Repository: MeqdadDev/teachable-machine (language: python)

Issue:
Dev 1.2 add package logo



Discussion / hints:

Fix patch:
diff --git a/.gitignore b/.gitignore
index c0650ec..d0deab4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -140,7 +140,7 @@ venv.bak/
 .ropeproject
 
 # mkdocs documentation
-/site
+#/docs
 
 # mypy
 .mypy_cache/
@@ -177,4 +177,14 @@ src/keras_model.h5
 src/labels.txt
 src/run_tm.py
 src/screenshot.jpg
-# End
\ No newline at end of file
+# End
+/.idea/.gitignore
+/src/model/keras_model.h5
+/src/model/labels.txt
+/.idea/misc.xml
+/.idea/modules.xml
+/.idea/inspectionProfiles/profiles_settings.xml
+/src/run_demo.py
+/screenshot.jpg
+/.idea/teachable-machine.iml
+/.idea/vcs.xml
diff --git a/README.md b/README.md
index 3289a75..293b46b 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,27 @@
 # Teachable Machine
+_By: [Meqdad Darwish](https://github.com/MeqdadDev)_
+
+
+<p align="center">
+<picture>
+  <img alt="Teachable Machine Package Logo" src="logo.png" width="50%" height="50%" >
+</picture>
+</p>
 
 [![Downloads](https://static.pepy.tech/badge/teachable-machine)](https://pepy.tech/project/teachable-machine)
 [![MIT License](https://img.shields.io/badge/License-MIT-green.svg)](https://choosealicense.com/licenses/mit/)
+[![PyPI](https://img.shields.io/pypi/v/teachable-machine)](https://pypi.org/project/teachable-machine/)
+
+A Python package designed to simplify the integration of exported models from Google's [Teachable Machine](https://teachablemachine.withgoogle.com/) platform into various environments.
+This tool was specifically crafted to work seamlessly with Teachable Machine, making it easier to implement and use your trained models.
 
-A Python package to simplify the deployment process of exported [Teachable Machine](https://teachablemachine.withgoogle.com/) models into Python projects.
+Source Code is published on [GitHub](https://github.com/MeqdadDev/teachable-machine)
+
+Read more about the project (requirements, installation, examples and more) in the [Documentation Website](https://meqdaddev.github.io/teachable-machine/) 
 
 ## Supported Classifiers
 
-**Image Classification**: use exported keras model from Teachable Machine platfrom.
+**Image Classification**: use exported keras model from Teachable Machine platform.
 
 ## Requirements
 
@@ -19,14 +33,6 @@ A Python package to simplify the deployment process of exported [Teachable Machi
 pip install teachable-machine
 ```
 
-## Dependencies
-
-```bash
-numpy
-Pillow
-tensorflow
-```
-
 ## Example
 
 An example for teachable machine package with OpenCV:
@@ -45,7 +51,7 @@ while True:
     _, img = cap.read()
     cv.imwrite(image_path, img)
 
-    result = model.classify_image(image_path)
+    result, resultImage = model.classify_and_show(image_path)
 
     print("class_index", result["class_index"])
 
@@ -55,15 +61,26 @@ while True:
 
     print("predictions:", result["predictions"])
 
-    cv.imshow("Video Stream", img)
+    cv.imshow("Video Stream", resultImage)
 
-    cv.waitKey(1)
+    k = cv.waitKey(1)
+    if k == 27:  # Press ESC to close the camera view
+        break
+    
+cap.release()
+cv.destroyAllWindows()
 ```
 
-`class_index` and `class_name`  are assigned based on the content of labels.txt file.
+Values of `result` are assigned based on the content of `labels.txt` file.
+
+For more; take a look on [these examples](https://meqdaddev.github.io/teachable-machine/codeExamples/)
+
+### Links:
+
+- [Documentation](https://meqdaddev.github.io/teachable-machine)
 
-Links:
+- [PyPI](https://pypi.org/project/teachable-machine/)
 
-[PyPI](https://pypi.org/project/teachable-machine/)
+- [Source Code](https://github.com/MeqdadDev/teachable-machine)
 
