Fixed progressbar.progressbar issue

- All prediction tests

.gitignore

@@ -113,4 +113,8 @@ venv.bak/
 *.model
 *.csv
 *.txt
+results.html
 !requirements.txt
+!requirements-gpu.txt
+!tests/models/*
+!tests/data/*

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

@@ -3,44 +3,35 @@ 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
 To eyeball some screenshots, just run the "predict" mode:
 
 ```
-eyeballer --weights YOUR_WEIGHTS.h5 predict YOUR_FILE.png
+eyeballer.py --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/
+eyeballer.py --weights YOUR_WEIGHTS.h5 predict PATH_TO/YOUR_FILES/
 ```
 
 ## Training
 To train a new model, run:
 ```
-eyeballer train
+eyeballer.py 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.
@@ -52,5 +43,5 @@ This will output a new model file (weights.h5 by default).
 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 --weights YOUR_WEIGHTS.h5 evaluate
 ```

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)
@@ -37,14 +38,15 @@ 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)
 
     if heatmap:
         # Generate a heatmap
-        HeatMap(screenshot, model).generate()
+        HeatMap(screenshot, model, threshold).generate()
 
     if not results:
         print("Error: Input file does not exist")
@@ -56,20 +58,60 @@ 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, threshold))
+        print("HTML written to results.html")
+
+
+def processResults(results, threshold):
+    '''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 > threshold:
+                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"'None of the above' Precision: {results['none_of_the_above_precision']:4.2%}")
-    print(f"'None of the above' Recall: {results['none_of_the_above_recall']:4.2%}")
-    print(f"All or nothing Accuracy: {results['all_or_nothing_accuracy']:4.2%}")
-    print(f"Overall Binary Accuracy: {results['total_binary_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

@@ -1,6 +1,7 @@
 import os
 import random
 import sys
+import progressbar
 
 # Prevent Tkinter Dependency
 import matplotlib
@@ -10,36 +11,15 @@ import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import Augmentor
+import tensorflow as tf
 
-from keras import regularizers
-from keras.applications import MobileNet
-from keras.applications.mobilenet import preprocess_input
-from keras.callbacks import ModelCheckpoint
-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 tensorflow.keras.applications.mobilenet import preprocess_input
 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.
 
@@ -47,7 +27,7 @@ class EyeballModel:
     """
     graphs_directory = "graphs/"
     checkpoint_file = "weights.h5"
-    image_dir = "images_224x224/"
+    image_dir = "images/"
     image_width, image_height = 224, 224
     input_shape = (image_width, image_height, 3)
 
@@ -59,23 +39,24 @@ class EyeballModel:
         weights_file -- A filename for where to load the model's weights.
         seed -- PRNG seed, useful for repeating a previous run and using the same data. Training/Validation split is determined randomly.
         """
-        # Build the model
-        base_model = MobileNet(weights='imagenet', include_top=False, input_shape=self.input_shape)
-        x = base_model.output
-        x = GlobalAveragePooling2D()(x)
-        x = Dense(256, activation="relu", name="HiddenLayer1")(x)
-        x = Dropout(0.5)(x)
-        x = Dense(128, activation="relu", name="HiddenLayer2", kernel_regularizer=regularizers.l2(0.01))(x)
-        x = Dropout(0.2)(x)
-        output_layer = Dense(len(DATA_LABELS), activation="sigmoid", name="OutputLayer")(x)
-        self.model = Model(inputs=base_model.input, outputs=output_layer)
+        # # Build the model
+        self.model = tf.keras.Sequential()
+        pretrained_layer = tf.keras.applications.mobilenet.MobileNet(weights='imagenet', include_top=False, input_shape=self.input_shape)
+        self.model.add(pretrained_layer)
+        self.model.add(tf.keras.layers.GlobalAveragePooling2D())
+        self.model.add(tf.keras.layers.Dense(256, activation="relu"))
+        self.model.add(tf.keras.layers.Dropout(0.5))
+        self.model.add(tf.keras.layers.Dense(128, activation="relu"))
+        self.model.add(tf.keras.layers.Dropout(0.2))
+        self.model.add(tf.keras.layers.Dense(len(DATA_LABELS), activation="sigmoid"))
 
