An *unofficial Python wrapper, python-gemini-api, is available for users facing frequent authentication issues or unable to use Google Authentication. This wrapper uses cookie values to interact with Google Gemini through reverse-engineering. The project involved a collaboration with Antonio Cheong.

On the official side, Google provides partially free, clean official Gemini APIs and SDKs, which can be accessed and utilized neatly via Python packages, google-generativeai.

• Gemini API
  • What is Gemini? 🔒
  • What is Python-Gemini-API? 🔐
    • Installation ✅
    • Authentication ✅
    • Quick Start ✅
    • Usage
      • # 01. Initialization ✅
      • # 02. Generate content
      • # 03. Send request
      • # 04. Text generation
      • # 05. Image generation
      • # 06. Retrieving Images from Gemini Responses
      • # 07. Generate content from images
      • # 08. Generate content using Google Services
      • # 09. Fix context setting RCID
      • # 10. Changing the Selected Response from 0 to n
      • # 11. Generate custom content
    • Further
  • Google Proprietary LLM, Gemini 🔒
  • Google Open-source LLMs 🤝
    • Open-source LLM, Gemma 🤝
    • Open-source LLM, Code Gemma 🤝
  • Utilize free open-source LLM API through Open Router 🤝 ✅

| Paper | Official Website | Official API | API Documents |

Gemini is a family of generative AI models developed by Google DeepMind that is designed for multimodal use cases. The Gemini API gives you access to the Gemini Pro and Gemini Pro Vision models. In February 2024, Google's Bard service was changed to Gemini.

This is a Python wrapper derived from the Bard API project, designed to retrieve responses from Gemini Web in REST format. Synchronous clients are preferred over asynchronous ones for Gemini because of rate limiting and blocking concerns.

pip install python-gemini-api

pip install git+https://github.com/dsdanielpark/Gemini-API.git

For the updated version, use as follows:

pip install -q -U python-gemini-api

1. Visit https://gemini.google.com/
   With browser open, try auto-collecting cookies first.

   from gemini import Gemini
   
   client = Gemini(auto_cookies=True)
   
   # Testing needed as cookies vary by region.
   # client = Gemini(auto_cookies=True, target_cookies=["__Secure-1PSID", "__Secure-1PSIDTS"])
   # client = Gemini(auto_cookies=True, target_cookies="all") # You can pass whole cookies
   
   response = client.generate_content("Hello, Gemini. What's the weather like in Seoul today?")
   print(response.payload)

2. (Manually) F12 for browser console → Session: Application → Cookies → Copy the value of some working cookie sets. If it doesn't work, go to step 3.

   Some working cookie sets

   Cookies may vary by account or region.

   First try __Secure-1PSIDCC alone. If it doesn't work, use __Secure-1PSID and __Secure-1PSIDTS. Still no success? Try these four cookies: __Secure-1PSIDCC, __Secure-1PSID, __Secure-1PSIDTS, NID. If none work, proceed to step 3 and consider sending the entire cookie file.

3. (Recommended) Export Gemini site cookies via a browser extension. For instance, use Chrome extension ExportThisCookies, open, and copy the txt file contents.

Generate content: returns parsed response.

from gemini import Gemini

cookies = {"<key>" : "<value>"} # Cookies may vary by account or region. Consider sending the entire cookie file.
client = Gemini(cookies=cookies) # You can use various args

response = client.generate_content("Hello, Gemini. What's the weather like in Seoul today?")
response.payload

Generate content from image: you can use image as input.

from gemini import Gemini

cookies = {"<key>" : "<value>"}
client = Gemini(cookies=cookies) # You can use various args

response = client.generate_content("What does the text in this image say?", image='folder/image.jpg')
response.payload

Setting language and Gemini version using environment variables:

Setting Gemini response language (Optional): Check supported languages here. Default is English.

import os

os.environ["GEMINI_LANGUAGE"] = "KR"  # Setting Gemini response language (Optional)
os.environ["GEMINI_ULTRA"] = "1"      # Switch to Gemini-advanced response (Experimental, Optional)
# In some accounts, access to Gemini Ultra may not be available. If that's the case, please revert it back to "0".

