You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Develop a NLP Model in Python & Deploy It with Flask, Step by Step

Flask API, Document Classification, Spam Filter

By far, we have developed many machine learning models, generated numeric predictions on the testing data, and tested the results. And we did everything offline. In reality, generating predictions is only part of a machine learning project, although it is the most important part in my opinion.

Considering a system using machine learning to detect spam SMS text messages. Our ML systems workflow is like this: Train offline -> Make model available as a service -> Predict online.

A classifier is trained offline with spam and non-spam messages.

The trained model is deployed as a service to serve users.

Figure 1

When we develop a machine learning model, we need to think about how to deploy it, that is, how to make this model available to other users.

Kaggle and Data science bootcamps are great for learning how to build and optimize models, but they don’t teach engineers how to take them to the next step, where there’s a major difference between building a model, and actually getting it ready for people to use in their products and services.

In this article, we will focus on both: building a machine learning model for spam SMS message classification, then create an API for the model, using Flask, the Python micro framework for building web applications.This API allows us to utilize the predictive capabilities through HTTP requests. Let’s get started!

ML Model Building

The data is a collection of SMS messages tagged as spam or ham that can be found here. First, we will use this dataset to build a prediction model that will accurately classify which texts are spam.

Naive Bayes classifiers are a popular statistical technique of e-mail filtering. They typically use bag of words features to identify spam e-mail. Therefore, We’ll build a simple message classifier using Naive Bayes theorem.

NB_spam.py

Figure 2

Not only Naive Bayes classifier is easy to implement but also provides very good result.

After training the model, it is desirable to have a way to persist the model for future use without having to retrain. To achieve this, we add the following lines to save our model as a .pkl file for the later use.

from sklearn.externals import joblib

joblib.dump(clf, 'NB_spam_model.pkl')

And we can load and use saved model later like so:

NB_spam_model = open('NB_spam_model.pkl','rb')

clf = joblib.load(NB_spam_model)

The above process called “persist model in a standard format”, that is, models are persisted in a certain format specific to the language in development.

And the model will be served in a micro-service that expose endpoints to receive requests from client. This is the next step.

Turning the Spam Message Classifier into a Web Application

Having prepared the code for classifying SMS messages in the previous section, we will develop a web application that consists of a simple web page with a form field that lets us enter a message. After submitting the message to the web application, it will render it on a new page which gives us a result of spam or not spam.

First, we create a folder for this project called SMS-Message-Spam-Detector , this is the directory tree inside the folder. We will explain each file.

spam.csv

app.py

templates/

home.html

result.html

static/

style.css

SMS-Message-Spam-Detector folder

templates folder

static folder

SMS Message Spam Detector folder

The sub-directory templates is the directory in which Flask will look for static HTML files for rendering in the web browser, in our case, we have two html files: home.html and result.html .

app.py

The app.py file contains the main code that will be executed by the Python interpreter to run the Flask web application, it included the ML code for classifying SMS messages:

app.py

We ran our application as a single module; thus we initialized a new Flask instance with the argument __name__ to let Flask know that it can find the HTML template folder ( templates ) in the same directory where it is located.

to let Flask know that it can find the HTML template folder ( ) in the same directory where it is located. Next, we used the route decorator ( @app.route('/') ) to specify the URL that should trigger the execution of the home function.

) to specify the URL that should trigger the execution of the function. Our home function simply rendered the home.html HTML file, which is located in the templates folder.

function simply rendered the HTML file, which is located in the folder. Inside the predict function, we access the spam data set, pre-process the text, and make predictions, then store the model. We access the new message entered by the user and use our model to make a prediction for its label.

function, we access the spam data set, pre-process the text, and make predictions, then store the model. We access the new message entered by the user and use our model to make a prediction for its label. we used the POST method to transport the form data to the server in the message body. Finally, by setting the debug=True argument inside the app.run method, we further activated Flask's debugger.

method to transport the form data to the server in the message body. Finally, by setting the argument inside the method, we further activated Flask's debugger. Lastly, we used the run function to only run the application on the server when this script is directly executed by the Python interpreter, which we ensured using the if statement with __name__ == '__main__' .

home.html

The following are the contents of the home.html file that will render a text form where a user can enter a message:

home.html

style.css

In the header section of home.html , we loaded styles.css file. CSS is to determine how the look and feel of HTML documents. styles.css has to be saved in a sub-directory called static , which is the default directory where Flask looks for static files such as CSS.

style.css

result.html

we create a result.html file that will be rendered via the render_template('result.html', prediction=my_prediction) line return inside the predict function, which we defined in the app.py script to display the text that a user submitted via the text field. The result.html file contains the following content:

result.html

From result.htm we can see that some code using syntax not normally found in HTML files: {% if prediction ==1%},{% elif prediction == 0%},{% endif %} This is jinja syntax, and it is used to access the prediction returned from our HTTP request within the HTML file.

We are almost there!

Once you have done all of the above, you can start running the API by either double click appy.py , or executing the command from the Terminal:

cd SMS-Message-Spam-Detector

python app.py

You should get the following output:

Figure 3

Now you could open a web browser and navigate to http://127.0.0.1:5000/, we should see a simple website with the content like so:

Figure 4

Let’s test our work!

spam_detector_app

Congratulations! We have now created an end-to-end machine learning (NLP) application at zero cost. If you look it back, the overall process is not complicated at all. With a little bit patience and desire to learn, anyone can do it. All the open-source tools make every thing possible.

More importantly, we are able to extend our knowledge of machine learning theory to a useful and practical web application and lets us make our SMS spam message classifier available to the outside world!

The complete working source code is available at this repository. Have a great week!

Reference:

Book: Python Machine Learning