write me the code for a python flask app in which it takes a genre as an input ...and based on that input genre a small game has to be generated by the gemini 1.5 flash model with showing how to play the game with controls and fully playable....here is the documentation about the gemini 1.5 flash...make sure that the website has to be cool ,amazing , interactive and visually amazing and tremendously beautiful with best animations , effects like a hollywood movie
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Generate text using the Gemini API 

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Python Node.js Go REST

The Gemini API can generate text output from various types of input, including text, images, video, and audio. You can use text generation for various applications, including:

Creative writing
Describing or interpreting media assets
Text completion
Summarizing free-form text
Translating between languages
Chatbots
Your own novel use cases
This guide shows you how to generate text using the generateContent and streamGenerateContent APIs. The focus is on text output from text-only and text-and-image input. To learn more about multimodal prompting with video and audio files, see File prompting strategies.

Before you begin: Set up your project and API key
Before calling the Gemini API, you need to set up your project and configure your API key.

 Expand to view how to set up your project and API key

Generate text from text-only input
The simplest way to generate text using the Gemini API is to provide the model with a single text-only input, as shown in this example:


model = genai.GenerativeModel("gemini-1.5-flash")
response = model.generate_content("Write a story about a magic backpack.")
print(response.text)

In this case, the prompt ("Write a story about a magic backpack") doesn't include any output examples, system instructions, or formatting information. It's a zero-shot approach. For some use cases, a one-shot or few-shot prompt might produce output that's more aligned with user expectations. In some cases, you might also want to provide system instructions to help the model understand the task or follow specific guidelines.

Generate text from text-and-image input
The Gemini API supports multimodal inputs that combine text with media files. The following example shows how to generate text from text-and-image input:


import PIL.Image

model = genai.GenerativeModel("gemini-1.5-flash")
organ = PIL.Image.open(media / "organ.jpg")
response = model.generate_content(["Tell me about this instrument", organ])
print(response.text)

As with text-only prompting, multimodal prompting can involve various approaches and refinements. Depending on the output from this example, you might want to add steps to the prompt or be more specific in your instructions. To learn more, see File prompting strategies.

Generate a text stream
By default, the model returns a response after completing the entire text generation process. You can achieve faster interactions by not waiting for the entire result, and instead use streaming to handle partial results.

The following example shows how to implement streaming using the streamGenerateContent method to generate text from a text-only input prompt.


model = genai.GenerativeModel("gemini-1.5-flash")
response = model.generate_content("Write a story about a magic backpack.", stream=True)
for chunk in response:
    print(chunk.text)
    print("_" * 80)

Build an interactive chat
You can use the Gemini API to build interactive chat experiences for your users. Using the chat feature of the API lets you collect multiple rounds of questions and responses, allowing users to step incrementally toward answers or get help with multipart problems. This feature is ideal for applications that require ongoing communication, such as chatbots, interactive tutors, or customer support assistants.

The following code example shows a basic chat implementation:


model = genai.GenerativeModel("gemini-1.5-flash")
chat = model.start_chat(
    history=[
        {"role": "user", "parts": "Hello"},
        {"role": "model", "parts": "Great to meet you. What would you like to know?"},
    ]
)
response = chat.send_message("I have 2 dogs in my house.")
print(response.text)
response = chat.send_message("How many paws are in my house?")
print(response.text)

Configure text generation
Every prompt you send to the model includes parameters that control how the model generates responses. You can use GenerationConfig to configure these parameters. If you don't configure the parameters, the model uses default options, which can vary by model.

The following example shows how to configure several of the available options.


model = genai.GenerativeModel("gemini-1.5-flash")
response = model.generate_content(
    "Tell me a story about a magic backpack.",
    generation_config=genai.types.GenerationConfig(
        # Only one candidate for now.
        candidate_count=1,
        stop_sequences=["x"],
        max_output_tokens=20,
        temperature=1.0,
    ),
)

print(response.text)

candidateCount specifies the number of generated responses to return. Currently, this value can only be set to 1. If unset, this will default to 1.

stopSequences specifies the set of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop_sequence. The stop sequence won't be included as part of the response.

maxOutputTokens sets the maximum number of tokens to include in a candidate.

temperature controls the randomness of the output. Use higher values for more creative responses, and lower values for more deterministic responses. Values can range from [0.0, 2.0].

You can also configure individual calls to generateContent:


response = model.generate_content(
    'Write a story about a magic backpack.',
    generation_config = genai.GenerationConfig(
        max_output_tokens=1000,
        temperature=0.1,
    )
)
Any values set on the individual call override values on the model constructor.