Please explicitly declare cookies in dict format. You can also enter the path to the file containing the cookie with cookie_fp(*.json, *.txt supported). Check sample cookie files in assets folder.

from gemini import Gemini

cookies = {
    "__Secure-1PSIDCC" : "value",
    "__Secure-1PSID" : "value",
    "__Secure-1PSIDTS" : "value",
    "NID" : "value",
    # Cookies may vary by account or region. Consider sending the entire cookie file.
  }

client = Gemini(cookies=cookies)
# client = Gemini(cookie_fp="folder/cookie_file.json") # (*.json, *.txt) are supported.
# client = Gemini(auto_cookies=True) # Or use auto_cookies paprameter

For auto_cookie to be set to True, and adjust target_cookies. Gemini WebUI must be active in the browser. The browser_cookie3 enables automatic cookie collection, though updates may not be complete yet.

Returns Gemini's response, but the first one might be empty.

from gemini import Gemini

cookies = {}
client = Gemini(cookies=cookies)

prompt = "Tell me about Large Language Model."
response = client.generate_content(prompt)
print(response.payload)

The output of the generate_content function is GeminiModelOutput, with the following structure:

• rcid: returns the response candidate id of the chosen candidate.
• text: returns the text of the chosen candidate.
• code: returns the codes of the chosen candidate.
• web_images: returns a list of web images from the chosen candidate.
• generated_images: returns a list of generated images from the chosen candidate.
• payload: returns the response dictionary, if available.

  Gemini-API/gemini/src/model/output.py

  Line 16 in fdf064c

  class GeminiModelOutput(BaseModel):

Send request: returns the request's payload and status_code, making debugging easier.

from gemini import Gemini

cookies = {} 
client = Gemini(cookies=cookies) 

response_text, response_status = client.send_request("Hello, Gemini. Tell me about Large Language Models.")
print(response_text)

You can track the total number of requests made by accessing the request_count property within the Gemini class.

Returns text generated by Gemini.

from gemini import Gemini

cookies = {}
client = Gemini(cookies=cookies)

prompt = "Hello, Gemini. Tell me about Large Language Models."
response = client.generate_content(prompt)
print(response.text)

Returns images generated by Gemini.

Async downloader

from gemini import Gemini, GeminiImage

cookies = {}
client = Gemini(cookies=cookies)

response = client.generate_content("Hello, Gemini. Tell me about Large Language Models.")
generated_images = response.generated_images # Check generated images [Dict]

await GeminiImage.save(generated_images, "output", cookies)
# image_data_dict = await GeminiImage.fetch_images_dict(generated_images, cookies)
# await GeminiImage.save_images(image_data_dict, "output")

import io
from gemini import Gemini, GeminiImage
from IPython.display import display, Image

cookies = {}
client = Gemini(cookies=cookies)
bytes_images_dict = GeminiImage.fetch_images_dict_sync(generated_images, cookies) # Get bytes images dict

for image_name, image_bytes in bytes_images_dict.items():
    print(image_name)
    image = Image(data=image_bytes)
    display(image)

from gemini import Gemini, GeminiImage

cookies = {}
client = Gemini(cookies=cookies)

response = client.generate_content("Create illustrations of Seoul, South Korea.")
generated_images = response.generated_images # Check generated images [Dict]

GeminiImage.save_sync(generated_images, save_path="output", cookies=cookies)

# You can use byte type image dict for printing images as follow:
# bytes_images_dict = GeminiImage.fetch_images_dict_sync(generated_images, cookies) # Get bytes images dict
# GeminiImage.save_images_sync(bytes_images_dict, path="output") # Save to dir

import asyncio
from gemini import GeminiImage

async def save_generated_images(generated_images, save_path="output", cookies=cookies):
    await GeminiImage.save(generated_images, save_path=save_path, cookies=cookies)

