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

README.md

@@ -0,0 +1,47 @@
+# Eyeballer
+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.
+
+## Setup
+
+Download required packages on pip:
+```
+sudo pip3 install -r requirements.txt
+```
+
+Or if you want GPU support:
+```
+sudo pip3 install -r requirements-gpu.txt
+```
+
+## Predicting Labels
+To eyeball some screenshots, just run the "predict" mode:
+
+```
+eyeballer --weights YOUR_WEIGHTS.h5 predict YOUR_FILE.png
+```
+
+Or for a whole directory of files:
+
+```
+eyeballer --weights YOUR_WEIGHTS.h5 predict PATH_TO/YOUR_FILES/
+```
+
+## Training
+To train a new model, run:
+```
+eyeballer train
+```
+
+You'll want a machine with a good GPU for this to run in a reasonable amount of time. Setting that up is outside the scope of this readme, however.
+
+This will output a new model file (weights.h5 by default).
+
+## Evaluation
+
+You just trained a new model, cool! Let's see how well it performs against some images it's never seen before, across a variety of metrics:
+
+```
+eyeballer --weights YOUR_WEIGHTS.h5 evaluate
+```

eyeballer.py

@@ -1,8 +1,11 @@
 #!/usr/bin/env python3
+
 import click
 import csv
 
-from model import EyeballModel, DATA_LABELS
+from eyeballer.model import EyeballModel, DATA_LABELS
+from eyeballer.visualization import HeatMap
+from jinja2 import Template
 
 
 @click.group(invoke_without_command=True)
@@ -34,10 +37,17 @@ 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):
+def predict(ctx, screenshot, heatmap, threshold):
     model = EyeballModel(**ctx.obj['model_kwargs'])
     results = model.predict(screenshot)
+
+    if heatmap:
+        # Generate a heatmap
+        HeatMap(screenshot, model).generate()
+
     if not results:
         print("Error: Input file does not exist")
     if len(results) == 1:
@@ -48,18 +58,61 @@ def predict(ctx, screenshot):
             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.eyeballer import EyeballModel  # noqa: F401

eyeballer/model.py

@@ -1,9 +1,10 @@
 import os
 import random
+import sys
 
 # Prevent Tkinter Dependency
 import matplotlib
-matplotlib.use('agg')
+matplotlib.use('agg')  # noqa: E402
 import matplotlib.pyplot as plt
 
 import numpy as np
@@ -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 augmentation import EyeballerAugmentation
-from keras.callbacks import Callback
+from eyeballer.augmentation import EyeballerAugmentation
 
 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()
@@ -189,11 +182,30 @@ class EyeballModel:
             plt.legend(['Train', 'Validation'], loc='upper left')
             plt.savefig(self.graphs_directory + "loss.png")
 
-    def predict(self, path):
+    def predict_on_array(self, image):
+        """Predict the labels for a single screenshot
+
+        Keyword arguments:
+        image -- The numpy array of the image to classify
+        """
+        img = np.expand_dims(image, axis=0)
+        img = preprocess_input(img)
+
+        prediction = self.model.predict(img, batch_size=1)
+        result = dict()
+        result["filename"] = "custom-image"
+        result["custom404"] = prediction[0][0]
+        result["login"] = prediction[0][1]
+        result["homepage"] = prediction[0][2]
+        result["oldlooking"] = prediction[0][3]
+        return result
+
+    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 = []
@@ -203,25 +215,32 @@ class EyeballModel:
             screenshots = os.listdir(path)
             screenshots = [os.path.join(path, s) for s in screenshots]
         else:
-            return None
+            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):
@@ -264,44 +283,71 @@ class EyeballModel:
         else:
             print("Using evaluation set...")
 
-        prediction_manager = PredictionManager()
-
-        try:
-            output = 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)
@@ -314,7 +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

