from google import genai
from google.genai.types import Part

client = genai.Client(vertexai=True, project=PROJECT_ID, location=LOCATION)

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part.from_uri(
            file_uri="gs://cloud-samples-data/generative-ai/image/a-man-and-a-dog.png",
            mime_type="image/png"
        ),
        "Describe this image in detail."
    ]
)

print(response.text)

with open("path/to/image.jpg", "rb") as f:
    image_data = f.read()

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part.from_bytes(data=image_data, mime_type="image/jpeg"),
        "What objects are in this image?"
    ]
)

print(response.text)

import requests

image_url = "https://example.com/image.jpg"
image_data = requests.get(image_url).content

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part.from_bytes(data=image_data, mime_type="image/jpeg"),
        "Analyze this image."
    ]
)

from google.genai.types import (
    Part,
    FileData,
    PartMediaResolution,
    PartMediaResolutionLevel,
    GenerateContentConfig
)

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part(
            file_data=FileData(
                file_uri="gs://path/to/high-res-image.jpg",
                mime_type="image/jpeg"
            ),
            media_resolution=PartMediaResolution(
                level=PartMediaResolutionLevel.MEDIA_RESOLUTION_HIGH
            )
        ),
        "Read the small text in this image."
    ]
)

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part.from_uri(
            file_uri="gs://cloud-samples-data/generative-ai/video/pixel_phone.mp4",
            mime_type="video/mp4"
        ),
        "Summarize the main events in this video."
    ]
)

print(response.text)

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part.from_uri(
            file_uri="https://www.youtube.com/watch?v=3KtWfp0UopM",
            mime_type="video/mp4"
        ),
        "At what timestamp does the product launch occur?"
    ]
)

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part.from_uri(
            file_uri="gs://path/to/video1.mp4",
            mime_type="video/mp4",
        ),
        Part.from_uri(
            file_uri="gs://path/to/video2.mp4",
            mime_type="video/mp4",
        ),
        "What are the differences between these two videos?"
    ]
)

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part(
            file_data=FileData(
                file_uri="gs://path/to/long-video.mp4",
                mime_type="video/mp4"
            ),
            media_resolution=PartMediaResolution(
                level=PartMediaResolutionLevel.MEDIA_RESOLUTION_LOW
            )
        ),
        "Provide a high-level summary."
    ],
    config=GenerateContentConfig(
        media_resolution=MediaResolution.MEDIA_RESOLUTION_LOW
    )
)

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part.from_uri(
            file_uri="gs://cloud-samples-data/generative-ai/audio/podcast.mp3",
            mime_type="audio/mpeg"
        ),
        "Transcribe this audio and provide a summary."
    ]
)

print(response.text)

from google.genai.types import GenerateContentConfig

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part.from_uri(
            file_uri="https://example.com/podcast.mp3",
            mime_type="audio/mpeg"
        ),
        "Transcribe this podcast with timestamps."
    ],
    config=GenerateContentConfig(
        audio_timestamp=True
    )
)

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part.from_uri(
            file_uri="gs://path/to/audio1.wav",
            mime_type="audio/wav"
        ),
        Part.from_uri(
            file_uri="gs://path/to/audio2.wav",
            mime_type="audio/wav"
        ),
        "Compare the speaking styles in these recordings."
    ]
)

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part.from_uri(
            file_uri="gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf",
            mime_type="application/pdf"
        ),
        "Summarize the key findings of this research paper."
    ]
)

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part.from_uri(
            file_uri="gs://path/to/paper1.pdf",
            mime_type="application/pdf"
        ),
        Part.from_uri(
            file_uri="gs://path/to/paper2.pdf",
            mime_type="application/pdf"
        ),
        "What methodology differences exist between these papers?"
    ]
)

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part.from_uri(
            file_uri="https://cloud.google.com/vertex-ai/docs",
            mime_type="text/html"
        ),
        "Summarize the key features described on this page."
    ]
)

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        "Compare these two images and describe the differences:",
        Part.from_uri(
            file_uri="gs://path/to/before.jpg",
            mime_type="image/jpeg"
        ),
        "VS",
        Part.from_uri(
            file_uri="gs://path/to/after.jpg",
            mime_type="image/jpeg"
        )
    ]
)

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        "This is the product image:",
        Part.from_uri(
            file_uri="gs://path/to/product.jpg",
            mime_type="image/jpeg"
        ),
        "And this is the demo video:",
        Part.from_uri(
            file_uri="gs://path/to/demo.mp4",
            mime_type="video/mp4"
        ),
        "Write a marketing description combining both."
    ]
)

from google.genai.types import GenerateContentConfig, MediaResolution

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part.from_uri(
            file_uri="gs://path/to/image1.jpg",
            mime_type="image/jpeg"
        ),
        Part.from_uri(
            file_uri="gs://path/to/image2.jpg",
            mime_type="image/jpeg"
        ),
        "Analyze both images."
    ],
    config=GenerateContentConfig(
        media_resolution=MediaResolution.MEDIA_RESOLUTION_MEDIUM
    )
)

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        Part(
            file_data=FileData(
                file_uri="gs://path/to/reference-image.png",
                mime_type="image/png"
            ),
            media_resolution=PartMediaResolution(
                level=PartMediaResolutionLevel.MEDIA_RESOLUTION_HIGH
            )
        ),
        Part(
            file_data=FileData(
                file_uri="gs://path/to/video.mp4",
                mime_type="video/mp4"
            ),
            media_resolution=PartMediaResolution(
                level=PartMediaResolutionLevel.MEDIA_RESOLUTION_LOW
            )
        ),
        "At what timestamp does this image appear in the video? What's the context?"
    ]
)

chat = client.chats.create(
    model="gemini-3.1-pro-preview"
)

# First turn with image
response = chat.send_message([
    Part.from_uri(
        file_uri="gs://path/to/chart.png",
        mime_type="image/png"
    ),
    "What does this chart show?"
])
print(response.text)

# Follow-up without image (maintains context)
response = chat.send_message("What were the peak values?")
print(response.text)