Merge pull request #21 from NIGMS/jghanaim04-patch-1

Bedrock

Author: kyleoconnell-NIH

AWS/README.md

@@ -64,4 +64,19 @@ These notebooks were designed to be run using a virtual machine on a cloud compu
 
 **[Bonus](Tutorial_5_BonusNotebook.ipynb):** Test your knowledge by filling in the blanks for key Cloud and bioinformatic tasks learned in the other submodules.
 
+## AWS Bedrock (Optional)
 
+Generative AI is available for this tutorial if you would like to use it. To run it, please reference Tutorial 1, or run the following code within a submodule notebook.
+
+```!pip install -q ipywidgets
+import sys
+import os
+util_path = os.path.join(os.getcwd(), 'util')
+if util_path not in sys.path:
+    sys.path.append(util_path)
+
+# Import the display_widgets function from your Python file
+from genai import display_widgets
+
+# Call the function to display the widgets
+display_widgets()

AWS/Tutorial_1.ipynb

@@ -34,7 +34,12 @@
     "*   **Assess read quality with FastQC:** Understand how to use FastQC to evaluate the quality of reads and identify potential biases or issues.\n",
     "*   **Generate a consolidated QC report with MultiQC:** Learn to use MultiQC to combine FastQC results and generate an overview of quality metrics across multiple samples.\n",
     "*   **Index a transcriptome with Salmon:** Understand the purpose of indexing and learn how to use Salmon to create an index of the reference transcriptome for efficient read mapping.\n",
-    "*   **Map reads to a transcriptome and quantify expression with Salmon:** Learn how to use Salmon to map reads to transcripts and quantify gene expression levels."
+    "*   **Map reads to a transcriptome and quantify expression with Salmon:** Learn how to use Salmon to map reads to transcripts and quantify gene expression levels.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-success\">\n",
+    "    <i class=\"fa fa-hand-paper-o\" aria-hidden=\"true\"></i>\n",
+    "    <b>Tip: </b>  If you're having trouble with any part of this tutorial, feel free to leverage AWS Bedrock (Amazon's advanced generative AI tool) at the bottom of this module.\n",
+    "</div>  "
    ]
   },
   {
@@ -415,11 +420,58 @@
     "\n",
     "Remember to move to the next notebook or shut down your instance if you are finished."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bedrock (Optional)\n",
+    "--------\n",
+    "\n",
+    "If you're having trouble with this submodule (or others within this tutorial), feel free to leverage Bedrock by running the cell below. AWS Bedrock is a fully managed service that simplifies building and scaling generative AI applications. It provides access to various foundation models (FMs) from Amazon and other AI companies.\n",
+    "\n",
+    "Before being able to use the chatbot you must request **Llama 3 8B Instruct** model access through AWS Bedrock. In order to do this follow the instructions to request model access provided in [AWS Bedrock Intro Notebook](https://github.com/STRIDES/NIHCloudLabAWS/blob/main/notebooks/GenAI/AWS_Bedrock_Intro.ipynb). After requesting the Llama 3 8B Instruct access it should only take a minute to get approved. While waiting for model approval attach the **AmazonBedrockFullAccess** permission to your notebook service role. Once approved run the following code cell to use the model within the notebook. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ensure you have the necessary libraries installed\n",
+    "!pip install -q ipywidgets\n",
+    "import sys\n",
+    "import os\n",
+    "util_path = os.path.join(os.getcwd(), 'util')\n",
+    "if util_path not in sys.path:\n",
+    "    sys.path.append(util_path)\n",
+    "\n",
+    "# Import the display_widgets function from your Python file\n",
+    "from genai import display_widgets\n",
+    "\n",
+    "# Call the function to display the widgets\n",
+    "display_widgets()"
+   ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "conda_python3",
+   "language": "python",
+   "name": "conda_python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.17"
   }
  },
  "nbformat": 4,

AWS/util/genai.py

@@ -0,0 +1,56 @@
+import boto3
+import json
+import ipywidgets as widgets
+from IPython.display import display
+
+# Setup Model
+my_session = boto3.session.Session()
+my_region = my_session.region_name
+bedrock = boto3.client(
+    service_name='bedrock-runtime',
+    region_name=my_region,
+    )
+def genai(prompt):
+    # Define model ID
+    model_id = "meta.llama3-8b-instruct-v1:0"
+
+    # Create request body
+    body = json.dumps({
+        "prompt": prompt,
+    })
+
+    # Send request to Bedrock
+    response = bedrock.invoke_model(
+        modelId=model_id,
+        body=body
+    )
+
+    # Process the response
+    response_body = json.loads(response['body'].read())
+    generated_text = response_body['generation']
+    generated_text = generated_text.lstrip('?').strip()
+    return generated_text
+
+# Create widgets
+prompt_input = widgets.Text(description="Prompt:")
+submit_button = widgets.Button(description="Submit")
+output_area = widgets.Output()
+
+# Define button click event
+def on_button_click(b):
+    with output_area:
+        output_area.clear_output()
+        print("Generating response...")
+        response = genai(prompt_input.value)
+        print("Response:")
+        print(response)
+
+# Attach the event to the button
+submit_button.on_click(on_button_click)
+
+def display_widgets():
+    """
+    Function to display the widgets in the notebook.
+    Call this function to show the interactive prompt interface.
+    """
+    display(prompt_input, submit_button, output_area)