# Run the async function
if __name__ == "__main__":
    cookies = {}
    client = Gemini(cookies=cookies)

    response = client.generate_content("Create illustrations of Seoul, South Korea.")

    generated_images = response.generated_images  
    asyncio.run(save_generated_images(generated_images, save_path="output", cookies=cookies))

import asyncio
from gemini import Gemini, GeminiImage

async def save_generated_images(generated_images, save_path="output", cookies=cookies):
    image_data_dict = await GeminiImage.fetch_images_dict(generated_images, cookies)  # Get bytes images dict asynchronously
    await GeminiImage.save_images(image_data_dict, save_path=save_path)  

# Run the async function
if __name__ == "__main__":
    cookies = {}
    client = Gemini(cookies=cookies)

    response = client.generate_content("Create illustrations of Seoul, South Korea.")

    generated_images = response.generated_images 
    asyncio.run(save_generated_images(generated_images, save_path="output", cookies=cookies))

Returns images in response of Gemini.

Async downloader

from gemini import Gemini, GeminiImage

cookies = {}
client = Gemini(cookies=cookies)

response = client.generate_content("Give me a picture of Stanford.")
response_images = response.web_images # Check generated images

await GeminiImage.save(response_images, "output")
# image_data_dict = await GeminiImage.fetch_images_dict(response_images)
# await GeminiImage.save_images(image_data_dict, "output")

from gemini import Gemini, GeminiImage

cookies = {}
client = Gemini(cookies=cookies)

response = client.generate_content("Give me a picture of Stanford.")
response_images = response.web_images # Check response images

GeminiImage.save_sync(response_images, save_path="output")

# You can use byte type image dict as follow:
# bytes_images_dict = GeminiImage.fetch_bytes_sync(response_images) # Get bytes images dict
# GeminiImage.save_images_sync(bytes_images_dict, save_path="output") # Save to path

import asyncio
from gemini import Gemini, GeminiImage
   
async def save_response_web_imagse(response_images, save_path="output"):
    await GeminiImage.save(response_images, save_path=save_path)

if __name__ == "__main__":
    cookies = {}
    client = Gemini(cookies=cookies)
    response = client.generate_content("Give me a picture of Stanford.")
    response_images = response.web_images  
    asyncio.run(save_response_web_imagse(response_images, save_path="output"))

import asyncio
from gemini import Gemini, GeminiImage

async def save_response_web_imagse(response_images, save_path="output"):
    image_data_dict = await GeminiImage.fetch_images_dict(response_images)  # Get bytes images dict asynchronously
    await GeminiImage.save_images(image_data_dict, save_path=save_path)  

# Run the async function
if __name__ == "__main__":
    cookies = {}
    client = Gemini(cookies=cookies)
    response = client.generate_content("Give me a picture of Stanford.")
    response_images = response.web_images 
    asyncio.run(save_response_web_imagse(response_images, save_path="output"))

Takes an image as input and returns a response.

image = 'folder/image.jpg'
# image = open('folder/image.jpg', 'rb').read() # (jpg, jpeg, png, webp) are supported.

# Image file path or Byte-formatted image array
response = client.generate_content("What does the text in this image say?", image=image)
print(response)

To begin, you must link Google Workspace to activate this extension via the Gemini web extension. Please refer to the official notice and review the privacy policies for more details.

extention flags

@Gmail, @Google Drive, @Google Docs, @Google Maps, @Google Flights, @Google Hotels, @YouTube

response = client.generate_content("@YouTube Search clips related with Google Gemini")
response.response_dict

You can specify a particular response by setting its Response Candidate ID(RCID).

