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+%global _empty_manifest_terminate_build 0
+Name:		python-dgaintel
+Version:	2.3
+Release:	1
+Summary:	Extremely fast and accurate predictions of whether a domain name is genuine or DGA with deep learning.
+License:	MIT
+URL:		https://github.com/sudo-rushil/dgaintel
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/9c/c0/a0ae8653f30a9562d373f461388455c660ed4d7a22ff34d69bd8ac542110/dgaintel-2.3.tar.gz
+BuildArch:	noarch
+
+
+%description
+# DGA Intel
+
+Using deep learning to detect DGA domains.
+
+# Overview
+The DGAIntel Python module allows you to utilize a powerful CNN-LSTM model to predict whether a given domain name was generated by a domain generation algorithm (DGA) or corresponds to a genuine domain. The prediction features are also accesible through [this website](http://www.dgaintel.com/), but this package allows for direct integration into your workflow.
+
+## Requirements
+
+DGAIntel is designed for use with Python 3. It has only two requirements:
+
+    - TensorFlow 2.x
+    - Numpy
+
+# Installation
+
+To download dgaintel, simply use Pypi via pip.
+```sh
+$ pip install dgaintel
+```
+
+Alternatively, you could install from source.
+```sh
+$ git clone https://github.com/sudo-rushil/dgaintel
+$ cd dgaintel
+$ python setup.py install
+```
+
+Verify your installation by running
+```Python
+>>> import dgaintel
+>>> dgaintel.get_prediction('microsoft.com')
+'microsoft.com is genuine with probability 0.00050'
+```
+
+# Examples
+
+### Predict DGA
+This is simple way of determining whether any given domain, such as `'microsoft.com'` is DGA or not, mainly intended for cyber security analysts.
+
+```Python
+from dgaintel import get_prediction
+
+get_prediction('microsoft.com')
+```
+> 'microsoft.com is genuine with probability 0.00050'
+
+### Predict DGA probability
+This allows for getting the probability, or probabilities, that a domain or list of domains is DGA or not, which is more useful to data scientists.
+
+```Python
+from dgaintel import get_prob
+
+# For single domain
+prob = get_prob('microsoft.com')
+print(prob)
+
+# For multiple domains
+probs = get_prob(['microsoft.com', 'wikipedia.com', 'vlurgpeddygdy.com'])
+print(probs)
+
+# To get just the scores
+raw_probs = list(get_prob(['microsoft.com', 'wikipedia.com', 'vlurgpeddygdy.com'], raw=True))
+print(raw_probs)
+```
+
+> 0.00050
+
+> [('microsoft.com', 0.00050), ('wikipedia.com', 0.00033), ('vlurgpeddygdy.com', 0.97601)]
+
+> [0.00050845, 0.00033092, 0.00144754]
+
+### Predict by file
+This is for inputing a file containing a list of domains to get predictions on all of them at once, which is helpful for data analysts.
+
+Say you have a domain file `domains.txt`.
+```
+microsoft.com
+wikipedia.com
+vlurgpeddygdy.com
+```
+
+Then, you can run the following code in the same directory.
+```Python
+from dgaintel import get_prediction
+
+# Print to console
+get_prediction('domains.txt')
+
+# Write to file
+get_prediction('domains.txt', to_file='domain_predictions.txt')
+```
+
+> microsoft.com is genuine with probability 0.00050
+
+> wikipedia.com is genuine with probability 0.00033
+
+> vlurgpeddygdy.com is DGA with probability 0.97601
+
+If you read the new file `domain_predictions.txt`, you will see the following.
+
+```
+microsoft.com is genuine with probability 0.0005084535223431885
+wikipedia.com is genuine with probability 0.00033092446392402053
+vlurgpeddygdy.com is DGA with probability 0.9760094285011292
+```
+
+### Prediction analysis
+This is an example function that integrates dgaintel with [whois](https://pypi.org/project/whois/) for performing basic prediction analysis, which is important for cyber security investigators.