-        # for layer in base_model.layers:
+        self.model.compile(optimizer=tf.keras.optimizers.Adam(0.0005),
+                           loss="binary_crossentropy",
+                           metrics=["accuracy"])
+
+        # for layer in pretrained_layer.layers:
         #     layer.trainable = False
 
-        adam = Adam(lr=0.0005)
-        self.model.compile(optimizer=adam, loss="binary_crossentropy", metrics=["accuracy"])
-
         if print_summary:
             print(self.model.summary())
 
@@ -91,7 +72,7 @@ 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)
 
@@ -99,7 +80,7 @@ class EyeballModel:
             try:
                 self.model.load_weights(weights_file)
             except OSError:
-                print(f"ERROR: Unable to open weights file '{weights_file}'")
+                print("ERROR: Unable to open weights file '{}'".foramt(weights_file))
                 sys.exit(-1)
             print("Loaded model from file.")
         else:
@@ -131,7 +112,7 @@ class EyeballModel:
         """
         print("Training with seed: " + str(self.seed))
 
-        data_generator = ImageDataGenerator(
+        data_generator = tf.keras.preprocessing.image.ImageDataGenerator(
             preprocessing_function=self.preprocess_training_function,
             validation_split=0.2,
             samplewise_center=True)
@@ -160,8 +141,12 @@ class EyeballModel:
             class_mode="other")
 
         # Model checkpoint - Saves model weights when validation accuracy improves
-        callbacks = [ModelCheckpoint(self.checkpoint_file, monitor='val_loss', verbose=1, save_best_only=True,
-                                     save_weights_only=True, mode='min')]
+        callbacks = [tf.keras.callbacks.ModelCheckpoint(self.checkpoint_file,
+                     monitor='val_loss',
+                     verbose=1,
+                     save_best_only=True,
+                     save_weights_only=True,
+                     mode='min')]
 
         history = self.model.fit_generator(
             training_generator,
@@ -214,11 +199,11 @@ class EyeballModel:
         result["oldlooking"] = prediction[0][3]
         return result
 
-    def predict(self, path):
-        """Predict the labels for a single file
+    def predict(self, path, threshold=0.5):
+        """Predict the labels for a single file or directory of files
 
         Keyword arguments:
-        path -- The path to the file that we'll be evaluating
+        path -- The path to the file(s) that we'll be evaluating.
         """
         # Is this a single file, or a directory?
         screenshots = []
@@ -231,12 +216,21 @@ class EyeballModel:
             raise FileNotFoundError
 
         results = []
-        for screenshot in screenshots:
+        pbar = progressbar.ProgressBar()
+        for screenshot in pbar(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)
+            img = None
+            try:
+                img = tf.keras.preprocessing.image.load_img(screenshot, target_size=(self.image_width, self.image_height))
+                img = tf.keras.preprocessing.image.img_to_array(img)
+                img = np.expand_dims(img, axis=0)
+                img = preprocess_input(img)
+            except IsADirectoryError:
+                print("\nWARN: Skipping directory: ", screenshot)
+                continue
+            except OSError:
+                print("\nWARN: Skipping empty or corrupt file: ", screenshot)
+                continue
 
             prediction = self.model.predict(img, batch_size=1)
             result = dict()
@@ -246,7 +240,6 @@ class EyeballModel:
             result["homepage"] = prediction[0][2]
             result["oldlooking"] = prediction[0][3]
             results.append(result)
-
         return results
 
