Fabric instructions update

docs/en/licensed_install.md

@@ -1588,43 +1588,65 @@ Navigate to [MS Fabric](https://app.fabric.microsoft.com/) and sign in with your
 
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-### Step 2: Create a Lakehouse
-- Go to the **Data Science** section.
-- Navigate to the **Create** section.
+### Step 2: Create a new workspace
+- Go to the **Home** section.
+- Navigate to the **New workspace** button.
 - Create a new lakehouse, (for instance let us name it `jsl_workspace`.)
 
-![Create a Lakehouse](/assets/images/installation/Fabric_1.png)
+![Create a Lakehouse](/assets/images/installation/FabricWorkspace1.png)
 
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-### Step 3: Create a Notebook
+### Step 3: Create a new Lakehouse
+- Go to the new created workspace.
+- Navigate to the **New item** button.
+- Create a new lakehouse, (for instance let us name it `JSL_Lakehouse`.)
+
+![Create a Lakehouse](/assets/images/installation/FabricLakehouse1.png)
+
+</div><div class="h3-box" markdown="1">
+
+### Step 4: Create a Notebook
 - Similarly, create a new notebook ( for instance let us name it `JSL_Notebook`.)
 
-![Create a Notebook in Fabric](/assets/images/installation/Fabric_2.png)
+![Create a Notebook in Fabric](/assets/images/installation/FabricNotebook1.png)
 
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-### Step 4: Attach the Lakehouse
+### Step 5: Attach the Lakehouse
 Attach the newly created lakehouse (`jsl_workspace`) to your notebook.
 
-![Attach the Lakehouse](/assets/images/installation/355921285-63996c40-4cd6-4aa2-925f-a1ad886914f4.webp)
+![Attach the Lakehouse](/assets/images/installation/FabricNBattach1.png)
+
+![Attach the Lakehouse](/assets/images/installation/FabricNBattach2.png)
 
-![Attach the Lakehouse](/assets/images/installation/355921392-b711eef6-55ed-4073-b974-14b565cd40be.webp)
+![Attach the Lakehouse](/assets/images/installation/FabricNBattach3.png)
 
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-### Step 5: Upload Files
+### Step 6: Upload Files
 Upload the necessary `.jar` and `.whl` files to the attached lakehouse.
+(Please reach out to [email protected] for the compatible jar and whl files)
+
+![Upload Files to Fabric](/assets/images/installation/FabricUploadFiles1.png)
+
+![Upload Files to Fabric](/assets/images/installation/FabricUploadFiles2.png)
+
+
+</div><div class="h3-box" markdown="1">
+
+### Step 7: Create the unzip_files
+
+Create a folder in the Lakehouse to define the cache_folder.
 
-![Upload Files to Fabric](/assets/images/installation/355921637-a275d80d-768f-4402-bdab-d95864e73690.webp)
+![Upload Files to Fabric](/assets/images/installation/FabricCacheFolder1.png)
 
-![Upload Files to Fabric](/assets/images/installation/Fabric_3.png)
+![Upload Files to Fabric](/assets/images/installation/FabricCacheFolder2.png)
 
-After uploading is complete, you can configure and run the notebook.
 
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-### Step 6: Configure the Notebook Session
+### Step 8: Configure the Notebook Session
 Configure the session within the notebook as follows:
 
 ```json
@@ -1646,7 +1668,7 @@ Configure the session within the notebook as follows:
     },
     "spark.jars": {
       "parameterName": "sparkJars",
-      "defaultValue": "abfss://&&&&&&/Files/spark-nlp-assembly-5.5.0.jar, abfss://&&&&&&/Files/spark-nlp-jsl-5.5.0.jar"
+      "defaultValue": "abfss://&&&&&&/Files/spark-nlp-assembly-5.5.2.jar, abfss://&&&&&&/Files/spark-nlp-jsl-5.5.2.jar"
     },
     "spark.jsl.settings.pretrained.cache_folder": {
       "parameterName": "cacheFolder",
@@ -1663,7 +1685,7 @@ Configure the session within the notebook as follows:
 
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-### Step 7: Install Spark NLP Libraries
+### Step 9: Install Johnsowlabs, Spark NLP and Healthcare NLP
 
 Install the required Spark NLP libraries using pip commands:
 ```bash
@@ -1674,11 +1696,11 @@ Install the required Spark NLP libraries using pip commands:
 
 **Example Usage:**
 
-![Install Spark NLP Libraries](/assets/images/installation/355919623-a4db5456-78a5-4d32-a50e-c40650b878f2.webp)
+![Upload Files to Fabric](/assets/images/installation/FabricWHL1.png)
 
 </div><div class="h3-box" markdown="1">
 
-### Step 8: Make Necessary Imports
+### Step 10: Make Necessary Imports
 Import the necessary Python and Spark libraries:
 ```python
 import functools
@@ -1769,4 +1791,4 @@ You can also run the pipelines without using the `.load()` or `.from_disk()` met
 
 ![Run the pipeline with `.pretrained()` method](/assets/images/installation/Fabric_4.png)
 ![Run the pipeline with `.pretrained()` method](/assets/images/installation/Fabric_5.png)
-</div>
+</div>