+    function buildPageButtons(indexes){
+        numOfPages = Math.ceil(indexes.length/numOnPage);
+        var html = '';
+        for (var i = 0; i < numOfPages; i++){
+            html += ('<button class="btnpage" onclick="buildHTML(' + (i*numOnPage) + ', ' + ((i+1)*numOnPage) + '); activatePage(this);">' + (i+1) +'</button>');
+        }
+        document.getElementById('btnsPage').innerHTML = html;
+    }
+    function activatePage(pressedBtn){
+        var activeNow = document.getElementsByClassName('activePage')
+        for (var i = 0; i < activeNow.length; i++){
+            activeNow[i].className = activeNow[i].className.replace(" activePage", "");
+        }
+        pressedBtn.className += ' activePage';
+    }
+    function buildHTML(rangeMin, rangeMax){
+        var html = '';
+        if (rangeMax > indexesToDisplay.length) rangeMax=indexesToDisplay.length;
+        for (var i = rangeMin; i < rangeMax; i++){
+            index=indexesToDisplay[i];
+            filename = Object.keys(results)[index];
+            html += ('<div class="filterDiv"><a href=' + filename + ' target="_blank"> ' + results[filename].toString().replace(/,/g, ' ') + '</a><img class="res_pic" src="' + filename + '"></div>');
+        }
+        document.getElementById('container').innerHTML = html;
+    }
+    window.onscroll = function() {scrollFunction()};
+    function scrollFunction() {
+      if (document.body.scrollTop > 20 || document.documentElement.scrollTop > 20) {
+        document.getElementById("topBtn").style.display = "block";
+      } else {
+        document.getElementById("topBtn").style.display = "none";
+      }
+    }
+    function topFunction() {
+      document.body.scrollTop = 0; // For Safari
+      document.documentElement.scrollTop = 0; // For Chrome, Firefox, IE and Opera
+    }
+</script>
+    <style>
+    #topBtn {
+      display: none; /* Hidden by default */
+      position: fixed; /* Fixed/sticky position */
+      bottom: 20px; /* Place the button at the bottom of the page */
+      right: 30px; /* Place the button 30px from the right */
+      z-index: 99; /* Make sure it does not overlap */
+      border: none; /* Remove borders */
+      outline: none; /* Remove outline */
+      background-color: #193b72; /* Set a background color */
+      color: white; /* Text color */
+      cursor: pointer; /* Add a mouse pointer on hover */
+      padding: 15px; /* Some padding */
+      border-radius: 10px; /* Rounded corners */
+      font-size: 18px; /* Increase font size */
+      font-weight:bold;
+      border:solid;
+    }
+    #topBtn:hover {
+      background-color: #555;
+    }
+    .container {
+    overflow: hidden;
+    }
+    #btnsPage {
+    margin:10px;
+    }
+    .btnpage{
+        width:40px;
+        height:30px;
+        background-color: #f1f1f1;
+        outline: none;
+    }
+    .res_pic{
+    width:100%;
+    height:auto;
+    }
+    a{
+    font-weight:600;
+    padding:3px;
+    text-decoration: none;
+    color:#ffffff;
+    font-size:16px;
+    font-family:Sans-serif;
+    }
+    .name{
+    float:left;
+    }
+    .filterDiv {
+      float: left;
+      background-color: #777;
+      color: #ffffff;
+      width: 22%;
+      border-style:solid;
+      border-color:#666;
+      text-align: center;
+      margin: 10px;
+      line-height: 30px;
+      display: block;
+    }
+    .btn {
+      border: none;
+      outline: none;
+      padding: 12px 16px;
+      margin-left:10px;
+      background-color: #f1f1f1;
+      cursor: pointer;
+    }
+    .btn:hover {
+      background-color: #ddd;
+    }
+    .active {
+      background-color: #666;
+      color: white;
+    }
+    .activePage{
+        border: solid;
+        border-color:#666666;
+        color: black;
+    }
+    .btn.all {
+      background-color: #193b72;
+      color: white;
+    }
+    </style>
+</body>
+</html>

requirements-gpu.txt

@@ -0,0 +1,9 @@
+Augmentor
+click
+matplotlib
+numpy
+pandas
+pillow
+sklearn
+tensorflow-gpu
+keras

requirements.txt

@@ -0,0 +1,10 @@
+Augmentor
+click
+matplotlib
+numpy
+pandas
+pillow
+sklearn
+tensorflow
+keras
+jinja2

setup.py

@@ -1,30 +0,0 @@
-from setuptools import find_packages, setup
-
-setup(
-    name='eyeballer',
-    version='0.0.1',
-    packages=find_packages(exclude=["tests"]),
-    url='https://github.com/BishopFox/eyeballer',
-    license='GNU General Public License v3.0',
-    author='Daniel Petro',
-    author_email='',
-    description='',
-    entry_points={
-        'console_scripts': [
-            'eyeballer = eyeballer.eyeballer:cli'
-        ]
-    },
-    install_requires=[
-        'Augmentor',
-        'click',
-        'matplotlib',
-        'numpy',
-        'pandas',
-        'pillow',
-        'sklearn',
-        'tensorflow',
-        'keras @ git+https://github.com/keras-team/keras',
-    ],
-    dependency_links=[
-    ],
-)

tests/PredictTest.py

@@ -2,7 +2,8 @@ import errno
 import os
 import unittest
 
-from eyeballer.model import EyeballModel, DATA_LABELS
+from eyeballer.model import EyeballModel
+
 
 class PredictTest(unittest.TestCase):
     def setUp(self):
@@ -10,9 +11,9 @@ class PredictTest(unittest.TestCase):
         if not os.path.isfile(weights_file):
             print("Error: Symlink the latest weights file to 'latest_weights.h5'")
             raise FileNotFoundError(
-            errno.ENOENT,
-            os.strerror(errno.ENOENT),
-            weights_file)
+                errno.ENOENT,
+                os.strerror(errno.ENOENT),
+                weights_file)
 
         model_kwargs = {
             "weights_file": weights_file,
@@ -51,15 +52,15 @@ class PredictTest(unittest.TestCase):
 
         self.assertEquals(results_one, results_two)
 
-    def get_prediction_by_name(self,results):
+    def get_prediction_by_name(self, results):
         prediction = None
         max_val = 0.0
 
         print(results)
         for k, v in results[0].items():
             if not isinstance(v, str) and v > max_val:
-                    prediction = k
-                    max_val = v
+                prediction = k
+                max_val = v
         return prediction
 
     def test_predict_custom404(self):