-[Source Code](https://github.com/MeqdadDev/teachable-machine)
+- [Teachable Machine Platform](https://teachablemachine.withgoogle.com/)
diff --git a/docs/codeExamples.md b/docs/codeExamples.md
new file mode 100644
index 0000000..0f72724
--- /dev/null
+++ b/docs/codeExamples.md
@@ -0,0 +1,105 @@
+## Code Examples
+
+Before running any of the code examples, ensure that the package is installed successfully.
+You can find the installation instructions [here](https://meqdaddev.github.io/teachable-machine/how-to-guide/#how-to-install-the-package).
+
+You also need to have an exported model (with `.h5` file extension) with an associated labels text file for the package to use in annotations.
+
+Expected structure before running the code examples:
+
+```
+test-directory/
+├── keras_model.h5
+├── labels.txt
+└── app.py (your code example file)
+```
+
+### Example 1
+
+In this example for the teachable machine package with OpenCV, we will classify frames coming from the camera view and display 
+the classification results on the camera view itself. We use `classify_and_show` to achieve this. See the code example below:
+
+```python
+from teachable_machine import TeachableMachine
+import cv2 as cv
+
+cap = cv.VideoCapture(0)
+model = TeachableMachine(model_path="keras_model.h5",
+                         labels_file_path="labels.txt")
+
+image_path = "screenshot.jpg"
+
+while True:
+    _, img = cap.read()
+    cv.imwrite(image_path, img)
+
+    result, resultImage = model.classify_and_show(image_path)
+
+    print("class_index", result["class_index"])
+
+    print("class_name:::", result["class_name"])
+
+    print("class_confidence:", result["class_confidence"])
+
+    print("predictions:", result["predictions"])
+
+    cv.imshow("Video Stream", resultImage)
+
+    k = cv.waitKey(1)
+    if k == 27:  # Press ESC to close the camera view
+        break
+    
+cap.release()
+cv.destroyAllWindows()
+```
+
+The values of `result` are assigned based on the content of the `labels.txt` file.
+
+### Example 2
+
+In this example for the teachable machine package with OpenCV, we will classify frames coming from the camera view and display 
+the classification results on the camera view itself, but with separated methods for each task.
+
+We use `classify_image` to classify the captured image but without showing the results on the camera view. Also, we use `show_prediction_on_image` to
+get a frame with previous classification results.
+
+
+Note that the `classify_image` method returns only the prediction results (a Python `dict`), not an image (numpy array or PIL.Image).
+
+See the code example below:
+
+```python
+from teachable_machine import TeachableMachine
+import cv2 as cv
+
+cap = cv.VideoCapture(0)
+model = TeachableMachine(model_path="keras_model.h5",
+                         labels_file_path="labels.txt")
+
+image_path = "screenshot.jpg"
+
+while True:
+    _, img = cap.read()
+    cv.imwrite(image_path, img)
+
+    result = model.classify_image(image_path)
+    
+    resultImage = model.show_prediction_on_image(image_path, result)
+
+    print("class_index", result["class_index"])
+
+    print("class_name:::", result["class_name"])
+
+    print("class_confidence:", result["class_confidence"])
+
+    print("predictions:", result["predictions"])
+
+    cv.imshow("Video Stream", resultImage)
+
+    k = cv.waitKey(1)
+    if k == 27:  # Press ESC to close the camera view
+        break
+    
+cap.release()
+cv.destroyAllWindows()
+```
diff --git a/docs/contribution.md b/docs/contribution.md
new file mode 100644
index 0000000..504d6cb
--- /dev/null
+++ b/docs/contribution.md
@@ -0,0 +1,16 @@
+## Contributing
+
+### Instructions
+Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
+
+For new features, please make sure you've tested your code in real environment. 
+And to avoid duplications, please take a sneak peek on the project structure and practices before making your PR.
+
+For more details and questions, contact me via links below.
+
+### 🔗 Contact me
+[![Email Badge](https://img.shields.io/badge/Gmail-Contact_Me?style=for-the-badge&logo=gmail&logoColor=white)](mailto:meqdad.darweesh@gmail.com)
+
+[![linkedin](https://img.shields.io/badge/linkedin-0A66C2?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/meqdad-darwish/)
+
+[![twitter](https://img.shields.io/badge/twitter-1DA1F2?style=for-the-badge&logo=twitter&logoColor=white)](https://twitter.com/MeqdadDev)
diff --git a/docs/explanation.md b/docs/explanation.md
new file mode 100644
index 0000000..23f506d
--- /dev/null
+++ b/docs/explanation.md
@@ -0,0 +1,5 @@
+## Explanation
+
+### Package Components
+
+::: src.teachable_machine
\ No newline at end of file
diff --git a/docs/how-to-guide.md b/docs/how-to-guide.md
new file mode 100644
index 0000000..6b8e644
--- /dev/null
+++ b/docs/how-to-guide.md
@@ -0,0 +1,7 @@
+# How-To Guide
+
+## How to install the package
+
+```bash
+pip install teachable-machine
+```
diff --git a/docs/index.md b/docs/index.md
new file mode 100644
index 0000000..a0cbe0d
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,38 @@
+# Welcome to Teachable Machine Package
+_By: [Meqdad Darwish](https://github.com/MeqdadDev)_
+
+<p align="center">
+<picture>
+  <img alt="Teachable Machine Package Logo" src="logo.png" width="80%" height="80%" >
+</picture>
+</p>
+
+[![Downloads](https://static.pepy.tech/badge/teachable-machine)](https://pepy.tech/project/teachable-machine)
+[![MIT License](https://img.shields.io/badge/License-MIT-green.svg)](https://choosealicense.com/licenses/mit/)
+[![PyPI](https://img.shields.io/pypi/v/teachable-machine)](https://pypi.org/project/teachable-machine/)
+
+## Description
+A Python package designed to simplify the integration of exported models from Google's [Teachable Machine](https://teachablemachine.withgoogle.com/) platform into various environments.
+This tool was specifically crafted to work seamlessly with Teachable Machine, making it easier to implement and use your trained models.
+
+Source Code is published on [GitHub](https://github.com/MeqdadDev/teachable-machine)
+
+## Table Of Contents
+
+1. [How-To Guide](how-to-guide.md)
+2. [Requirements](requirements.md)
+2. [Code Examples](codeExamples.md)
+3. [Explanation](explanation.md)
+4. [Contributing](contribution.md)
+
+## Supported Classifiers
+
+**Image Classification**
+
+## Links
+
+- [PyPI](https://pypi.org/project/teachable-machine/)
+
+- [Source Code](https://github.com/MeqdadDev/teachable-machine)
+
+- [Teachable Machine Platform](https://teachablemachine.withgoogle.com/)
diff --git a/docs/logo.png b/docs/logo.png
new file mode 100644
index 0000000..eb271b7
Binary files /dev/null and b/docs/logo.png differ
diff --git a/docs/requirements.md b/docs/requirements.md
new file mode 100644
index 0000000..15a58af
--- /dev/null
+++ b/docs/requirements.md
@@ -0,0 +1,12 @@
+## Requirements
+
+### Python Version
+``` Python >= 3.7 ```
+
+### Dependencies
+
+```bash
+numpy
+Pillow
+tensorflow
+```
diff --git a/logo.png b/logo.png
new file mode 100644
index 0000000..eb271b7
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diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 0000000..16b9a09
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,15 @@
+site_name: Teachable Machine Package - Documentation
+
+theme:
+  name: "readthedocs"
+
+plugins:
+  - mkdocstrings
+
+nav:
+  - Teachable Machine - Home: index.md
+  - How-To Guide: how-to-guide.md
+  - Requirements: requirements.md
+  - Code Examples: codeExamples.md
+  - Explanation: explanation.md
+  - Contributing: contribution.md
diff --git a/src/requirements.txt b/requirements.txt
similarity index 100%
rename from src/requirements.txt
rename to requirements.txt
diff --git a/site/404.html b/site/404.html
new file mode 100644
index 0000000..f20429c
--- /dev/null
+++ b/site/404.html
@@ -0,0 +1,121 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+    <meta charset="utf-8" />
+    <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+      <link rel="shortcut icon" href="/img/favicon.ico" />
+    <title>Teachable Machine Package - Documentation</title>
+    <link rel="stylesheet" href="/css/theme.css" />
+    <link rel="stylesheet" href="/css/theme_extra.css" />
+        <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/styles/github.min.css" />
+        <link href="/assets/_mkdocstrings.css" rel="stylesheet" />
+    
+    <!--[if lt IE 9]>
+      <script src="/js/html5shiv.min.js"></script>
+    <![endif]-->
+      <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min.js"></script>
+      <script>hljs.highlightAll();</script> 
+</head>
+
+<body class="wy-body-for-nav" role="document">
+
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side stickynav">
+    <div class="wy-side-scroll">
+      <div class="wy-side-nav-search">
+          <a href="/." class="icon icon-home"> Teachable Machine Package - Documentation
+        </a>
+      </div>
+
+      <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="/.">Teachable Machine - Home</a>
+                </li>
+              </ul>
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="/how-to-guide/">How-To Guide</a>
+                </li>
+              </ul>
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="/requirements/">Requirements</a>
+                </li>
+              </ul>
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="/codeExamples/">Code Examples</a>
+                </li>
+              </ul>
