Quickstart

response = client.models.generate_content(
    model="gemini-2.0-flash-exp",
    contents="What are the key differences between Python and JavaScript?"
)

print(response.text)

prompt = """
Write a short story about a robot learning to paint.
Make it heartwarming and under 200 words.
"""

response = client.models.generate_content(
    model="gemini-2.0-flash-exp",
    contents=prompt
)

print(response.text)

prompt = "Explain quantum computing in detail."

for chunk in client.models.generate_content_stream(
    model="gemini-2.0-flash-exp",
    contents=prompt
):
    print(chunk.text, end="")

from google import genai
from google.genai import types

# Initialize client
client = genai.Client(
    vertexai=True,
    project="your-project-id",
    location="us-central1"
)

# Analyze a product image
response = client.models.generate_content(
    model="gemini-2.0-flash-exp",
    contents=[
        types.Part.from_uri(
            file_uri="gs://cloud-samples-data/generative-ai/image/a-man-and-a-dog.png",
            mime_type="image/png"
        ),
        """
        Analyze this image and provide:
        1. A detailed description
        2. The mood or atmosphere
        3. Potential use cases for marketing
        """
    ]
)

print(response.text)

# Start a chat session
chat = client.chats.create(
    model="gemini-2.0-flash-exp"
)

# First message
response = chat.send_message(
    "I'm building a web app. Should I use React or Vue?"
)
print("Gemini:", response.text)

# Follow-up question
response = chat.send_message(
    "What about performance differences?"
)
print("Gemini:", response.text)

# The model remembers the context
response = chat.send_message(
    "Can you show me a simple component example?"
)
print("Gemini:", response.text)

response = client.models.generate_content(
    model="gemini-2.0-flash-exp",
    contents="Write a product description for a smart watch.",
    config=types.GenerateContentConfig(
        temperature=0.9,          # Higher = more creative (0.0-2.0)
        top_p=0.95,              # Nucleus sampling
        max_output_tokens=1024,   # Maximum length
    )
)

print(response.text)

system_instruction = """
You are a helpful coding assistant specializing in Python.
Always provide working code examples with comments.
Use best practices and explain your reasoning.
"""

response = client.models.generate_content(
    model="gemini-2.0-flash-exp",
    contents="How do I read a CSV file?",
    config=types.GenerateContentConfig(
        system_instruction=system_instruction
    )
)

print(response.text)

safety_settings = [
    types.SafetySetting(
        category=types.HarmCategory.HARM_CATEGORY_HATE_SPEECH,
        threshold=types.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE
    ),
    types.SafetySetting(
        category=types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
        threshold=types.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE
    )
]

response = client.models.generate_content(
    model="gemini-2.0-flash-exp",
    contents="Your prompt here",
    config=types.GenerateContentConfig(
        safety_settings=safety_settings
    )
)

from google import genai
from google.genai import types
import os

class GeminiAnalyzer:
    def __init__(self, project_id: str, location: str = "us-central1"):
        self.client = genai.Client(
            vertexai=True,
            project=project_id,
            location=location
        )
        self.model = "gemini-2.0-flash-exp"
    
    def analyze_image(self, image_uri: str, question: str) -> str:
        """Analyze an image with a custom question."""
        response = self.client.models.generate_content(
            model=self.model,
            contents=[
                types.Part.from_uri(
                    file_uri=image_uri,
                    mime_type="image/jpeg"
                ),
                question
            ],
            config=types.GenerateContentConfig(
                temperature=0.4,
                max_output_tokens=2048
            )
        )
        return response.text
    
    def chat(self, messages: list[str]) -> list[str]:
        """Have a multi-turn conversation."""
        chat = self.client.chats.create(model=self.model)
        responses = []
        
        for message in messages:
            response = chat.send_message(message)
            responses.append(response.text)
        
        return responses

# Usage
analyzer = GeminiAnalyzer(project_id="your-project-id")

# Analyze an image
result = analyzer.analyze_image(
    image_uri="gs://cloud-samples-data/generative-ai/image/meal.png",
    question="What ingredients would I need to recreate this dish?"
)
print(result)

# Have a conversation
responses = analyzer.chat([
    "What's the best way to learn Python?",
    "How long would that take?",
    "What resources do you recommend?"
])
for i, response in enumerate(responses, 1):
    print(f"Response {i}: {response}\n")

from google.api_core import exceptions

try:
    response = client.models.generate_content(
        model="gemini-2.0-flash-exp",
        contents="Your prompt here"
    )
    print(response.text)
    
except exceptions.ResourceExhausted:
    print("Quota exceeded. Please try again later.")
    
except exceptions.InvalidArgument as e:
    print(f"Invalid request: {e}")
    
except exceptions.PermissionDenied:
    print("Permission denied. Check your authentication.")
    
except Exception as e:
    print(f"Unexpected error: {e}")