How To Use Gemini Pro On Google AI Studio?

Gemini Pro, Google’s powerful conversational AI model, is now accessible within Google AI Studio. This opens up a world of possibilities for integrating AI-powered interactions into your data visualizations and dashboards. Here you will find a comprehensive guide to help you get started.

How to use Gemini Pro on Google AI Studio?

1. Setup:

1. Create a project: Go to https://cloud.google.com/generative-ai-studio and click ‘Create project’. Enter a name and select your preferred region.
   • Go to: https://cloud.google.com/generative-ai-studio
   • Click ‘Create Dataset’ and choose ‘Empty Dataset’.
   • Give your dataset a name (e.g. Gemini Project).
   • Click ‘Connect’ and select ‘Google Cloud’ to authenticate.
   • Navigate to ‘Create Credentials’ and select ‘Create Credentials’.
2. Generate API key: In the project, click ‘Settings’ and choose ‘API keys’. Click ‘Create API Key’ and select ‘Google AI Studio’. Copy the generated key.
3. Enable Gemini Pro: Go to the ‘Services’ tab and click on ‘Available services’. Scroll down and find ‘Gemini Pro’. Click “Enable” and select your preferred region.

2. Build Your Power:

1. Click on ‘Create Flow’ within your dataset.
2. Drag and drop a “Python Notebook” component into the workspace.
3. Click “Edit Notebook” to open the coding environment.
4. Import libraries: In a new cell, install the necessary libraries:

Python

from google.cloud import aiplatform
from google.cloud import dialogflow_v2beta1 as dialogflow

# Get API key and endpoint from environment variables
API_KEY = os.environ['GEMINI_API_KEY']
ENDPOINT = os.environ['GEMINI_ENDPOINT']

Use code with caution.

• Authenticate and connect: Set environment variables for your API key and endpoint:

Python

%setenv GEMINI_API_KEY your_api_key
%setenv GEMINI_ENDPOINT your_endpoint

Use code with caution.

• To connect to Gemini Pro: Create a client object:

Python

client = aiplatform.gapic.dialogflow_v2beta1.SessionsClient()
session_path = client.session_path(project=project_id, session='my-session')

Use code with caution.

3. Build your conversation:

• Send first prompt: Start the conversation with Gemini Pro by sending an initial prompt:

Python

text_input = dialogflow.TextInput(text="Hello, Gemini Pro!")
response = client.detect_intent(session_path, query=text_input)
print(response.query_result.text)

Use code with caution.

• Processing and responding: Walk through Gemini Pro’s responses and build your interaction:

Python

while True:
  # Get user input
  user_input = input()

  # Send user input to Gemini Pro
  text_input = dialogflow.TextInput(text=user_input)
  response = client.detect_intent(session_path, query=text_input)

  # Print and handle Gemini Pro's response
  print(response.query_result.text)
  # ... your custom logic here based on response content

  # Check for ending conditions
  if response.query_result.fulfillment_text == "Goodbye!":
    break

Use code with caution.

4. Integration with data and visualizations:

• Passing data on to Gemini Pro: You can pass data from your AI Studio charts and tables to Gemini Pro for personalized answers. Use libraries like pandas to access and format data before sending it as context:

Python

# Access data from your chart or table
data = ...  # your data extraction logic

# Format data for Gemini Pro context
context = dialogflow.Context(parameters="data": data)

# Send text input with context
text_input = dialogflow.TextInput(text="What can you tell me about this data?", context=context)
response = client.detect_intent(session_path, query=text_input)

Use code with caution.

• To update visualizations based on responses: Use Gemini Pro’s responses to dynamically update your charts and dashboards. Libraries such as plotly and bokeh can be used to manipulate visualizations based on the output.

To remind: This is a basic guide and you can customize your interactions with Gemini Pro based on your specific needs and data. Don’t hesitate to experiment and explore the possibilities!

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