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="/explanation/">Explanation</a>
+                </li>
+              </ul>
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="/contribution/">Contributing</a>
+                </li>
+              </ul>
+      </div>
+    </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
+      <nav class="wy-nav-top" role="navigation" aria-label="Mobile navigation menu">
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="/.">Teachable Machine Package - Documentation</a>
+        
+      </nav>
+      <div class="wy-nav-content">
+        <div class="rst-content"><div role="navigation" aria-label="breadcrumbs navigation">
+  <ul class="wy-breadcrumbs">
+    <li><a href="/." class="icon icon-home" aria-label="Docs"></a></li>
+    <li class="wy-breadcrumbs-aside">
+    </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+            <div class="section" itemprop="articleBody">
+              
+
+  <h1 id="404-page-not-found">404</h1>
+
+  <p><strong>Page not found</strong></p>
+
+
+            </div>
+          </div><footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <!-- Copyright etc -->
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+
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+</footer>
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+  <div class="rst-versions" role="note" aria-label="Versions">
+  <span class="rst-current-version" data-toggle="rst-current-version">
+    
+    
+    
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+</div>
+    <script src="/js/jquery-3.6.0.min.js"></script>
+    <script>var base_url = "/";</script>
+    <script src="/js/theme_extra.js"></script>
+    <script src="/js/theme.js"></script>
+    <script>
+        jQuery(function () {
+            SphinxRtdTheme.Navigation.enable(true);
+        });
+    </script>
+
+</body>
+</html>
diff --git a/site/assets/_mkdocstrings.css b/site/assets/_mkdocstrings.css
new file mode 100644
index 0000000..8a5d3cf
--- /dev/null
+++ b/site/assets/_mkdocstrings.css
@@ -0,0 +1,57 @@
+
+/* Avoid breaking parameters name, etc. in table cells. */
+.doc-contents td code {
+  word-break: normal !important;
+}
+
+/* No line break before first paragraph of descriptions. */
+.doc-md-description,
+.doc-md-description>p:first-child {
+  display: inline;
+}
+
+/* Avoid breaking code headings. */
+.doc-heading code {
+  white-space: normal;
+}
+
+/* Improve rendering of parameters, returns and exceptions. */
+.doc-contents .field-name {
+  min-width: 100px;
+}
+
+/* Other curious-spacing fixes. */
+.doc-contents .field-name,
+.doc-contents .field-body {
+  border: none !important;
+  padding: 0 !important;
+}
+
+.doc-contents p {
+  margin: 1em 0 1em;
+}
+
+.doc-contents .field-list {
+  margin: 0 !important;
+}
+
+.doc-contents pre {
+  padding: 0 !important;
+}
+
+.doc-contents .wy-table-responsive {
+  margin-bottom: 0 !important;
+}
+
+.doc-contents td.code {
+  padding: 0 !important;
+}
+
+.doc-contents td.linenos {
+  padding: 0 8px !important;
+}
+
+.doc-children,
+footer {
+  margin-top: 20px;
+}
\ No newline at end of file
diff --git a/site/codeExamples/index.html b/site/codeExamples/index.html
new file mode 100644
index 0000000..178fc11
--- /dev/null
+++ b/site/codeExamples/index.html
@@ -0,0 +1,225 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+    <meta charset="utf-8" />
+    <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+      <link rel="shortcut icon" href="../img/favicon.ico" />
+    <title>Code Examples - Teachable Machine Package - Documentation</title>
+    <link rel="stylesheet" href="../css/theme.css" />
+    <link rel="stylesheet" href="../css/theme_extra.css" />
+        <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/styles/github.min.css" />
+        <link href="../assets/_mkdocstrings.css" rel="stylesheet" />
+    
+      <script>
+        // Current page data
+        var mkdocs_page_name = "Code Examples";
+        var mkdocs_page_input_path = "codeExamples.md";
+        var mkdocs_page_url = null;
+      </script>
+    
+    <!--[if lt IE 9]>
+      <script src="../js/html5shiv.min.js"></script>
+    <![endif]-->
+      <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min.js"></script>
+      <script>hljs.highlightAll();</script> 
+</head>
+
+<body class="wy-body-for-nav" role="document">
+
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side stickynav">
+    <div class="wy-side-scroll">
+      <div class="wy-side-nav-search">
+          <a href=".." class="icon icon-home"> Teachable Machine Package - Documentation
+        </a>
+      </div>
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+      <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="..">Teachable Machine - Home</a>
+                </li>
+              </ul>
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="../how-to-guide/">How-To Guide</a>
+                </li>
+              </ul>
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="../requirements/">Requirements</a>
+                </li>
+              </ul>
+              <ul class="current">
+                <li class="toctree-l1 current"><a class="reference internal current" href="#">Code Examples</a>
+    <ul class="current">
+    <li class="toctree-l2"><a class="reference internal" href="#example-1">Example 1</a>
+    </li>
+    <li class="toctree-l2"><a class="reference internal" href="#example-2">Example 2</a>
+    </li>
+    </ul>
+                </li>
+              </ul>
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="../explanation/">Explanation</a>
+                </li>
+              </ul>
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="../contribution/">Contributing</a>
+                </li>
+              </ul>
+      </div>
+    </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
+      <nav class="wy-nav-top" role="navigation" aria-label="Mobile navigation menu">
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+      <li class="breadcrumb-item active">Code Examples</li>
+    <li class="wy-breadcrumbs-aside">
+    </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+            <div class="section" itemprop="articleBody">
+              
+                <h2 id="code-examples">Code Examples</h2>
+<p>Before running any of the code examples, ensure that the package is installed successfully.
+You can find the installation instructions <a href="https://meqdaddev.github.io/teachable-machine/how-to-guide/#how-to-install-the-package">here</a>.</p>
+<p>You also need to have an exported model (with <code>.h5</code> file extension) with an associated labels text file for the package to use in annotations.</p>
+<p>Expected structure before running the code examples:</p>
+<pre><code>test-directory/
+├── keras_model.h5
+├── labels.txt
+└── app.py (your code example file)
+</code></pre>
+<h3 id="example-1">Example 1</h3>
+<p>In this example for the teachable machine package with OpenCV, we will classify frames coming from the camera view and display 
+the classification results on the camera view itself. We use <code>classify_and_show</code> to achieve this. See the code example below:</p>
+<pre><code class="language-python">from teachable_machine import TeachableMachine
+import cv2 as cv
+
+cap = cv.VideoCapture(0)
+model = TeachableMachine(model_path=&quot;keras_model.h5&quot;,
+                         labels_file_path=&quot;labels.txt&quot;)
+
+image_path = &quot;screenshot.jpg&quot;
+
+while True:
+    _, img = cap.read()
+    cv.imwrite(image_path, img)
+
+    result, resultImage = model.classify_and_show(image_path)
+
+    print(&quot;class_index&quot;, result[&quot;class_index&quot;])
+
+    print(&quot;class_name:::&quot;, result[&quot;class_name&quot;])
+
+    print(&quot;class_confidence:&quot;, result[&quot;class_confidence&quot;])
+
+    print(&quot;predictions:&quot;, result[&quot;predictions&quot;])
+
+    cv.imshow(&quot;Video Stream&quot;, resultImage)
+
+    k = cv.waitKey(1)
+    if k == 27:  # Press ESC to close the camera view
+        break
+
+cap.release()
+cv.destroyAllWindows()
+</code></pre>
+<p>The values of <code>result</code> are assigned based on the content of the <code>labels.txt</code> file.</p>
+<h3 id="example-2">Example 2</h3>
+<p>In this example for the teachable machine package with OpenCV, we will classify frames coming from the camera view and display 
+the classification results on the camera view itself, but with separated methods for each task.</p>
+<p>We use <code>classify_image</code> to classify the captured image but without showing the results on the camera view. Also, we use <code>show_prediction_on_image</code> to
+get a frame with previous classification results.</p>
+<p>Note that the <code>classify_image</code> method returns only the prediction results (a Python <code>dict</code>), not an image (numpy array or PIL.Image).</p>
+<p>See the code example below:</p>
+<pre><code class="language-python">from teachable_machine import TeachableMachine
+import cv2 as cv
+
+cap = cv.VideoCapture(0)
+model = TeachableMachine(model_path=&quot;keras_model.h5&quot;,
+                         labels_file_path=&quot;labels.txt&quot;)
+
+image_path = &quot;screenshot.jpg&quot;
+
+while True:
+    _, img = cap.read()
+    cv.imwrite(image_path, img)
+
+    result = model.classify_image(image_path)
+
+    resultImage = model.show_prediction_on_image(image_path, result)
+
+    print(&quot;class_index&quot;, result[&quot;class_index&quot;])
+
