I believe your goal is as follows.

• You want to convert your top script with google-generativeai.

In this case, how about the following modification?

Modified script:

import google.generativeai as genai
import google.ai.generativelanguage as glm

apiKey = "###" # Please set your API key.

genai.configure(api_key=apiKey)

tool = glm.Tool(
    function_declarations=[
        glm.FunctionDeclaration(
            name="find_movies",
            description="find movie titles currently playing in theaters based on any description, genre, title words, etc.",
            parameters=glm.Schema(
                type=glm.Type.OBJECT,
                properties={
                    "location": glm.Schema(
                        type=glm.Type.STRING,
                        description="The city and state, e.g. San Francisco, CA or a zip code e.g. 95616",
                    ),
                    "description": glm.Schema(
                        type=glm.Type.STRING,
                        description="Any kind of description including category or genre, title words, attributes, etc.",
                    ),
                },
                required=["description"],
            ),
        ),
        glm.FunctionDeclaration(
            name="find_theaters",
            description="find theaters based on location and optionally movie title which is currently playing in theaters",
            parameters=glm.Schema(
                type=glm.Type.OBJECT,
                properties={
                    "location": glm.Schema(
                        type=glm.Type.STRING,
                        description="The city and state, e.g. San Francisco, CA or a zip code e.g. 95616",
                    ),
                    "movie": glm.Schema(
                        type=glm.Type.STRING,
                        description="Any movie title",
                    ),
                },
                required=["location"],
            ),
        ),
        glm.FunctionDeclaration(
            name="get_showtimes",
            description="Find the start times for movies playing in a specific theater",
            parameters=glm.Schema(
                type=glm.Type.OBJECT,
                properties={
                    "location": glm.Schema(
                        type=glm.Type.STRING,
                        description="The city and state, e.g. San Francisco, CA or a zip code e.g. 95616",
                    ),
                    "movie": glm.Schema(
                        type=glm.Type.STRING,
                        description="Any movie title",
                    ),
                    "theater": glm.Schema(
                        type=glm.Type.STRING,
                        description="Name of the theater",
                    ),
                    "date": glm.Schema(
                        type=glm.Type.STRING,
                        description="Date for requested showtime",
                    ),
                },
                required=["location", "movie", "theater", "date"],
            ),
        ),
    ]
)
model = genai.GenerativeModel("gemini-pro", tools=[tool])
messages = [
    glm.Part(text="Which theaters in Mountain View show Barbie movie?"),
    glm.Part(
        function_call=glm.FunctionCall(
            name="find_theaters",
            args={"location": "Mountain View, CA", "movie": "Barbie"},
        )
    ),
    glm.Part(
        function_response=glm.FunctionResponse(
            name="find_theaters",
            response={
                "name": "find_theaters",
                "content": {
                    "movie": "Barbie",
                    "theaters": [
                        {
                            "name": "AMC Mountain View 16",
                            "address": "2000 W El Camino Real, Mountain View, CA 94040",
                        },
                        {
                            "name": "Regal Edwards 14",
                            "address": "245 Castro St, Mountain View, CA 94040",
                        },
                    ],
                },
            },
        )
    ),
]
response = model.generate_content(messages)
print(response.text)

Testing:

When this script is run, the following result is obtained.

There are two theaters in Mountain View showing Barbie movie: AMC Mountain View 16 and Regal Edwards 14.

For anyone (or bot) that arrives here looking for how to set a timeout when using the modern-as-of-late-2024 Google GenAI SDK that can also use the Vertex API (more info at the repository), setting a timeout using HttpOptions works.

Here is an example with a 30-second timeout:

from google import genai
from google.genai import types

client = genai.Client(
    http_options=types.HttpOptions(timeout=30_000) # timeout is in milliseconds
)

response = client.models.generate_content(
    model='gemini-2.5-flash', contents='Why is the sky blue?'
)
print(response.text)

For Python, it is easier to work with the Python unified SDK than playing directly with the endpoints using libs like requests.

Using the Python SDK, you have two ways to send images within your prompts:

1. you can send them inline, very straightforward:

image = Image.open(img_path)

response = client.models.generate_content(
    model=MODEL_ID,
    contents=[
        image,
        "ask something about the image here"
    ]
)

Or you can use the (File API)[https://googleapis.github.io/python-genai/genai.html#genai.files.AsyncFiles.upload] for payloads that may be higher than 20MB. Using that you will first upload the media using the SDK:

file_ref = client.files.upload(path=img_path)

And then you will reference the file API object file_ref within your prompt:

response = client.models.generate_content(
    model=MODEL_ID,
    contents=[
        file_ref,
        "ask something about the image here"
    ]
)

The response for your requests using those example requests will be text only and will can handle the results in the same way you do for text only prompts - like exploring the response object with structures like response.text for the model answer in text. more details about the response object structure can be found at the SDK reference doc.

This Gemini API get started notebook may be useful as well.

hope that helps.