+
+```Python
+from dgaintel import get_prob
+from whois import query
+
+def analyze(domain, out=True):
+    prob = get_prob(domain)
+    whois = query(domain)
+    dga = False
+    if prob >= 0.5: dga = True
+
+    domain_analysis = {'domain_name': domain,
+                       'dga': dga,
+                       'registrar': whois.registrar if whois else None,
+                       'creation date' : whois.creation_date if whois else None,
+                       'expiration date': whois.expiration_date if whois else None}
+
+    if out:
+        print()
+        for key, val in domain_analysis.items():
+            print('{}: {}'.format(key, val))
+        print()
+        return None
+
+    return domain_analysis
+
+analyze('microsoft.com')
+
+# Get analysis dictionary in python itself
+analysis = analyze('microsoft.com', out=False)
+```
+
+> name: microsoft.com
+
+> dga: False
+
+> registrar: MarkMonitor Inc.
+
+> creation date: 1991-05-02 04:00:00
+
+> expiration date: 2021-05-03 04:00:00
+
+
+### Predictions with Whitelisting
+This example shows how the class interface to DGAIntel allows for certain TLDs to be whitelisted, preventing them from raising errors in a given ecosystem.
+
+```Python
+from dgaintel import Intel
+
+intel = Intel(['cloud.com'])
+
+print(intel.get_prob(['www.cloud.com',
+                        'dfsadkcda.cloud.com',
+                        'www.cloud.org',
+                        'www.dkfjsdakfj.org']))
+```
+
+> [('www.cloud.com', 0.0), ('dfsadkcda.cloud.com', 0.0), ('www.cloud.org', 0.00045579672), ('www.dkfjsdakfj.org', 0.99884665)]
+
+
+# Documentation
+DGAIntel has support for polymorphism; to input domains to run predictions on, you can use a single domain name, a list of domain names, or a text file with line-separated domain names. The text file has the format
+
+```
+microsoft.com
+wikipedia.com
+vlurgpeddygdy.com
+...
+```
+
+Additionally, the Tensorflow Keras model running in the backend supports input batching, meaning there is a significant increase in speed for running predictions on lists or files rather than individual domains. This was tested in Jupyter.
+
+```Python
+from dgaintel import get_prob
+
+# List of 10 domain names
+l = ['microsoft.com', 'squarespace.com', 'hsfkjdshfjasdhfk.com', 'fdkhakshfda.com', 'foilfencersarebad.com', 'foilfencersarebad.com', 'foilfencersarebad.com', 'discojjfdsf.com', 'fasddafhkj.com', 'wikipedai.com']
+```
+
+```Python
+# One domain
+%%timeit
+get_prob(l[0])
+```
+
+> 286 ms ± 4.99 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+```Python
+# Ten domains
+%%timeit
+get_prob(l)
+```
+
+> 290 ms ± 7.23 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+```Python
+# Hundred domains
+%%timeit
+get_prob(l*10)
+```
+
+> 333 ms ± 4.71 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+```Python
+# Thousand domains
+%%timeit
+get_prob(l*100)
+```
+
+> 584 ms ± 14.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+This demonstrates that increasing the number of domain names one runs the prediction by 1000x only increases the inference time by less than 2x. Therefore, this model is easily adaptable to large-scale predictions.
+
+## API
+The `get_prediction` function will either print the predictions or write them to a user-specified file.
+```Python
+from dgaintel import get_prediction
+
+get_prediction('microsoft.com')
+get_prediction(['microsoft.com', 'wikipedia.com', 'vlurgpeddygdy.com'])
+get_prediction('domains.txt')
+get_prediction('domains.txt', to_file='domain_predictions.txt')
+```
+
+The `get_prob` function will perform the inference and provide the prediction floats. It is helpful if you want to use the prediction scores directly in your workflow.