+    print(&quot;class_name:::&quot;, result[&quot;class_name&quot;])
+
+    print(&quot;class_confidence:&quot;, result[&quot;class_confidence&quot;])
+
+    print(&quot;predictions:&quot;, result[&quot;predictions&quot;])
+
+    cv.imshow(&quot;Video Stream&quot;, resultImage)
+
+    k = cv.waitKey(1)
+    if k == 27:  # Press ESC to close the camera view
+        break
+
+cap.release()
+cv.destroyAllWindows()
+</code></pre>
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+<p>For new features, please make sure you've tested your code in real environment. 
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new file mode 100644
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--- /dev/null
+++ b/site/css/theme_extra.css
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+/*
+ * Wrap inline code samples otherwise they shoot of the side and
+ * can't be read at all.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/313
+ * https://github.com/mkdocs/mkdocs/issues/233
+ * https://github.com/mkdocs/mkdocs/issues/834
+ */
+.rst-content code {
+    white-space: pre-wrap;
+    word-wrap: break-word;
+    padding: 2px 5px;
+}
+
+/**
+ * Make code blocks display as blocks and give them the appropriate
+ * font size and padding.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/855
+ * https://github.com/mkdocs/mkdocs/issues/834
+ * https://github.com/mkdocs/mkdocs/issues/233
+ */
+.rst-content pre code {
+    white-space: pre;
+    word-wrap: normal;
+    display: block;
+    padding: 12px;
+    font-size: 12px;
+}
+
+/**
+ * Fix code colors
+ *
+ * https://github.com/mkdocs/mkdocs/issues/2027
+ */
+.rst-content code {
+    color: #E74C3C;
+}
+
+.rst-content pre code {
+    color: #000;
+    background: #f8f8f8;
+}
+
+/*
+ * Fix link colors when the link text is inline code.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/718
+ */
+a code {
+    color: #2980B9;
+}
+a:hover code {
+    color: #3091d1;
+}
+a:visited code {
+    color: #9B59B6;
+}
+
+/*
+ * The CSS classes from highlight.js seem to clash with the
+ * ReadTheDocs theme causing some code to be incorrectly made
+ * bold and italic.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/411
+ */
+pre .cs, pre .c {
+    font-weight: inherit;
+    font-style: inherit;
+}
+
+/*
+ * Fix some issues with the theme and non-highlighted code
+ * samples. Without and highlighting styles attached the
+ * formatting is broken.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/319
+ */
+.rst-content .no-highlight {
+    display: block;
+    padding: 0.5em;
+    color: #333;
+}
+
+
+/*
+ * Additions specific to the search functionality provided by MkDocs
+ */
+
+.search-results {
+    margin-top: 23px;
+}
+
+.search-results article {
+    border-top: 1px solid #E1E4E5;
+    padding-top: 24px;
+}
+
+.search-results article:first-child {
+    border-top: none;
+}
+
+form .search-query {
+    width: 100%;
+    border-radius: 50px;
+    padding: 6px 12px;
+    border-color: #D1D4D5;
+}
+
+/*
+ * Improve inline code blocks within admonitions.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/656
+ */
+ .rst-content .admonition code {
+    color: #404040;
+    border: 1px solid #c7c9cb;
+    border: 1px solid rgba(0, 0, 0, 0.2);
+    background: #f8fbfd;
+    background: rgba(255, 255, 255, 0.7);
+}
+
+/*
+ * Account for wide tables which go off the side.
+ * Override borders to avoid weirdness on narrow tables.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/834
+ * https://github.com/mkdocs/mkdocs/pull/1034
+ */
+.rst-content .section .docutils {
+    width: 100%;
+    overflow: auto;
+    display: block;
+    border: none;
+}
+
+td, th {
+    border: 1px solid #e1e4e5 !important;
+    border-collapse: collapse;
+}
+
+/*
+ * Without the following amendments, the navigation in the theme will be
+ * slightly cut off. This is due to the fact that the .wy-nav-side has a
+ * padding-bottom of 2em, which must not necessarily align with the font-size of
+ * 90 % on the .rst-current-version container, combined with the padding of 12px
+ * above and below. These amendments fix this in two steps: First, make sure the
+ * .rst-current-version container has a fixed height of 40px, achieved using
+ * line-height, and then applying a padding-bottom of 40px to this container. In
+ * a second step, the items within that container are re-aligned using flexbox.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/2012
+ */
+ .wy-nav-side {
+    padding-bottom: 40px;
+}
+
+/* For section-index only */
+.wy-menu-vertical .current-section p {
+    background-color: #e3e3e3;
+    color: #404040;
+}
+
+/*
+ * The second step of above amendment: Here we make sure the items are aligned
+ * correctly within the .rst-current-version container. Using flexbox, we
+ * achieve it in such a way that it will look like the following:
+ *
+ * [No repo_name]
+ *         Next >>                    // On the first page
+ * << Previous     Next >>            // On all subsequent pages
+ *
+ * [With repo_name]
+ *    <repo_name>        Next >>      // On the first page
+ * <repo_name>  << Previous  Next >>  // On all subsequent pages
+ *
+ * https://github.com/mkdocs/mkdocs/issues/2012
+ */
+.rst-versions .rst-current-version {
+    padding: 0 12px;
+    display: flex;
+    font-size: initial;
+    justify-content: space-between;
+    align-items: center;
+    line-height: 40px;
+}
+
+/*
+ * Please note that this amendment also involves removing certain inline-styles
+ * from the file ./mkdocs/themes/readthedocs/versions.html.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/2012
+ */
+.rst-current-version span {
+    flex: 1;
+    text-align: center;
+}
diff --git a/site/explanation/index.html b/site/explanation/index.html
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--- /dev/null
+++ b/site/explanation/index.html
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+<html class="writer-html5" lang="en" >
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+    <title>Explanation - Teachable Machine Package - Documentation</title>
+    <link rel="stylesheet" href="../css/theme.css" />
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+        <link href="../assets/_mkdocstrings.css" rel="stylesheet" />
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+      <script>hljs.highlightAll();</script> 
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+
+<body class="wy-body-for-nav" role="document">
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+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side stickynav">
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+          <a href=".." class="icon icon-home"> Teachable Machine Package - Documentation
+        </a>
+      </div>
+
+      <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="..">Teachable Machine - Home</a>
+                </li>
+              </ul>
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="../how-to-guide/">How-To Guide</a>
+                </li>
+              </ul>
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="../requirements/">Requirements</a>
+                </li>
+              </ul>
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="../codeExamples/">Code Examples</a>
+                </li>
+              </ul>
+              <ul class="current">
+                <li class="toctree-l1 current"><a class="reference internal current" href="#">Explanation</a>
+    <ul class="current">
+    <li class="toctree-l2"><a class="reference internal" href="#package-components">Package Components</a>
+    </li>
+    </ul>
+                </li>
+              </ul>
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="../contribution/">Contributing</a>
+                </li>
+              </ul>
+      </div>
+    </div>
+    </nav>
+
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+      <li class="breadcrumb-item active">Explanation</li>
+    <li class="wy-breadcrumbs-aside">
+    </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+            <div class="section" itemprop="articleBody">
+              
+                <h2 id="explanation">Explanation</h2>
+<h3 id="package-components">Package Components</h3>
+
+
+<div class="doc doc-object doc-module">
+
+
+
+<a id="src.teachable_machine"></a>
+    <div class="doc doc-contents first">
+
+
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+
+<div class="doc doc-object doc-class">
+
+
+
+<h2 id="src.teachable_machine.TeachableMachine" class="doc doc-heading">
+            <code>TeachableMachine</code>
+
+
+</h2>
+
+
+    <div class="doc doc-contents ">
+            <p class="doc doc-class-bases">
+              Bases: <code>object</code></p>
+
+
+      <p>Create a TeachableMachine object to run pre-trained AI models.</p>
+
+              <details class="quote">
+                <summary>Source code in <code>src/teachable_machine.py</code></summary>
+                <pre class="highlight"><code class="language-python">class TeachableMachine(object):
+    """
+    Create a TeachableMachine object to run pre-trained AI models.
+    """
+
+    SUPPORTED_TYPES = {"keras", "h5"}
+    IMAGE_SIZE = (224, 224)
+
+    def __init__(
+        self,
+        model_path="keras_model.h5",
+        labels_file_path="labels.txt",
+        model_type="h5",
+    ) -&gt; None:
+        self._model_type = model_type.lower()
+        if self._model_type not in self.SUPPORTED_TYPES:
+            raise ValueError(