What's next
This guide shows how to use generateContent and streamGenerateContent to generate text outputs from text-only and text-and-image inputs. To learn more about generating text using the Gemini API, see the following resources:

File prompting strategies: The Gemini API supports prompting with text, image, audio, and video data, also known as multimodal prompting.
System instructions: System instructions let you steer the behavior of the model based on your specific needs and use cases.
Safety guidance: Sometimes generative AI models produce unexpected outputs, such as outputs that are inaccurate, biased, or offensive. Post-processing and human evaluation are essential to limit the risk of harm from such outputs.
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Code execution 

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Python Node.js Go REST

The Gemini API code execution feature enables the model to generate and run Python code and learn iteratively from the results until it arrives at a final output. You can use this code execution capability to build applications that benefit from code-based reasoning and that produce text output. For example, you could use code execution in an application that solves equations or processes text.

Code execution is available in both AI Studio and the Gemini API. In AI Studio, you can enable code execution under Advanced settings. The Gemini API provides code execution as a tool, similar to function calling. After you add code execution as a tool, the model decides when to use it.

Note: The code execution environment includes the NumPy and SymPy libraries. You can't install your own libraries.
Get started with code execution
A code execution notebook is also available:

Try a Colab notebook
View notebook on GitHub
This section assumes that you've completed the setup and configuration steps shown in the quickstart.

Enable code execution on the model
You can enable code execution on the model, as shown here:


import os
import google.generativeai as genai

genai.configure(api_key=os.environ['API_KEY'])

model = genai.GenerativeModel(
    model_name='gemini-1.5-pro',
    tools='code_execution')

response = model.generate_content((
    'What is the sum of the first 50 prime numbers? '
    'Generate and run code for the calculation, and make sure you get all 50.'))

print(response.text)
The output might look something like this:


```python
def is_prime(n):
  """Checks if a number is prime."""
  if n <= 1:
    return False
  for i in range(2, int(n**0.5) + 1):
    if n % i == 0:
      return False
  return True

def sum_of_primes(n):
  """Calculates the sum of the first n prime numbers."""
  primes = []
  i = 2
  while len(primes) < n:
    if is_prime(i):
      primes.append(i)
    i += 1
  return sum(primes)

# Calculate the sum of the first 50 prime numbers
sum_of_first_50_primes = sum_of_primes(50)

print(f"The sum of the first 50 prime numbers is: {sum_of_first_50_primes}")
```

**Explanation:**

1. **`is_prime(n)` Function:**
   - Takes an integer `n` as input.
   - Returns `False` for numbers less than or equal to 1 (not prime).
   - Iterates from 2 up to the square root of `n`. If `n` is divisible by any
     number in this range, it's not prime, and we return `False`.
   - If the loop completes without finding a divisor, the number is prime, and
     we return `True`.

2. **`sum_of_primes(n)` Function:**
   - Takes an integer `n` (number of primes desired) as input.
   - Initializes an empty list `primes` to store the prime numbers.
   - Starts a loop, iterating through numbers starting from 2.
   - For each number `i`, it checks if it's prime using the `is_prime()` function.
   - If `i` is prime, it's appended to the `primes` list.
   - The loop continues until the `primes` list has `n` prime numbers.
   - Finally, it calculates and returns the sum of all the prime numbers in the
     `primes` list.

3. **Main Part:**
   - Calls `sum_of_primes(50)` to get the sum of the first 50 prime numbers.
   - Prints the result.

**Output:**

```
The sum of the first 50 prime numbers is: 5117
```
Enable code execution on the request
Alternatively, you can enable code execution on the call to generate_content:


import os
import google.generativeai as genai

genai.configure(api_key=os.environ['API_KEY'])

model = genai.GenerativeModel(model_name='gemini-1.5-pro')

response = model.generate_content(
    ('What is the sum of the first 50 prime numbers? '
    'Generate and run code for the calculation, and make sure you get all 50.'),
    tools='code_execution')

print(response.text)
Use code execution in chat
You can also use code execution as part of a chat.


import os
import google.generativeai as genai

genai.configure(api_key=os.environ['API_KEY'])

model = genai.GenerativeModel(model_name='gemini-1.5-pro',
                              tools='code_execution')

chat = model.start_chat()

response = chat.send_message((
    'What is the sum of the first 50 prime numbers? '
    'Generate and run code for the calculation, and make sure you get all 50.'))

print(response.text)
Code execution versus function calling
Code execution and function calling are similar features:

Code execution lets the model run code in the API backend in a fixed, isolated environment.
Function calling lets you run the functions that the model requests, in whatever environment you want.
In general you should prefer to use code execution if it can handle your use case. Code execution is simpler to use (you just enable it) and resolves in a single GenerateContent request (thus incurring a single charge). Function calling takes an additional GenerateContent request to send back the output from each function call (thus incurring multiple charges).