+```Python
+from dgaintel import get_prob
+
+get_prob('microsoft.com') # 0.00050851
+get_prob(['microsoft.com', 'wikipedia.com', 'vlurgpeddygdy.com']) # [('microsoft.com', 0.00050), ('wikipedia.com', 0.00033), ('vlurgpeddygdy.com', 0.0.97601)]
+get_prob('domains.txt') # [('microsoft.com', 0.00050), ('wikipedia.com', 0.00033), ('vlurgpeddygdy.com', 0.97601)]
+get_prob(['microsoft.com', 'wikipedia.com', 'google.com'], raw=True) # array([0.00050, 0.00033, 0.0.97601], dtype=float32)
+```
+
+The `Intel` interface allows DGAIntel to avoid checking certain domains with known TLDs to ensure enterprise functions are not compromised.
+```Python
+from dgaintel import Intel
+
+intel = Intel(['microsoft.com'])
+intel.get_prob('microsoft.com') # 0.0
+intel.get_prob(['microsoft.com', 'wikipedia.com', 'vlurgpeddygdy.com']) # [('microsoft.com', 0.0), ('wikipedia.com', 0.00033), ('vlurgpeddygdy.com', 0.0.97601)]
+intel.get_prob('domains.txt') # [('microsoft.com', 0.0), ('wikipedia.com', 0.00033), ('vlurgpeddygdy.com', 0.97601)]
+intel.get_prob(['microsoft.com', 'wikipedia.com', 'google.com'], raw=True) # array([0.0, 0.00033, 0.0.97601], dtype=float32)
+```
+
+%package -n python3-dgaintel
+Summary:	Extremely fast and accurate predictions of whether a domain name is genuine or DGA with deep learning.
+Provides:	python-dgaintel
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-dgaintel
+# DGA Intel
+
+Using deep learning to detect DGA domains.
+
+# Overview
+The DGAIntel Python module allows you to utilize a powerful CNN-LSTM model to predict whether a given domain name was generated by a domain generation algorithm (DGA) or corresponds to a genuine domain. The prediction features are also accesible through [this website](http://www.dgaintel.com/), but this package allows for direct integration into your workflow.
+
+## Requirements
+
+DGAIntel is designed for use with Python 3. It has only two requirements:
+
+    - TensorFlow 2.x
+    - Numpy
+
+# Installation
+
+To download dgaintel, simply use Pypi via pip.
+```sh
+$ pip install dgaintel
+```
+
+Alternatively, you could install from source.
+```sh
+$ git clone https://github.com/sudo-rushil/dgaintel
+$ cd dgaintel
+$ python setup.py install
+```
+
+Verify your installation by running
+```Python
+>>> import dgaintel
+>>> dgaintel.get_prediction('microsoft.com')
+'microsoft.com is genuine with probability 0.00050'
+```
+
+# Examples
+
+### Predict DGA
+This is simple way of determining whether any given domain, such as `'microsoft.com'` is DGA or not, mainly intended for cyber security analysts.
+
+```Python
+from dgaintel import get_prediction
+
+get_prediction('microsoft.com')
+```
+> 'microsoft.com is genuine with probability 0.00050'
+
+### Predict DGA probability
+This allows for getting the probability, or probabilities, that a domain or list of domains is DGA or not, which is more useful to data scientists.
+
+```Python
+from dgaintel import get_prob
+
+# For single domain
+prob = get_prob('microsoft.com')
+print(prob)
+
+# For multiple domains
+probs = get_prob(['microsoft.com', 'wikipedia.com', 'vlurgpeddygdy.com'])
+print(probs)
+
+# To get just the scores
+raw_probs = list(get_prob(['microsoft.com', 'wikipedia.com', 'vlurgpeddygdy.com'], raw=True))
+print(raw_probs)
+```
+
+> 0.00050
+
+> [('microsoft.com', 0.00050), ('wikipedia.com', 0.00033), ('vlurgpeddygdy.com', 0.97601)]
+
+> [0.00050845, 0.00033092, 0.00144754]
+
+### Predict by file
+This is for inputing a file containing a list of domains to get predictions on all of them at once, which is helpful for data analysts.