+                f"Unsupported model type: {self._model_type}. Use 'keras' or 'h5'."
+            )
+        np.set_printoptions(suppress=True)
+
+        self._load_model(model_path)
+        self._load_labels(labels_file_path)
+        print("Teachable Machine Object is created successfully.")
+
+    def _load_model(self, model_path: str):
+        try:
+            self._model = load_model(model_path, compile=False)
+        except IOError as e:
+            print("LoadingModelError: Error while loading Teachable Machine model")
+            raise IOError("Error loading model") from e
+        except Exception as e:
+            print("LoadingModelError: Error while loading Teachable Machine model")
+            raise FileNotFoundError("Model file not found") from e
+
+    def _load_labels(self, labels_file_path):
+        try:
+            with open(labels_file_path, "r") as file:
+                self._labels = file.readlines()
+        except IOError as e:
+            print("LoadingLabelsError: Error while loading labels.txt file")
+            raise IOError("Error loading labels") from e
+        except Exception as e:
+            print("LoadingLabelsError: Error while loading labels.txt file")
+            raise FileNotFoundError("Labels file not found") from e
+
+    def _open_image(self, image_path):
+        """
+        Open an image file and convert it to RGB mode.
+
+        Parameters:
+        image_path (str): Path to the image file.
+
+        Returns:
+        PIL.Image.Image: Opened image in RGB mode.
+        """
+        try:
+            return Image.open(image_path).convert("RGB")
+        except FileNotFoundError as e:
+            print("ImageNotFound: Error in image file.")
+            raise FileNotFoundError("Image file not found") from e
+        except Exception as e:
+            print("ImageTypeError: Error while opening or converting image")
+            raise TypeError("Unsupported image type") from e
+
+    def classify_image(self, image_path: str):
+        """
+        Classify an image using the pre-trained model.
+
+        Parameters:
+        image_path (str): Path of the image to be classified.
+
+        Returns:
+        dict: Classification results including class name, index, confidence and predictions.
+        """
+        image = self._open_image(image_path)
+        return self._get_image_classification(image)
+
+    def _get_image_classification(self, image):
+        data = self._preprocess_image(image)
+        prediction = self._model.predict(data)
+        class_index = np.argmax(prediction)
+        class_name = self._labels[class_index].strip()
+        class_confidence = prediction[0][class_index]
+
+        return {
+            "class_name": class_name,
+            "highest_class_name": class_name,
+            "highest_class_id": class_index,
+            "class_index": class_index,
+            "class_id": class_index,
+            "predictions": prediction[0],
+            "all_predictions": prediction[0],
+            "class_confidence": class_confidence,
+            "highest_class_confidence": class_confidence,
+        }
+
+    def _preprocess_image(self, image):
+        image = ImageOps.fit(image, self.IMAGE_SIZE, Image.Resampling.LANCZOS)
+        image_array = np.asarray(image)
+        normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
+        return np.expand_dims(normalized_image_array, axis=0)
+
+    def classify_and_show(self, image_path: str, convert_to_bgr=True):
+        """
+        Classify an image and show the prediction results on the image.
+
+        Parameters:
+        image_path (str): Path of the input image to be classified.
+        convert_to_bgr (bool, optional): Whether to convert the image to BGR format for OpenCV.
+            If False, the image will be returned in RGB format. Default is True.
+
+        Returns:
+        tuple: (classification_result, image_with_prediction)
+            classification_result (dict): Classification results including class name, index, confidence and predictions.
+            image_with_prediction (np.ndarray or PIL.Image.Image): The image with prediction results drawn on it.
+                Returns a NumPy array in BGR format if convert_to_bgr is True.
+                Otherwise, returns a PIL.Image.Image in RGB format.
+        """
+        classification_result = self.classify_image(image_path)
+        image_with_prediction = self.show_prediction_on_image(
+            image_path, classification_result, convert_to_bgr=convert_to_bgr
+        )
+        return classification_result, image_with_prediction
+
+    def show_prediction_on_image(
+        self, image_path: str, classification_result=None, convert_to_bgr=True
+    ):
+        """
+        Show the prediction results on the image and return the modified image.
+
+        Parameters:
+        image_path (str): Path of the input image to be classified.
+        classification_result (dict, optional): Pre-computed classification result.
+            If not provided, the method will classify the image.
+        convert_to_bgr (bool, optional): Whether to convert the image to BGR format for OpenCV.
+            If False, the image will be returned in RGB format. Default is True.
+
+        Returns:
+        np.ndarray or PIL.Image.Image: The image with prediction results drawn on it.
+            Returns a NumPy array in BGR format if convert_to_bgr is True.
+            Otherwise, returns a PIL.Image.Image in RGB format.
+        """
+        image = self._open_image(image_path)
+
+        if classification_result is None:
+            classification_result = self._get_image_classification(image)
+
+        class_name = classification_result["class_name"]
+        confidence = classification_result["class_confidence"]
+        confidence_percent = confidence * 100
+        text = f"{class_name}: {confidence_percent:.2f}%"
+
+        draw = ImageDraw.Draw(image)
+
+        font_size = int(image.height * 0.04)  # 4% of the image height
+        font = ImageFont.truetype("DejaVuSans-Bold.ttf", font_size)
+
+        text_width, text_height = draw.textsize(text, font=font)
+        position = (10, image.height - text_height - 10)
+
+        draw.rectangle(
+            [
+                position[0],
+                position[1],
+                position[0] + text_width,
+                position[1] + text_height,
+            ],
+            fill=(0, 0, 0, 128),
+        )
+
+        draw.text(position, text, font=font, fill=(255, 255, 255))
+
+        if convert_to_bgr:
+            image_2_numpy_arr = np.array(image)
+
+            # Convert RGB to BGR
+            image_2_numpy_arr = image_2_numpy_arr[:, :, ::-1]
+            return image_2_numpy_arr
+
+        return image</code></pre>
+              </details>
+
+
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+
+
+<div class="doc doc-object doc-function">
+
+
+<h3 id="src.teachable_machine.TeachableMachine.classify_and_show" class="doc doc-heading">
+            <code class="highlight language-python">classify_and_show(image_path, convert_to_bgr=True)</code>
+
+</h3>
+
+
+    <div class="doc doc-contents ">
+
+      <p>Classify an image and show the prediction results on the image.</p>
+<p>Parameters:
+image_path (str): Path of the input image to be classified.
+convert_to_bgr (bool, optional): Whether to convert the image to BGR format for OpenCV.
+    If False, the image will be returned in RGB format. Default is True.</p>
+      <p>tuple: (classification_result, image_with_prediction)
+    classification_result (dict): Classification results including class name, index, confidence and predictions.
+    image_with_prediction (np.ndarray or PIL.Image.Image): The image with prediction results drawn on it.
+        Returns a NumPy array in BGR format if convert_to_bgr is True.
+        Otherwise, returns a PIL.Image.Image in RGB format.</p>
+
+            <details class="quote">
+              <summary>Source code in <code>src/teachable_machine.py</code></summary>
+              <pre class="highlight"><code class="language-python">def classify_and_show(self, image_path: str, convert_to_bgr=True):
+    """
+    Classify an image and show the prediction results on the image.
+
+    Parameters:
+    image_path (str): Path of the input image to be classified.
+    convert_to_bgr (bool, optional): Whether to convert the image to BGR format for OpenCV.
+        If False, the image will be returned in RGB format. Default is True.
+
+    Returns:
+    tuple: (classification_result, image_with_prediction)
+        classification_result (dict): Classification results including class name, index, confidence and predictions.
+        image_with_prediction (np.ndarray or PIL.Image.Image): The image with prediction results drawn on it.
+            Returns a NumPy array in BGR format if convert_to_bgr is True.
+            Otherwise, returns a PIL.Image.Image in RGB format.
+    """
+    classification_result = self.classify_image(image_path)
+    image_with_prediction = self.show_prediction_on_image(
+        image_path, classification_result, convert_to_bgr=convert_to_bgr
+    )
+    return classification_result, image_with_prediction</code></pre>
+            </details>
+    </div>
+
+</div>
+
+<div class="doc doc-object doc-function">
+
+
+<h3 id="src.teachable_machine.TeachableMachine.classify_image" class="doc doc-heading">
+            <code class="highlight language-python">classify_image(image_path)</code>
