Building Multimodal AI Application with Gemini 2.0 Pro

Google's Gemini 2.0 Pro: A Deep Dive into Multimodal AI Capabilities and Deployment

Google has unveiled Gemini 2.0 Pro, its most advanced AI model yet. Currently in experimental stages, access is via API for developers. This powerful model shines in coding and complex reasoning, boasting a massive 2 million token context window for handling extensive information. Its ability to leverage Google Search and execute code adds to its versatility.

This tutorial demonstrates how to access Gemini 2.0 Pro's features using Google's GenAI Python package, building a user-friendly Gradio application, and deploying it to Hugging Face Spaces for public access. For comparative analysis against OpenAI and DeepSeek models, see our guide on Gemini 2.0 Flash Thinking Experimental. Adel Nehme's tutorial offers further insights into building multimodal apps with Gemini 2.0:

Setting Up Gemini 2.0 Pro

Access to Gemini 2.0 Pro is exclusively through Google AI Studio, requiring a Google account.

1. Google AI Studio Login: Access the Google AI Studio website and log in.

2. API Key Generation: Navigate to the dashboard, locate, and click "Get API Key," followed by "Create API Key."

Source: Google AI Studio

3. Environment Variable: Set the environment variable GEMINI_API_KEY to your newly generated key.

4. Python Package Installation: Install required packages using:

<code class="language-bash">pip install google-genai gradio</code>

Exploring Gemini 2.0 Pro Capabilities

Let's utilize the Gemini Python client to explore its features: text, image, audio, and document processing, along with code execution.

1. Text Generation: The following code snippet demonstrates text generation using a streaming response for real-time feedback:

<code class="language-bash">pip install google-genai gradio</code>

2. Image Understanding: Using Pillow, we can process images:

<code class="language-python">import os
from google import genai

API_KEY = os.environ.get("GEMINI_API_KEY")
client = genai.Client(api_key=API_KEY)

response = client.models.generate_content_stream(
    model="gemini-2.0-pro-exp-02-05",
    contents=["Explain how the Stock Market works"])
for chunk in response:
    print(chunk.text, end="")</code>

3. Audio Understanding: Gemini 2.0 Pro directly processes audio:

<code class="language-python">from google import genai
from google.genai import types
import PIL.Image

image = PIL.Image.open('image.png')
response = client.models.generate_content_stream(
    model="gemini-2.0-pro-exp-02-05",
    contents=["Describe this image", image])
for chunk in response:
    print(chunk.text, end="")</code>

4. Document Understanding: Directly process PDFs without Langchain or RAG:

<code class="language-python">with open('audio.wav', 'rb') as f:
    audio_bytes = f.read()

response = client.models.generate_content_stream(
  model='gemini-2.0-pro-exp-02-05',
  contents=[
    'Describe this audio',
    types.Part.from_bytes(
      data=audio_bytes,
      mime_type='audio/wav',
    )
  ]
)

for chunk in response:
    print(chunk.text, end="")</code>

5. Code Generation and Execution: Gemini 2.0 Pro's standout feature is its ability to generate and execute code within the API:

<code class="language-python">from google import genai
from google.genai import types
import pathlib

prompt = "Summarize this document"
response = client.models.generate_content_stream(
  model="gemini-2.0-pro-exp-02-05",
  contents=[
      types.Part.from_bytes(
        data=pathlib.Path('cv.pdf').read_bytes(),
        mime_type='application/pdf',
      ),
      prompt])

for chunk in response:
    print(chunk.text, end="")</code>

(Note: The complete code for the Gradio application, image displays, and detailed error handling are available in the GitHub repository mentioned in the original text. This response is a condensed version for clarity.)

Building and Deploying the Gradio Application

The provided GitHub repository (Gemini-2-Pro-Chat) contains the Gradio application code. After cloning and setting up the environment, run python app.py locally. Deployment to Hugging Face Spaces involves creating a new Space, cloning the repository, adding a requirements.txt file (containing google-genai==1.0.0), modifying README.md as instructed, and pushing the changes. Remember to add your GEMINI_API_KEY as a secret in the Hugging Face Spaces settings.

Conclusion

Gemini 2.0 Pro simplifies the creation of high-performance AI applications. Its multimodal capabilities and code execution features are game-changers. While currently free with usage limits, remember to adhere to Google's terms of service. This tutorial provides a comprehensive guide to harnessing its power and deploying applications to the cloud.

The above is the detailed content of Building Multimodal AI Application with Gemini 2.0 Pro. For more information, please follow other related articles on the PHP Chinese website!