+
+Say you have a domain file `domains.txt`.
+```
+microsoft.com
+wikipedia.com
+vlurgpeddygdy.com
+```
+
+Then, you can run the following code in the same directory.
+```Python
+from dgaintel import get_prediction
+
+# Print to console
+get_prediction('domains.txt')
+
+# Write to file
+get_prediction('domains.txt', to_file='domain_predictions.txt')
+```
+
+> microsoft.com is genuine with probability 0.00050
+
+> wikipedia.com is genuine with probability 0.00033
+
+> vlurgpeddygdy.com is DGA with probability 0.97601
+
+If you read the new file `domain_predictions.txt`, you will see the following.
+
+```
+microsoft.com is genuine with probability 0.0005084535223431885
+wikipedia.com is genuine with probability 0.00033092446392402053
+vlurgpeddygdy.com is DGA with probability 0.9760094285011292
+```
+
+### Prediction analysis
+This is an example function that integrates dgaintel with [whois](https://pypi.org/project/whois/) for performing basic prediction analysis, which is important for cyber security investigators.
+
+```Python
+from dgaintel import get_prob
+from whois import query
+
+def analyze(domain, out=True):
+    prob = get_prob(domain)
+    whois = query(domain)
+    dga = False
+    if prob >= 0.5: dga = True
+
+    domain_analysis = {'domain_name': domain,
+                       'dga': dga,
+                       'registrar': whois.registrar if whois else None,
+                       'creation date' : whois.creation_date if whois else None,
+                       'expiration date': whois.expiration_date if whois else None}
+
+    if out:
+        print()
+        for key, val in domain_analysis.items():
+            print('{}: {}'.format(key, val))
+        print()
+        return None
+
+    return domain_analysis
+
+analyze('microsoft.com')
+
+# Get analysis dictionary in python itself
+analysis = analyze('microsoft.com', out=False)
+```
+
+> name: microsoft.com
+
+> dga: False
+
+> registrar: MarkMonitor Inc.
+
+> creation date: 1991-05-02 04:00:00
+
+> expiration date: 2021-05-03 04:00:00
+
+
+### Predictions with Whitelisting
+This example shows how the class interface to DGAIntel allows for certain TLDs to be whitelisted, preventing them from raising errors in a given ecosystem.
+
+```Python
+from dgaintel import Intel
+
+intel = Intel(['cloud.com'])
+
+print(intel.get_prob(['www.cloud.com',
+                        'dfsadkcda.cloud.com',
+                        'www.cloud.org',
+                        'www.dkfjsdakfj.org']))
+```
+
+> [('www.cloud.com', 0.0), ('dfsadkcda.cloud.com', 0.0), ('www.cloud.org', 0.00045579672), ('www.dkfjsdakfj.org', 0.99884665)]
+
+
+# Documentation
+DGAIntel has support for polymorphism; to input domains to run predictions on, you can use a single domain name, a list of domain names, or a text file with line-separated domain names. The text file has the format
+
+```
+microsoft.com
+wikipedia.com
+vlurgpeddygdy.com
+...
+```
+
+Additionally, the Tensorflow Keras model running in the backend supports input batching, meaning there is a significant increase in speed for running predictions on lists or files rather than individual domains. This was tested in Jupyter.