     def evaluate(self, threshold=0.5):
@@ -256,7 +249,7 @@ class EyeballModel:
         threshold -- Value between 0->1. The cutoff where the numerical prediction becomes boolean. Default: 0.5
         """
         # Prepare the data
-        data_generator = ImageDataGenerator(
+        data_generator = tf.keras.preprocessing.image.ImageDataGenerator(
             preprocessing_function=preprocess_input)
         evaluation_generator = data_generator.flow_from_dataframe(
             self.evaluation_labels,
@@ -271,7 +264,7 @@ class EyeballModel:
         if not self.random_seed:
             print("Using validation set...")
             # Data augmentation
-            data_generator = ImageDataGenerator(
+            data_generator = tf.keras.preprocessing.image.ImageDataGenerator(
                 preprocessing_function=self.preprocess_training_function,
                 samplewise_center=True,
                 validation_split=0.2)
@@ -289,30 +282,21 @@ 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
@@ -329,6 +313,9 @@ class EyeballModel:
                 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):
@@ -343,20 +330,23 @@ class EyeballModel:
                 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, labels, predictions, top_k=10, best=False):
+    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)
@@ -369,7 +359,7 @@ 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

@@ -7,7 +7,7 @@ from eyeballer.model import DATA_LABELS
 
 
 class HeatMap():
-    def __init__(self, screenshot_path, model, boxsize=28, step=7):
+    def __init__(self, screenshot_path, model, threshold=0.5, boxsize=28, step=7):
         self.model = model
 
         img = image.load_img(screenshot_path, target_size=(self.model.image_width, self.model.image_height))
@@ -20,6 +20,7 @@ class HeatMap():
         self.y = 0
         self.screenshot_path = screenshot_path
         self.gamma = 3
+        self.threshold = threshold
 
     def generate(self, output_file="heatmap.png"):
         """ Make a single heatmap image and return it """
@@ -30,13 +31,13 @@ class HeatMap():
         for label in DATA_LABELS:
             boxsize = self.boxsize
             # Ignore this label if it didn't predict positively (true label)
-            if results[label] > 0.5:
+            if results[label] > self.threshold:
                 worst_score = 1
-                while worst_score > 0.5:
+                while worst_score > self.threshold:
                     heatmap, worst_score = self._get_heatmap(label, boxsize)
-                    if worst_score > 0.5:
+                    if worst_score > self.threshold:
                         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 +60,7 @@ 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 +1,10 @@
+Augmentor
+click
+matplotlib
+numpy
+pandas
+pillow
+sklearn
+tensorflow-gpu==2.0.0-beta0
+jinja2
+progressbar2

requirements.txt

@@ -0,0 +1,10 @@
+Augmentor
+click
+matplotlib
+numpy
+pandas
+pillow
+sklearn
+tensorflow==2.0.0-beta0
+jinja2
+progressbar2

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='Bishop Fox',
-    author_email='',
-    description='',
-    entry_points={
-        'console_scripts': [
-            'eyeballer = eyeballer.eyeballer:cli'
-        ]
-    },
-    install_requires=[
-        'Augmentor',
-        'click',
-        'matplotlib',
-        'numpy',
-        'pandas',
-        'pillow',
-        'sklearn',
-        'tensorflow-gpu',
-        'keras @ git+https://github.com/keras-team/keras',
-    ],
-    dependency_links=[
-    ],
-)

tests/PredictTest.py

@@ -1,15 +1,22 @@
 import errno
 import os
 import unittest
+import sys
 
 from eyeballer.model import EyeballModel
 
 
 class PredictTest(unittest.TestCase):
     def setUp(self):
-        weights_file = "latest_weights.h5"
+
+        class DummyFile(object):
+            def write(self, x): pass
+            def flush(self): pass
+
+        sys.stdout = DummyFile()
+        weights_file = "tests/models/test_weights.h5"
         if not os.path.isfile(weights_file):
-            print("Error: Symlink the latest weights file to 'latest_weights.h5'")
+            print("Error: Symlink the latest weights file to " + weights_file)
             raise FileNotFoundError(
                 errno.ENOENT,
                 os.strerror(errno.ENOENT),
@@ -30,7 +37,7 @@ class PredictTest(unittest.TestCase):
         }
         same_seed_model = EyeballModel(**model_kwargs)
 