# Generate content for the prompt "Give me some information about the USA."
response1 = client.generate_content("Give me some information about the USA.")
# After reviewing the responses, choose the one you prefer and copy its RCID.
client.rcid = "rc_xxxx"

# Now, generate content for the next prompt "How long does it take from LA to New York?"
response2 = client.generate_content("How long does it take from LA to New York?")

# However, RCID may not persist. If parsing fails, reset `client.rcid` to None.
# client.rcid = None

In Gemini, generate_content returns the first response. This may vary depending on length or sorting. Therefore, you can specify the index of the chosen response from 0 to n as follows. However, if there is only one response, revert it back to 0.

from gemini import GeminiModelOutput

GeminiModelOutput.chosen = 1 # default is 0
response_choice_1 = client.generate_content("Give me some information about the USA.")

# If not all Gemini returns are necessarily plural, revert back to 0 in case of errors.
#  GeminiModelOutput.chosen = 0

Parse the response text to extract desired values.

Using Gemini.generate_custom_content, specify custom parsing to extract specific values. Utilize ParseMethod1 and ParseMethod2 by default, and you can pass custom parsing methods as arguments if desired. Refer to custom_parser.py.

# You can create a parser method that takes response_text as the input for custom_parser.
response_text, response_status = client.send_request("Give me some information about the USA.")

# Use custom_parser function or class inheriting from BaseParser
response = client.generate_custom_content("Give me some information about the USA.", *custom_parser)

If you want to avoid blocked requests and bans, then use Smart Proxy by Crawlbase. It forwards your connection requests to a randomly rotating IP address in a pool of proxies before reaching the target website. The combination of AI and ML make it more effective to avoid CAPTCHAs and blocks. The argument at the Secure Sockets Layer (SSL) level may need to be added to the header. Use it in conjunction with verify=False.

# Get your proxy url at crawlbase https://crawlbase.com/docs/smart-proxy/get/
proxy_url = "http://xxxxx:@smartproxy.crawlbase.com:8012" 
proxies = {"http": proxy_url, "https": proxy_url}

client = Gemini(cookies=cookies, proxies=proxies, timeout=30, verify=False)
client.session.header["crawlbaseAPI-Parameters"] = "country=US"
client.generate_content("Hello, Gemini. Give me a beautiful photo of Seoul's scenery.")

For standard cases, use Gemini class; for exceptions, use session objects. When creating a new bot Gemini server, adjust Headers.MAIN.

import requests
from gemini import Gemini, Headers

cookies = {} 

session = requests.Session()
session.headers = Headers.MAIN
for key, value in cookies.items():
    session.cookies.update({key: value})

client = Gemini(session=session) # You can use various args
response = client.generate_content("Hello, Gemini. Tell me about Large Language Model.")

Explore additional features in this document.

If you want to develop your own simple code, you can start from this simple code example.

Prepare necessary items and obtain an API key at Google AI Studio. Install on Python 3.9 or higher and enter the issued API key. Refer to the tutorial for details.

pip install -q -U google-generativeai

import google.generativeai as genai

GOOGLE_API_KEY="<your_gemini_api_key>"
genai.configure(api_key=GOOGLE_API_KEY)

model = genai.GenerativeModel('gemini-pro')
response = model.generate_content("Write me a poem about Machine Learning.")

print(response.text)

If you have sufficient GPU resources, you can download weights directly instead of using the Gemini API to generate content. Consider Gemma and Code Gemma, an open-source models available for on-premises use.

Gemma models are Google's lightweight, advanced text-to-text, decoder-only language models, derived from Gemini research. Available in English, they offer open weights and variants, ideal for tasks like question answering and summarization. For more infomation, visit Gemma-7b model card.