+
+</h3>
+
+
+    <div class="doc doc-contents ">
+
+      <p>Classify an image using the pre-trained model.</p>
+<p>Parameters:
+image_path (str): Path of the image to be classified.</p>
+<p>Returns:
+dict: Classification results including class name, index, confidence and predictions.</p>
+
+            <details class="quote">
+              <summary>Source code in <code>src/teachable_machine.py</code></summary>
+              <pre class="highlight"><code class="language-python">def classify_image(self, image_path: str):
+    """
+    Classify an image using the pre-trained model.
+
+    Parameters:
+    image_path (str): Path of the image to be classified.
+
+    Returns:
+    dict: Classification results including class name, index, confidence and predictions.
+    """
+    image = self._open_image(image_path)
+    return self._get_image_classification(image)</code></pre>
+            </details>
+    </div>
+
+</div>
+
+<div class="doc doc-object doc-function">
+
+
+<h3 id="src.teachable_machine.TeachableMachine.show_prediction_on_image" class="doc doc-heading">
+            <code class="highlight language-python">show_prediction_on_image(image_path, classification_result=None, convert_to_bgr=True)</code>
+
+</h3>
+
+
+    <div class="doc doc-contents ">
+
+      <p>Show the prediction results on the image and return the modified image.</p>
+<p>Parameters:
+image_path (str): Path of the input image to be classified.
+classification_result (dict, optional): Pre-computed classification result.
+    If not provided, the method will classify the image.
+convert_to_bgr (bool, optional): Whether to convert the image to BGR format for OpenCV.
+    If False, the image will be returned in RGB format. Default is True.</p>
+      <p>np.ndarray or PIL.Image.Image: The image with prediction results drawn on it.
+    Returns a NumPy array in BGR format if convert_to_bgr is True.
+    Otherwise, returns a PIL.Image.Image in RGB format.</p>
+
+            <details class="quote">
+              <summary>Source code in <code>src/teachable_machine.py</code></summary>
+              <pre class="highlight"><code class="language-python">def show_prediction_on_image(
+    self, image_path: str, classification_result=None, convert_to_bgr=True
+):
+    """
+    Show the prediction results on the image and return the modified image.
+
+    Parameters:
+    image_path (str): Path of the input image to be classified.
+    classification_result (dict, optional): Pre-computed classification result.
+        If not provided, the method will classify the image.
+    convert_to_bgr (bool, optional): Whether to convert the image to BGR format for OpenCV.
+        If False, the image will be returned in RGB format. Default is True.
+
+    Returns:
+    np.ndarray or PIL.Image.Image: The image with prediction results drawn on it.
+        Returns a NumPy array in BGR format if convert_to_bgr is True.
+        Otherwise, returns a PIL.Image.Image in RGB format.
+    """
+    image = self._open_image(image_path)
+
+    if classification_result is None:
+        classification_result = self._get_image_classification(image)
+
+    class_name = classification_result["class_name"]
+    confidence = classification_result["class_confidence"]
+    confidence_percent = confidence * 100
+    text = f"{class_name}: {confidence_percent:.2f}%"
+
+    draw = ImageDraw.Draw(image)
+
+    font_size = int(image.height * 0.04)  # 4% of the image height
+    font = ImageFont.truetype("DejaVuSans-Bold.ttf", font_size)
+
+    text_width, text_height = draw.textsize(text, font=font)
+    position = (10, image.height - text_height - 10)
+
+    draw.rectangle(
+        [
+            position[0],
+            position[1],
+            position[0] + text_width,
+            position[1] + text_height,
+        ],
+        fill=(0, 0, 0, 128),
+    )
+
+    draw.text(position, text, font=font, fill=(255, 255, 255))
+
+    if convert_to_bgr:
+        image_2_numpy_arr = np.array(image)
+
+        # Convert RGB to BGR
+        image_2_numpy_arr = image_2_numpy_arr[:, :, ::-1]
+        return image_2_numpy_arr
+
+    return image</code></pre>
+            </details>
+    </div>
+
+</div>
+
+
+
+  </div>
+
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+              
+                <h1 id="how-to-guide">How-To Guide</h1>
+<h2 id="how-to-install-the-package">How to install the package</h2>
+<pre><code class="language-bash">pip install teachable-machine
+</code></pre>
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+            <div class="section" itemprop="articleBody">
+              
+                <h1 id="welcome-to-teachable-machine-package">Welcome to Teachable Machine Package</h1>
+<p><em>By: <a href="https://github.com/MeqdadDev">Meqdad Darwish</a></em></p>
+<p align="center">
+<picture>
+  <img alt="Teachable Machine Package Logo" src="logo.png" width="80%" height="80%" >
+</picture>
+</p>
+
+<p><a href="https://pepy.tech/project/teachable-machine"><img alt="Downloads" src="https://static.pepy.tech/badge/teachable-machine" /></a>
+<a href="https://choosealicense.com/licenses/mit/"><img alt="MIT License" src="https://img.shields.io/badge/License-MIT-green.svg" /></a>
+<a href="https://pypi.org/project/teachable-machine/"><img alt="PyPI" src="https://img.shields.io/pypi/v/teachable-machine" /></a></p>
+<h2 id="description">Description</h2>
+<p>A Python package designed to simplify the integration of exported models from Google's <a href="https://teachablemachine.withgoogle.com/">Teachable Machine</a> platform into various environments.
+This tool was specifically crafted to work seamlessly with Teachable Machine, making it easier to implement and use your trained models.</p>
+<p>Source Code is published on <a href="https://github.com/MeqdadDev/teachable-machine">GitHub</a></p>
+<h2 id="table-of-contents">Table Of Contents</h2>
+<ol>
+<li><a href="how-to-guide/">How-To Guide</a></li>
+<li><a href="requirements/">Requirements</a></li>
+<li><a href="codeExamples/">Code Examples</a></li>
+<li><a href="explanation/">Explanation</a></li>
+<li><a href="contribution/">Contributing</a></li>
+</ol>
+<h2 id="supported-classifiers">Supported Classifiers</h2>
+<p><strong>Image Classification</strong></p>
+<h2 id="links">Links</h2>
+<ul>
+<li>
+<p><a href="https://pypi.org/project/teachable-machine/">PyPI</a></p>
+</li>
+<li>
+<p><a href="https://github.com/MeqdadDev/teachable-machine">Source Code</a></p>
+</li>
+<li>
+<p><a href="https://teachablemachine.withgoogle.com/">Teachable Machine Platform</a></p>
+</li>
+</ul>
+              
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new file mode 100644
index 0000000..533e634
--- /dev/null
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+++ b/site/js/theme_extra.js
@@ -0,0 +1,8 @@
+/*
+ * Assign 'docutils' class to tables so styling and
+ * JavaScript behavior is applied.
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+ * https://github.com/mkdocs/mkdocs/issues/2028
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+$('div.rst-content table').addClass('docutils');
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diff --git a/site/requirements/index.html b/site/requirements/index.html
new file mode 100644
index 0000000..7867a47
--- /dev/null
+++ b/site/requirements/index.html
@@ -0,0 +1,146 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
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+                </li>
+              </ul>
+              <ul class="current">
+                <li class="toctree-l1 current"><a class="reference internal current" href="#">Requirements</a>
+    <ul class="current">
+    <li class="toctree-l2"><a class="reference internal" href="#python-version">Python Version</a>
+    </li>
+    <li class="toctree-l2"><a class="reference internal" href="#dependencies">Dependencies</a>
+    </li>
+    </ul>
+                </li>
+              </ul>
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="../codeExamples/">Code Examples</a>
+                </li>
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+                </li>
+              </ul>
+              <ul>
+                <li class="toctree-l1"><a class="reference internal" href="../contribution/">Contributing</a>
+                </li>
+              </ul>
+      </div>
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+
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+            <div class="section" itemprop="articleBody">
+              
+                <h2 id="requirements">Requirements</h2>
+<h3 id="python-version">Python Version</h3>
+<p><code>Python &gt;= 3.7</code></p>
+<h3 id="dependencies">Dependencies</h3>
+<pre><code class="language-bash">numpy
+Pillow
+tensorflow
+</code></pre>
+              
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diff --git a/site/sitemap.xml b/site/sitemap.xml
new file mode 100644
index 0000000..0f8724e
--- /dev/null
+++ b/site/sitemap.xml
@@ -0,0 +1,3 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
+</urlset>
\ No newline at end of file
diff --git a/site/sitemap.xml.gz b/site/sitemap.xml.gz
new file mode 100644
index 0000000..42476f2
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diff --git a/src/__init__.py b/src/__init__.py
index 6e2b6c8..bde846c 100644
--- a/src/__init__.py
+++ b/src/__init__.py
@@ -1,24 +1,24 @@
-from teachable_machine import TeachableMachine
+# from teachable_machine import TeachableMachine
 