+
+```Python
+from dgaintel import get_prob
+
+# List of 10 domain names
+l = ['microsoft.com', 'squarespace.com', 'hsfkjdshfjasdhfk.com', 'fdkhakshfda.com', 'foilfencersarebad.com', 'foilfencersarebad.com', 'foilfencersarebad.com', 'discojjfdsf.com', 'fasddafhkj.com', 'wikipedai.com']
+```
+
+```Python
+# One domain
+%%timeit
+get_prob(l[0])
+```
+
+> 286 ms ± 4.99 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+```Python
+# Ten domains
+%%timeit
+get_prob(l)
+```
+
+> 290 ms ± 7.23 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+```Python
+# Hundred domains
+%%timeit
+get_prob(l*10)
+```
+
+> 333 ms ± 4.71 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+```Python
+# Thousand domains
+%%timeit
+get_prob(l*100)
+```
+
+> 584 ms ± 14.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+This demonstrates that increasing the number of domain names one runs the prediction by 1000x only increases the inference time by less than 2x. Therefore, this model is easily adaptable to large-scale predictions.
+
+## API
+The `get_prediction` function will either print the predictions or write them to a user-specified file.
+```Python
+from dgaintel import get_prediction
+
+get_prediction('microsoft.com')
+get_prediction(['microsoft.com', 'wikipedia.com', 'vlurgpeddygdy.com'])
+get_prediction('domains.txt')
+get_prediction('domains.txt', to_file='domain_predictions.txt')
+```
+
+The `get_prob` function will perform the inference and provide the prediction floats. It is helpful if you want to use the prediction scores directly in your workflow.
+```Python
+from dgaintel import get_prob
+
+get_prob('microsoft.com') # 0.00050851
+get_prob(['microsoft.com', 'wikipedia.com', 'vlurgpeddygdy.com']) # [('microsoft.com', 0.00050), ('wikipedia.com', 0.00033), ('vlurgpeddygdy.com', 0.0.97601)]
+get_prob('domains.txt') # [('microsoft.com', 0.00050), ('wikipedia.com', 0.00033), ('vlurgpeddygdy.com', 0.97601)]
+get_prob(['microsoft.com', 'wikipedia.com', 'google.com'], raw=True) # array([0.00050, 0.00033, 0.0.97601], dtype=float32)
+```
+
+The `Intel` interface allows DGAIntel to avoid checking certain domains with known TLDs to ensure enterprise functions are not compromised.
+```Python
+from dgaintel import Intel
+
+intel = Intel(['microsoft.com'])
+intel.get_prob('microsoft.com') # 0.0
+intel.get_prob(['microsoft.com', 'wikipedia.com', 'vlurgpeddygdy.com']) # [('microsoft.com', 0.0), ('wikipedia.com', 0.00033), ('vlurgpeddygdy.com', 0.0.97601)]
+intel.get_prob('domains.txt') # [('microsoft.com', 0.0), ('wikipedia.com', 0.00033), ('vlurgpeddygdy.com', 0.97601)]
+intel.get_prob(['microsoft.com', 'wikipedia.com', 'google.com'], raw=True) # array([0.0, 0.00033, 0.0.97601], dtype=float32)
+```
+
+%package help
+Summary:	Development documents and examples for dgaintel
+Provides:	python3-dgaintel-doc
+%description help
+# DGA Intel
+
+Using deep learning to detect DGA domains.
+
+# Overview
+The DGAIntel Python module allows you to utilize a powerful CNN-LSTM model to predict whether a given domain name was generated by a domain generation algorithm (DGA) or corresponds to a genuine domain. The prediction features are also accesible through [this website](http://www.dgaintel.com/), but this package allows for direct integration into your workflow.
+
+## Requirements
+
+DGAIntel is designed for use with Python 3. It has only two requirements:
+
+    - TensorFlow 2.x
+    - Numpy
+
+# Installation
+
+To download dgaintel, simply use Pypi via pip.
+```sh
+$ pip install dgaintel
+```
+
+Alternatively, you could install from source.
+```sh
+$ git clone https://github.com/sudo-rushil/dgaintel
+$ cd dgaintel
+$ python setup.py install
+```
+
+Verify your installation by running
+```Python
+>>> import dgaintel
+>>> dgaintel.get_prediction('microsoft.com')
+'microsoft.com is genuine with probability 0.00050'
+```
+
+# Examples
+
+### Predict DGA
+This is simple way of determining whether any given domain, such as `'microsoft.com'` is DGA or not, mainly intended for cyber security analysts.