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt")

outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))

CodeGemma, which is an official release from Google for code LLMs, was released on April 9, 2024. It provides three models specifically designed for generating and interacting with code. You can explore the Code Gemma models and view the model card for more details.

from transformers import GemmaTokenizer, AutoModelForCausalLM

tokenizer = GemmaTokenizer.from_pretrained("google/codegemma-7b-it")
model = AutoModelForCausalLM.from_pretrained("google/codegemma-7b-it")

input_text = "Write me a Python function to calculate the nth fibonacci number."
input_ids = tokenizer(input_text, return_tensors="pt")

outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))

OpenRouter offers temporary free inference for select models. Obtain an API key from Open Router API and check free models at Open Router models. Use models with a 0-dollar token cost primarily; other models may incur charges. See more at free open-source LLM API guide.

Sync client is favored over async for Gemini due to rate limiting and blocking issues, but OpenRouter offers reliable open-source LLMs for async implementation. (Free limit: 10 requests/minute)

from gemini import OpenRouter

OPENROUTER_API_KEY = "<your_open_router_api_key>"
gemma_client = OpenRouter(api_key=OPENROUTER_API_KEY, model="google/gemma-7b-it:free")

prompt = "Do you know UCA academy in Korea? https://blog.naver.com/ulsancoding"
response = gemma_client.create_chat_completion(prompt)
print(response)

# payload = gemma_client.generate_content(prompt)
# print(payload.json())

The free model list includes:

• google/gemma-7b-it:free - google/gemma-7b from Google ***
• mistralai/mistral-7b-instruct:free - mistralai/Mistral-7B-Instruct-v0.1 for instruction from Mistral AI ****
• huggingfaceh4/zephyr-7b-beta:free - HuggingFaceH4/zephyr-7b-beta ***
• openchat/openchat-7b:free - openchat/openchat for chat **
• openrouter/cinematika-7b:free - jondurbin/cinematika-7b-v0.1
• undi95/toppy-m-7b:free - Undi95/Toppy-M-7B
• gryphe/mythomist-7b:free - Gryphe/MythoMist-7b
• nousresearch/nous-capybara-7b:free - NousResearch/Nous-Capybara-7B-V1 from Nous Research

Use Crawlbase API for efficient data scraping to train AI models, boasting a 98% success rate and 99.9% uptime. It's quick to start, GDPR/CCPA compliant, supports massive data extraction, and is trusted by 70k+ developers.

First review HanaokaYuzu/Gemini-API and the Official Google Gemini API before using this package. You can find most help on the FAQ and Issue pages.

Sincerely grateful for any reports on new features or bugs. Your valuable feedback on the code is highly appreciated. Frequent errors may occur due to changes in Google's service API interface. Both Issue reports and Pull requests contributing to improvements are always welcome. We strive to maintain an active and courteous open community.

dig the well before you are thirsty.

We would like to express our sincere gratitude to all the contributors.

This package aims to re-implement the functionality of the Bard API, which has been archived for the contributions of the beloved open-source community, despite Gemini's official API already being available.

Contributors to the Bard API and Gemini API.

MIT license, 2024. We hereby strongly disclaim any explicit or implicit legal liability related to our works. Users are required to use this package responsibly and at their own risk. This project is a personal initiative and is not affiliated with or endorsed by Google. It is recommended to use Google's official API.

1. Introducing GEMINI: Multimodal Generative Models
2. Google DeepMind: GEMINI Introduction
3. GEMMA: A Unified Language Model for Text Generation, Understanding, Translation, Coding, and Math
4. AI at Google: GEMS Documentation
5. CodeGMMA: Large Language Models Can Write Realistic Programming Assignments
6. Announcing CodeGen: Building Better Developers' Tools Using LLMs
7. Google: CodeGen Release
8. acheong08/Bard
9. dsdanielpark/Bard-API
10. HanaokaYuzu/Gemini-API
11. GoogleCloudPlatform/generative-ai
12. OpenRouter
13. Google AI Studio

Warning Users assume full legal responsibility for GeminiAPI. Not endorsed by Google. Excessive use may lead to account restrictions. Changes in policies or account status may affect functionality. Utilize issue and discussion pages.

Python 3.7 or higher.