 
-__author__ = 'MeqdadDev'
-__version__ = '1.1'
+__author__ = "MeqdadDev"
+__version__ = "1.2"
 
 __doc__ = """
 Teachable Machine
 ================
 Description
 -----------
-A Python package to simplify the deployment process of exported Teachable Machine models into different environments.
-Teahchable Machine is a tool that was built for Teachable Machine Platform from Google.
-Teahchable Machine developed by Eng. Meqdad Darwish.
+A Python package designed to simplify the integration of exported models from Google's Teachable Machine platform into various environments.
+This tool was specifically crafted to work seamlessly with Teachable Machine, making it easier to implement and use your trained models.
+
+Developed by: Meqdad Darwish
 
 Source Code is published on GitHub:
 https://github.com/MeqdadDev/teachable-machine
 
 Example
 -------
->>> from detectors_world import DetectorCreator
 >>> from teachable_machine import TeachableMachine
 >>> import cv2 as cv
 
diff --git a/src/teachable_machine.py b/src/teachable_machine.py
index cefd3e8..efa43bf 100644
--- a/src/teachable_machine.py
+++ b/src/teachable_machine.py
@@ -1,97 +1,186 @@
 from tensorflow.keras.models import load_model
-from PIL import Image, ImageOps
+from PIL import Image, ImageOps, ImageDraw, ImageFont
 import numpy as np
 
 
 class TeachableMachine(object):
-    '''
-    Create your TeachableMachine object to run your trained AI models.
-    '''
+    """
+    Create a TeachableMachine object to run pre-trained AI models.
+    """
 
-    def __init__(self, model_path='keras_model.h5', labels_file_path='labels.txt', model_type='h5') -> None:
-        self._model_type = model_type.lower()
-        self._labels_file_path = labels_file_path
-        self._supported_types = ('keras', 'Keras', 'h5', 'h5py')
+    SUPPORTED_TYPES = {"keras", "h5"}
+    IMAGE_SIZE = (224, 224)
 
+    def __init__(
+        self,
+        model_path="keras_model.h5",
+        labels_file_path="labels.txt",
+        model_type="h5",
+    ) -> None:
+        self._model_type = model_type.lower()
+        if self._model_type not in self.SUPPORTED_TYPES:
+            raise ValueError(
+                f"Unsupported model type: {self._model_type}. Use 'keras' or 'h5'."
+            )
         np.set_printoptions(suppress=True)
+
+        self._load_model(model_path)
+        self._load_labels(labels_file_path)
+        print("Teachable Machine Object is created successfully.")
+
+    def _load_model(self, model_path: str):
         try:
             self._model = load_model(model_path, compile=False)
         except IOError as e:
-            print('LoadingModelError: Error while loading Teachable Machine model')
-            raise IOError from e
-        except:
             print("LoadingModelError: Error while loading Teachable Machine model")
-            raise FileNotFoundError
+            raise IOError("Error loading model") from e
+        except Exception as e:
+            print("LoadingModelError: Error while loading Teachable Machine model")
+            raise FileNotFoundError("Model file not found") from e
+
+    def _load_labels(self, labels_file_path):
         try:
-            self._labels_file = open(self._labels_file_path, "r").readlines()
+            with open(labels_file_path, "r") as file:
+                self._labels = file.readlines()
         except IOError as e:
-            print('LoadingLabelsError: Error while loading labels.txt file')
-            raise IOError from e
-        except:
             print("LoadingLabelsError: Error while loading labels.txt file")
-            raise FileNotFoundError
-
-        self._object_creation_status = self._model_type in self._supported_types
-        if self._object_creation_status:
-            print('Teachable Machine Object is created successfully.')
-        else:
-            raise 'NotSupportedType: Your model type is not supported, try to use types such as "keras" or "h5".'
-
-    def classify_image(self, frame_path: str):
-        '''To deploy your Teachable Machine Model on a computer/PC
-            with .h5 extension using TensorFlow.
-
-            Parameters:
-            * (str) frame_path: Provide path of the image to be classified.
-
-            Returns:
-            * class_name: Name of the highest predicted class according to labels.txt file
-            * class_index: Index or ID of the highest predicted class according to labels.txt file
-            * predictions: All prediction values for all classes.
-        '''
+            raise IOError("Error loading labels") from e
+        except Exception as e:
+            print("LoadingLabelsError: Error while loading labels.txt file")
+            raise FileNotFoundError("Labels file not found") from e
+
+    def _open_image(self, image_path):
+        """
+        Open an image file and convert it to RGB mode.
+
+        Parameters:
+        image_path (str): Path to the image file.
+
+        Returns:
+        PIL.Image.Image: Opened image in RGB mode.
+        """
         try:
-            frame = Image.open(frame_path)
-            if frame.mode != "RGB":
-                frame = frame.convert("RGB")
+            return Image.open(image_path).convert("RGB")
         except FileNotFoundError as e:
             print("ImageNotFound: Error in image file.")
-            raise FileNotFoundError from e
-        except TypeError as e:
-            print(
-                "ImageTypeError: Error while converting image to RGB format, image type is not supported")
-        try:
-            if self._object_creation_status:
-                return self._get_image_classification(frame)
-        except BaseException as e:
-            print('Error in classification process, retrain your model.')
-            raise e
+            raise FileNotFoundError("Image file not found") from e
+        except Exception as e:
+            print("ImageTypeError: Error while opening or converting image")
+            raise TypeError("Unsupported image type") from e
+
+    def classify_image(self, image_path: str):
+        """
+        Classify an image using the pre-trained model.
+
+        Parameters:
+        image_path (str): Path of the image to be classified.
+
+        Returns:
+        dict: Classification results including class name, index, confidence and predictions.
+        """
+        image = self._open_image(image_path)
+        return self._get_image_classification(image)
 
     def _get_image_classification(self, image):
-        data = self._form_image(image)
+        data = self._preprocess_image(image)
         prediction = self._model.predict(data)
         class_index = np.argmax(prediction)
-        class_name = self._labels_file[class_index]
+        class_name = self._labels[class_index].strip()
         class_confidence = prediction[0][class_index]
 
         return {
-            "class_name": class_name[2:],
-            "highest_class_name": class_name[2:],
+            "class_name": class_name,
+            "highest_class_name": class_name,
             "highest_class_id": class_index,
             "class_index": class_index,
             "class_id": class_index,
-            "predictions": prediction,
-            "all_predictions": prediction,
+            "predictions": prediction[0],
+            "all_predictions": prediction[0],
             "class_confidence": class_confidence,
             "highest_class_confidence": class_confidence,
         }
 