+
+```Python
+from dgaintel import get_prediction
+
+get_prediction('microsoft.com')
+```
+> 'microsoft.com is genuine with probability 0.00050'
+
+### Predict DGA probability
+This allows for getting the probability, or probabilities, that a domain or list of domains is DGA or not, which is more useful to data scientists.
+
+```Python
+from dgaintel import get_prob
+
+# For single domain
+prob = get_prob('microsoft.com')
+print(prob)
+
+# For multiple domains
+probs = get_prob(['microsoft.com', 'wikipedia.com', 'vlurgpeddygdy.com'])
+print(probs)
+
+# To get just the scores
+raw_probs = list(get_prob(['microsoft.com', 'wikipedia.com', 'vlurgpeddygdy.com'], raw=True))
+print(raw_probs)
+```
+
+> 0.00050
+
+> [('microsoft.com', 0.00050), ('wikipedia.com', 0.00033), ('vlurgpeddygdy.com', 0.97601)]
+
+> [0.00050845, 0.00033092, 0.00144754]
+
+### Predict by file
+This is for inputing a file containing a list of domains to get predictions on all of them at once, which is helpful for data analysts.
+
+Say you have a domain file `domains.txt`.
+```
+microsoft.com
+wikipedia.com
+vlurgpeddygdy.com
+```
+
+Then, you can run the following code in the same directory.
+```Python
+from dgaintel import get_prediction
+
+# Print to console
+get_prediction('domains.txt')
+
+# Write to file
+get_prediction('domains.txt', to_file='domain_predictions.txt')
+```
+
+> microsoft.com is genuine with probability 0.00050
+
+> wikipedia.com is genuine with probability 0.00033
+
+> vlurgpeddygdy.com is DGA with probability 0.97601
+
+If you read the new file `domain_predictions.txt`, you will see the following.
+
+```
+microsoft.com is genuine with probability 0.0005084535223431885
+wikipedia.com is genuine with probability 0.00033092446392402053
+vlurgpeddygdy.com is DGA with probability 0.9760094285011292
+```
+
+### Prediction analysis
+This is an example function that integrates dgaintel with [whois](https://pypi.org/project/whois/) for performing basic prediction analysis, which is important for cyber security investigators.
+
+```Python
+from dgaintel import get_prob
+from whois import query
+
+def analyze(domain, out=True):
+    prob = get_prob(domain)
+    whois = query(domain)
+    dga = False
+    if prob >= 0.5: dga = True
+
+    domain_analysis = {'domain_name': domain,
+                       'dga': dga,
+                       'registrar': whois.registrar if whois else None,
+                       'creation date' : whois.creation_date if whois else None,
+                       'expiration date': whois.expiration_date if whois else None}
+
+    if out:
+        print()
+        for key, val in domain_analysis.items():
+            print('{}: {}'.format(key, val))
+        print()
+        return None
+
+    return domain_analysis
+
+analyze('microsoft.com')
+
+# Get analysis dictionary in python itself
+analysis = analyze('microsoft.com', out=False)
+```
+
+> name: microsoft.com
+
+> dga: False
+
+> registrar: MarkMonitor Inc.
+
+> creation date: 1991-05-02 04:00:00
+
+> expiration date: 2021-05-03 04:00:00
+
+
+### Predictions with Whitelisting
+This example shows how the class interface to DGAIntel allows for certain TLDs to be whitelisted, preventing them from raising errors in a given ecosystem.