@@ -0,0 +1,127 @@
+import numpy as np
+import math
+from keras.preprocessing import image
+from copy import deepcopy
+from matplotlib import pyplot as plt
+from eyeballer.model import DATA_LABELS
+
+
+class HeatMap():
+    def __init__(self, screenshot_path, model, boxsize=28, step=7):
+        self.model = model
+
+        img = image.load_img(screenshot_path, target_size=(self.model.image_width, self.model.image_height))
+        img = image.img_to_array(img)
+
+        self.screenshot = img
+        self.boxsize = boxsize
+        self.step = step
+        self.x = 0
+        self.y = 0
+        self.screenshot_path = screenshot_path
+        self.gamma = 3
+
+    def generate(self, output_file="heatmap.png"):
+        """ Make a single heatmap image and return it """
+        heatmaps = []
+        labels = []
+        results = self.model.predict(self.screenshot_path)
+        results = results[0]
+        for label in DATA_LABELS:
+            boxsize = self.boxsize
+            # Ignore this label if it didn't predict positively (true label)
+            if results[label] > 0.5:
+                worst_score = 1
+                while worst_score > 0.5:
+                    heatmap, worst_score = self._get_heatmap(label, boxsize)
+                    if worst_score > 0.5:
+                        boxsize += 28
+                        print("Didn't get a good image for {}. Trying again with a bigger boxsize: {}".format(label, boxsize))
+                    else:
+                        heatmaps.append(heatmap)
+                        labels.append(label)
+
+        plt.figure()
+        screenshot_image = image.load_img(self.screenshot_path, target_size=(self.model.image_width, self.model.image_width))
+        _, subplots = plt.subplots(1, len(heatmaps))
+        # This is a little janky, but matplotlib returns a list above if there's multiple subplots, and just a single subplot if there's only one
+        #   it would have been easier if it was always a list. But alas
+        if not heatmaps:
+            print("No heatmap made. The image did not have a True classification for any label")
+            return
+        if len(heatmaps) > 1:
+            for i, heatmap in enumerate(heatmaps):
+                subplots[i].imshow(screenshot_image, cmap='binary', interpolation='none')
+                subplots[i].imshow(heatmap, alpha=0.5, interpolation='none')
+                subplots[i].set_title(labels[i])
+        else:
+            subplots.imshow(screenshot_image, cmap='binary', interpolation='none')
+            subplots.imshow(heatmap, alpha=0.5, interpolation='none')
+            subplots.set_title(labels[0])
+        plt.savefig(output_file)
+        print("Heatmap image written to: {}".format(output_file))
+
+    def _get_heatmap(self, label, boxsize):
+        worst_score = 1
+        self.x, self.y = 0, 0
+
+        heatmap = np.ones((self.model.image_width, self.model.image_height))
+        heatmap *= 255
+        while True:
+            # Occlude an image
+            occluded_image, x, y = self._occlude(boxsize)
+            if occluded_image is not None:
+                # Score the new occluded image
+                results = self.model.predict_on_array(occluded_image)
+                score = results[label]  # TODO
+                worst_score = min(worst_score, score)
+                # Scale the score up to pixel values
+                score = math.floor(score * 255)
+                # Anneal the scores as you go outward into the occlusion block
+                new_scores = np.ones((boxsize, boxsize))
+                new_scores *= score
+                new_scores = self._gamma_anneal(new_scores)
+
+                # For each pixel, update only where the new score is lower
+                original_scores = heatmap[x: x+boxsize, y: y+boxsize]
+
+                target_area = np.where(new_scores < original_scores, new_scores, original_scores)
+                heatmap[x: x+boxsize, y: y+boxsize] = target_area
+            else:
+                break
+
+        return heatmap, worst_score
+
+    def _gamma_anneal(self, occlusion_area):
+        """ Get the annealed score based on centeredness of the occlusion area """
+        width, height = occlusion_area.shape
+
+        center_x = (width-1) / 2
+        center_y = (height-1) / 2
+
+        annealed_area = np.zeros(occlusion_area.shape)
+
+        for (x, y), score in np.ndenumerate(occlusion_area):
+            distance = math.sqrt((center_x - x)**2 + (center_y - y)**2)
+            annealed_area[x, y] = min(255, score + (self.gamma * distance))
+
+        return annealed_area
+
+    def _occlude(self, boxsize):
+        ''' Return a single occluded image and its location '''
+        if self.x + boxsize > self.screenshot.shape[0]:
+            return None, None, None
+
+        retImg = np.copy(self.screenshot)
+        retImg[self.x:self.x+boxsize, self.y:self.y+boxsize] = 0.0
+
+        old_i = deepcopy(self.x)
+        old_j = deepcopy(self.y)
+
+        # update indices
+        self.y = self.y + self.step
+        if self.y+boxsize > self.screenshot.shape[1]:  # reached end
+            self.y = 0  # reset j
+            self.x = self.x + self.step  # go to next row
+
+        return retImg, old_i, old_j

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

@@ -6,4 +6,5 @@ pandas
 pillow
 sklearn
 tensorflow
-git+https://github.com/keras-team/keras/
+keras
+jinja2

tests/EvaluateTest.py

@@ -0,0 +1 @@
+#TODO

tests/PredictTest.py

@@ -0,0 +1,84 @@
+import errno
+import os
+import unittest
+
+from eyeballer.model import EyeballModel
+
+
+class PredictTest(unittest.TestCase):
+    def setUp(self):
+        weights_file = "latest_weights.h5"
+        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)
+
+        model_kwargs = {
+            "weights_file": weights_file,
+            "print_summary": False,
+            "seed": None
+        }
+        self.model = EyeballModel(**model_kwargs)
+
+    def test_different_seed_predict(self):
+        model_kwargs = {
+            "weights_file": None,
+            "print_summary": False,
+            "seed": 12345678
+        }
+        same_seed_model = EyeballModel(**model_kwargs)
+
+        screenshot = "tests/data/test.png"
+
+        results_one = self.model.predict(screenshot)
+        results_two = same_seed_model.predict(screenshot)
+
+        self.assertNotEqual(results_one, results_two)
+
+    def test_same_seed_predict(self):
+        model_kwargs = {
+            "weights_file": None,
+            "print_summary": False,
+            "seed": 12345678
+        }
+        same_seed_model = EyeballModel(**model_kwargs)
+
+        screenshot = "tests/data/test.png"
+
+        results_one = same_seed_model.predict(screenshot)
+        results_two = same_seed_model.predict(screenshot)
+
+        self.assertEquals(results_one, results_two)
+
+    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
+        return prediction
+
+    def test_predict_custom404(self):
+        screenshot = "tests/data/custom404.png"
+        results = self.model.predict(screenshot)
+        self.assertEqual(self.get_prediction_by_name(results), "custom404")
+
+    def test_predict_login(self):
+        screenshot = "tests/data/login.png"
+        results = self.model.predict(screenshot)
+        self.assertEqual(self.get_prediction_by_name(results), "login")
+
+    def test_predict_homepage(self):
+        screenshot = "tests/data/homepage.png"
+        results = self.model.predict(screenshot)
+        self.assertEqual(self.get_prediction_by_name(results), "homepage")
+
+    def test_predict_oldlooking(self):
+        screenshot = "tests/data/oldlooking.png"
+        results = self.model.predict(screenshot)
+        self.assertEqual(self.get_prediction_by_name(results), "oldlooking")

tests/TrainTest.py

@@ -0,0 +1 @@
+#TODO

tests/__init__.py

@@ -0,0 +1 @@
+from tests.PredictTest import *