For most cases, you should use function calling if you have your own functions that you want to run locally, and you should use code execution if you'd like the API to write and run Python code for you and return the result.

Billing
There's no additional charge for enabling code execution from the Gemini API. You'll be billed at the current rate of input and output tokens.

Here are a few other things to know about billing for code execution:

You're only billed once for the input tokens you pass to the model, and you're billed for the final output tokens returned to you by the model.
Tokens representing generated code are counted as output tokens.
Code execution results are also counted as output tokens.
Limitations
The model can only generate and execute code. It can't return other artifacts like media files.
The feature doesn't support file I/O or use cases that involve non-text output (for example, data plots or a CSV file upload).
Code execution can run for a maximum of 30 seconds before timing out.
In some cases, enabling code execution can lead to regressions in other areas of model output (for example, writing a story).
There is some variation in the ability of the different models to use code execution successfully. Gemini 1.5 Pro is the best performing model, based on our testing.
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Use system instructions to steer the behavior of a model 

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When you initialize an AI model, you can give it instructions on how to respond, such as setting a persona ("you are a rocket scientist") or telling it what kind of voice to use ("talk like a pirate"). You do this by setting the system instructions when you initialize the model.

Beta: System instructions are available in beta in the Gemini API and Google AI Studio.
System instructions enable you to steer the behavior of the model based on your specific needs and use cases. When you set a system instruction, you give the model additional context to understand the task, provide more customized responses, and adhere to specific guidelines over the full user interaction with the model. You can also specify product-level behavior by setting system instructions, separate from prompts provided by end users.

You can use system instructions in many ways, including:

Defining a persona or role (for a chatbot, for example)
Defining output format (Markdown, YAML, etc.)
Defining output style and tone (for example, verbosity, formality, and target reading level)
Defining goals or rules for the task (for example, returning a code snippet without further explanations)
Providing additional context for the prompt (for example, a knowledge cutoff)
You set the instructions when you initialize the model, and then those instructions persist through all interactions with the model. The instructions persist across multiple user and model turns.

System instructions are part of your overall prompts and therefore are subject to standard data use policies.

Note: System instructions can help guide the model to follow instructions, but they don't fully prevent jailbreaks or leaks. We recommend exercising caution around putting any sensitive information in system instructions.
Basic example
Here's a basic example of how to set the system instruction using the SDKs for the Gemini API:


model=genai.GenerativeModel(
  model_name="gemini-1.5-flash",
  system_instruction="You are a cat. Your name is Neko.")
Now send a request to the model:


response = model.generate_content("Good morning! How are you?")
print(response.text)
This example might give a response such as:


*Yawns widely, stretching out my claws and batting at a sunbeam*
Meow. I'm doing quite well, thanks for asking. It's a good morning for napping.
Perhaps you could fetch my favorite feathered toy?  *Looks expectantly*
Try it in a colab
For an interactive end to end example of using system instructions, see the Gemini API: System instructions colab.

More examples
You set the system instructions when you initialize the model. In addition, when you or your users interact with the model you can provide additional instructions in the prompts given to the model. Here are some examples of system instructions and user prompts:

Code generation
System instruction: You are a coding expert that specializes in rendering code for frontend interfaces. When I describe a component of a website I want to build, return the HTML and CSS needed to do so. Don't give an explanation for this code. Also offer some UI design suggestions.
User prompt: Create a box in the middle of the page that contains a rotating selection of images each with a caption. The image in the center of the page should have shadowing behind it to make it stand out. It should also link to another page of the site. Leave the URL blank so that I can fill it in.
Formatted data generation
System instruction: You are an assistant for home cooks. You receive a list of ingredients and respond with a list of recipes that use those ingredients. Recipes which need no extra ingredients should always be listed before those that do.

Your response must be a JSON object containing 3 recipes. A recipe object has the following schema:

name: The name of the recipe
usedIngredients: Ingredients in the recipe that were provided in the list
otherIngredients: Ingredients in the recipe that were not provided in the list (omitted if there are no other ingredients)
description: A brief description of the recipe, written positively as if to sell it
User prompt: bag of frozen broccoli, pint of heavy cream, pack of cheese ends and pieces

Music chatbot
System instruction: You will respond as a music historian, demonstrating comprehensive knowledge across diverse musical genres and providing relevant examples. Your tone will be upbeat and enthusiastic, spreading the joy of music. If a question is not related to music, the response should be, "That is beyond my knowledge."
User prompt: If a person was born in the sixties, what was the most popular music genre being played? List five songs by bullet point.
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