+
+```Python
+from dgaintel import Intel
+
+intel = Intel(['cloud.com'])
+
+print(intel.get_prob(['www.cloud.com',
+                        'dfsadkcda.cloud.com',
+                        'www.cloud.org',
+                        'www.dkfjsdakfj.org']))
+```
+
+> [('www.cloud.com', 0.0), ('dfsadkcda.cloud.com', 0.0), ('www.cloud.org', 0.00045579672), ('www.dkfjsdakfj.org', 0.99884665)]
+
+
+# Documentation
+DGAIntel has support for polymorphism; to input domains to run predictions on, you can use a single domain name, a list of domain names, or a text file with line-separated domain names. The text file has the format
+
+```
+microsoft.com
+wikipedia.com
+vlurgpeddygdy.com
+...
+```
+
+Additionally, the Tensorflow Keras model running in the backend supports input batching, meaning there is a significant increase in speed for running predictions on lists or files rather than individual domains. This was tested in Jupyter.
+
+```Python
+from dgaintel import get_prob
+
+# List of 10 domain names
+l = ['microsoft.com', 'squarespace.com', 'hsfkjdshfjasdhfk.com', 'fdkhakshfda.com', 'foilfencersarebad.com', 'foilfencersarebad.com', 'foilfencersarebad.com', 'discojjfdsf.com', 'fasddafhkj.com', 'wikipedai.com']
+```
+
+```Python
+# One domain
+%%timeit
+get_prob(l[0])
+```
+
+> 286 ms ± 4.99 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+```Python
+# Ten domains
+%%timeit
+get_prob(l)
+```
+
+> 290 ms ± 7.23 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+```Python
+# Hundred domains
+%%timeit
+get_prob(l*10)
+```
+
+> 333 ms ± 4.71 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+```Python
+# Thousand domains
+%%timeit
+get_prob(l*100)
+```
+
+> 584 ms ± 14.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+This demonstrates that increasing the number of domain names one runs the prediction by 1000x only increases the inference time by less than 2x. Therefore, this model is easily adaptable to large-scale predictions.
+
+## API
+The `get_prediction` function will either print the predictions or write them to a user-specified file.
+```Python
+from dgaintel import get_prediction
+
+get_prediction('microsoft.com')
+get_prediction(['microsoft.com', 'wikipedia.com', 'vlurgpeddygdy.com'])
+get_prediction('domains.txt')
+get_prediction('domains.txt', to_file='domain_predictions.txt')
+```
+
+The `get_prob` function will perform the inference and provide the prediction floats. It is helpful if you want to use the prediction scores directly in your workflow.
+```Python
+from dgaintel import get_prob
+
+get_prob('microsoft.com') # 0.00050851
+get_prob(['microsoft.com', 'wikipedia.com', 'vlurgpeddygdy.com']) # [('microsoft.com', 0.00050), ('wikipedia.com', 0.00033), ('vlurgpeddygdy.com', 0.0.97601)]
+get_prob('domains.txt') # [('microsoft.com', 0.00050), ('wikipedia.com', 0.00033), ('vlurgpeddygdy.com', 0.97601)]
+get_prob(['microsoft.com', 'wikipedia.com', 'google.com'], raw=True) # array([0.00050, 0.00033, 0.0.97601], dtype=float32)
+```
+
+The `Intel` interface allows DGAIntel to avoid checking certain domains with known TLDs to ensure enterprise functions are not compromised.
+```Python
+from dgaintel import Intel
+
+intel = Intel(['microsoft.com'])
+intel.get_prob('microsoft.com') # 0.0
+intel.get_prob(['microsoft.com', 'wikipedia.com', 'vlurgpeddygdy.com']) # [('microsoft.com', 0.0), ('wikipedia.com', 0.00033), ('vlurgpeddygdy.com', 0.0.97601)]
+intel.get_prob('domains.txt') # [('microsoft.com', 0.0), ('wikipedia.com', 0.00033), ('vlurgpeddygdy.com', 0.97601)]
+intel.get_prob(['microsoft.com', 'wikipedia.com', 'google.com'], raw=True) # array([0.0, 0.00033, 0.0.97601], dtype=float32)
+```
+
+%prep
+%autosetup -n dgaintel-2.3
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-dgaintel -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 2.3-1
+- Package Spec generated