-        screenshot = "tests/data/test.png"
+        screenshot = "tests/data/404.png"
 
         results_one = self.model.predict(screenshot)
         results_two = same_seed_model.predict(screenshot)
@@ -45,40 +52,77 @@ class PredictTest(unittest.TestCase):
         }
         same_seed_model = EyeballModel(**model_kwargs)
 
-        screenshot = "tests/data/test.png"
+        screenshot = "tests/data/404.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
+        self.assertEqual(results_one, results_two)
 
     def test_predict_custom404(self):
-        screenshot = "tests/data/custom404.png"
-        results = self.model.predict(screenshot)
-        self.assertEqual(self.get_prediction_by_name(results), "custom404")
+        screenshot = "tests/data/404.png"
+        results = self.model.predict(screenshot)[0]
+        self.assertGreater(results["custom404"], 0.5)
+
+    def test_predict_not_custom404(self):
+        screenshot = "tests/data/nothing.png"
+        results = self.model.predict(screenshot)[0]
+        self.assertLess(results["custom404"], 0.5)
 
     def test_predict_login(self):
         screenshot = "tests/data/login.png"
-        results = self.model.predict(screenshot)
-        self.assertEqual(self.get_prediction_by_name(results), "login")
+        results = self.model.predict(screenshot)[0]
+        self.assertGreater(results["login"], 0.5)
+
+    def test_predict_not_login(self):
+        screenshot = "tests/data/nothing.png"
+        results = self.model.predict(screenshot)[0]
+        print(screenshot, results)
+        self.assertLess(results["login"], 0.5)
 
     def test_predict_homepage(self):
         screenshot = "tests/data/homepage.png"
-        results = self.model.predict(screenshot)
-        self.assertEqual(self.get_prediction_by_name(results), "homepage")
+        results = self.model.predict(screenshot)[0]
+        self.assertGreater(results["homepage"], 0.5)
+
+    def test_predict_not_homepage(self):
+        screenshot = "tests/data/nothing.png"
+        results = self.model.predict(screenshot)[0]
+        self.assertLess(results["homepage"], 0.5)
 
     def test_predict_oldlooking(self):
-        screenshot = "tests/data/oldlooking.png"
-        results = self.model.predict(screenshot)
-        self.assertEqual(self.get_prediction_by_name(results), "oldlooking")
+        screenshot = "tests/data/old-looking.png"
+        results = self.model.predict(screenshot)[0]
+        self.assertGreater(results["oldlooking"], 0.5)
+
+    def test_predict_not_oldlooking(self):
+        screenshot = "tests/data/nothing.png"
+        results = self.model.predict(screenshot)[0]
+        self.assertLess(results["oldlooking"], 0.5)
+
+    def test_file_doesnt_exist(self):
+        screenshot = "tests/data/doesnotexist.png"
+        try:
+            self.model.predict(screenshot)[0]
+            self.fail("FileNotFoundError was expected but not found")
+        except FileNotFoundError:
+            pass
+
+    def test_folder(self):
+        screenshots = "tests/data/"
+        results = self.model.predict(screenshots)
+        self.assertEqual(len(results), 7)
+
+    def test_file_is_empty(self):
+        """
+        We're just testing that it doesn't crash, basically
+        """
+        screenshot = "tests/data/empty.png"
+        self.model.predict(screenshot)
+
+    def test_file_is_invalid(self):
+        """
+        We're just testing that it doesn't crash, basically
+        """
+        screenshot = "tests/data/invalid.png"
+        self.model.predict(screenshot)

tests/data/invalid.png

@@ -0,0 +1 @@
+THIS TEXT IS NOT A VALID PNG