-    def _form_image(self, image):
-        image_data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
-        crop_size = (224, 224)
-        image = ImageOps.fit(image, crop_size, Image.Resampling.LANCZOS)
+    def _preprocess_image(self, image):
+        image = ImageOps.fit(image, self.IMAGE_SIZE, Image.Resampling.LANCZOS)
         image_array = np.asarray(image)
+        normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
+        return np.expand_dims(normalized_image_array, axis=0)
+
+    def classify_and_show(self, image_path: str, convert_to_bgr=True):
+        """
+        Classify an image and show the prediction results on the image.
+
+        Parameters:
+        image_path (str): Path of the input image to be classified.
+        convert_to_bgr (bool, optional): Whether to convert the image to BGR format for OpenCV.
+            If False, the image will be returned in RGB format. Default is True.
+
+        Returns:
+        tuple: (classification_result, image_with_prediction)
+            classification_result (dict): Classification results including class name, index, confidence and predictions.
+            image_with_prediction (np.ndarray or PIL.Image.Image): The image with prediction results drawn on it.
+                Returns a NumPy array in BGR format if convert_to_bgr is True.
+                Otherwise, returns a PIL.Image.Image in RGB format.
+        """
+        classification_result = self.classify_image(image_path)
+        image_with_prediction = self.show_prediction_on_image(
+            image_path, classification_result, convert_to_bgr=convert_to_bgr
+        )
+        return classification_result, image_with_prediction
+
+    def show_prediction_on_image(
+        self, image_path: str, classification_result=None, convert_to_bgr=True
+    ):
+        """
+        Show the prediction results on the image and return the modified image.
+
+        Parameters:
+        image_path (str): Path of the input image to be classified.
+        classification_result (dict, optional): Pre-computed classification result.
+            If not provided, the method will classify the image.
+        convert_to_bgr (bool, optional): Whether to convert the image to BGR format for OpenCV.
+            If False, the image will be returned in RGB format. Default is True.
+
+        Returns:
+        np.ndarray or PIL.Image.Image: The image with prediction results drawn on it.
+            Returns a NumPy array in BGR format if convert_to_bgr is True.
+            Otherwise, returns a PIL.Image.Image in RGB format.
+        """
+        image = self._open_image(image_path)
+
+        if classification_result is None:
+            classification_result = self._get_image_classification(image)
+
+        class_name = classification_result["class_name"]
+        confidence = classification_result["class_confidence"]
+        confidence_percent = confidence * 100
+        text = f"{class_name}: {confidence_percent:.2f}%"
+
+        draw = ImageDraw.Draw(image)
+
+        font_size = int(image.height * 0.04)  # 4% of the image height
+        font = ImageFont.truetype("DejaVuSans-Bold.ttf", font_size)
+
+        text_width, text_height = draw.textsize(text, font=font)
+        position = (10, image.height - text_height - 10)
+
+        draw.rectangle(
+            [
+                position[0],
+                position[1],
+                position[0] + text_width,
+                position[1] + text_height,
+            ],
+            fill=(0, 0, 0, 128),
+        )
+
+        draw.text(position, text, font=font, fill=(255, 255, 255))
+
+        if convert_to_bgr:
+            image_2_numpy_arr = np.array(image)
 
-        normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+            # Convert RGB to BGR
+            image_2_numpy_arr = image_2_numpy_arr[:, :, ::-1]
+            return image_2_numpy_arr
 
-        image_data[0] = normalized_image_array
-        return image_data
+        return image


Test patch:
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..14834c1
--- /dev/null
+++ b/tests/__init__.py
@@ -0,0 +1,5 @@
+import sys
+from pathlib import Path
+
+project_root = Path(__file__).parent.parent
+sys.path.insert(0, str(project_root))
\ No newline at end of file
diff --git a/tests/test_teachable_machine.py b/tests/test_teachable_machine.py
new file mode 100644
index 0000000..8bbd585
--- /dev/null
+++ b/tests/test_teachable_machine.py
@@ -0,0 +1,145 @@
+import pytest
+from src.teachable_machine import TeachableMachine
+from PIL import Image
+import numpy as np
+
+
+@pytest.fixture
+def teachable_machine(mocker):
+    # Create a TeachableMachine instance
+    mocker.patch("src.teachable_machine.load_model", return_value="mock_model")
+
+    # Mock the open function to simulate reading from the labels.txt file with the specified content
+    mocker.patch("builtins.open", mocker.mock_open(read_data="0 Class A\n1 Class B\n"))
+
+    # Initialize TeachableMachine
+    return TeachableMachine(
+        model_path="dummy_model_path.h5", labels_file_path="dummy_labels.txt"
+    )
+
+
+def test_teachable_machine_initialization(mocker):
+    """
+    Test the initialization of the TeachableMachine class and loading of the model and labels.
+    """
+    # Mock the load_model and _load_labels methods to avoid actual file operations
+    mocker.patch("src.teachable_machine.load_model", return_value="mock_model")
+
+    # Mock the open function to simulate reading from the labels.txt file with the specified content
+    mocker.patch("builtins.open", mocker.mock_open(read_data="0 Class A\n1 Class B\n"))
+
+    # Initialize TeachableMachine
+    tm = TeachableMachine(
+        model_path="dummy_model_path.h5", labels_file_path="dummy_labels.txt"
+    )
+
+    # Check if the model is loaded correctly
+    assert tm._model == "mock_model"
+
+    # Check if labels are loaded and formatted correctly
+    assert tm._labels == ["0 Class A\n", "1 Class B\n"]
+
+    # Check if the model type is correctly set
+    assert tm._model_type == "h5"
+
+    # Test that the initialization does not raise any exceptions
+    assert isinstance(tm, TeachableMachine)
+
+
+def test_open_image(mocker):
+    """
+    Test the _open_image method of the TeachableMachine class.
+    """
+    # Create a TeachableMachine instance
+    mocker.patch("src.teachable_machine.load_model", return_value="mock_model")
+
+    # Mock the open function to simulate reading from the labels.txt file with the specified content
+    mocker.patch("builtins.open", mocker.mock_open(read_data="0 Class A\n1 Class B\n"))
+
+    # Initialize TeachableMachine
+    tm = TeachableMachine(
+        model_path="dummy_model_path.h5", labels_file_path="dummy_labels.txt"
+    )
+
+    # Mock PIL.Image.open and the convert method
+    mock_image = mocker.Mock(spec=Image.Image)
+    mock_image.convert.return_value = mock_image
+    mock_open = mocker.patch("PIL.Image.open", return_value=mock_image)
+
+    # Test successful image opening
+    result = tm._open_image("dummy_image.jpg")
+    assert result == mock_image
+    mock_open.assert_called_once_with("dummy_image.jpg")
+    mock_image.convert.assert_called_once_with("RGB")
+
+    # Test FileNotFoundError
+    mock_open.side_effect = FileNotFoundError
+    with pytest.raises(FileNotFoundError, match="Image file not found"):
+        tm._open_image("non_existent_image.jpg")
+
+    # Test other exceptions (simulating unsupported image type)
+    mock_open.side_effect = Exception
+    with pytest.raises(TypeError, match="Unsupported image type"):
+        tm._open_image("invalid_image.txt")
+
+    # Test that error messages are printed
+    mock_print = mocker.patch("builtins.print")
+
+    mock_open.side_effect = FileNotFoundError
+    with pytest.raises(FileNotFoundError):
+        tm._open_image("non_existent_image.jpg")
+    mock_print.assert_called_with("ImageNotFound: Error in image file.")
+
+    mock_open.side_effect = Exception
+    with pytest.raises(TypeError):
+        tm._open_image("invalid_image.txt")
+    mock_print.assert_called_with(
+        "ImageTypeError: Error while opening or converting image"
+    )
+
+
+def test_preprocess_image_output_shape(teachable_machine):
+    # Create a sample image
+    sample_image = Image.new("RGB", (300, 200))
+
+    # Process the image
+    processed = teachable_machine._preprocess_image(sample_image)
+
+    # Check the output shape
+    assert processed.shape == (1, 224, 224, 3)
+
+
+def test_preprocess_image_normalization(teachable_machine):
+    # Create a sample image with known values
+    sample_array = np.full((300, 200, 3), 127, dtype=np.uint8)
+    sample_image = Image.fromarray(sample_array)
+
+    # Process the image
+    processed = teachable_machine._preprocess_image(sample_image)
+
+    # Check if the values are normalized correctly (close to -0.00392, not exactly 0)
+    expected_value = (127 / 127.5) - 1
+    assert np.allclose(processed, expected_value, atol=1e-6)
+
+
+def test_preprocess_image_different_sizes(teachable_machine):
+    # Test with different image sizes
+    sizes = [(100, 100), (500, 300), (224, 224)]
+
+    for size in sizes:
+        sample_image = Image.new("RGB", size)
+        processed = teachable_machine._preprocess_image(sample_image)
+        assert processed.shape == (1, 224, 224, 3)
+
+
+def test_preprocess_image_content(teachable_machine):
+    # Create a sample image with a specific pattern
+    sample_array = np.zeros((300, 200, 3), dtype=np.uint8)
+    sample_array[:100, :100] = 255  # White square in top-left corner
+    sample_image = Image.fromarray(sample_array)
+
+    # Process the image
+    processed = teachable_machine._preprocess_image(sample_image)
+
+    # Check if the white square is still in the top-left corner (approximately)
+    assert np.mean(processed[0, :50, :50]) > np.mean(processed[0, 50:, 50:])