Gemini API
2.4.12
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.
Tip
| 2024-03-26 | [See Code Examples]
Check out temporarily free Open-source LLM APIs with Open Router. (Free limit: 10 requests/minute)
| 2024-05-20 | There are some changes in logic depending on the region/country (IP) and account. Users need to check the following to find the appropriate logic for themselves. The package remains suitable for the most common use cases.
BOT_SERVER params in const.pyself._sid in client.pyself._rcid in client.py| 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.
| Model | Type | Access | Details |
|---|---|---|---|
| Gemini | Proprietary | API [13] | A proprietary multimodal AI by Google DeepMind, including advanced models such as Gemini Pro and Gemini Pro Vision. Access is restricted to API usage; additional insights may be obtained through the paper and website. [1][2] |
| Gemma | Open Source | Downloadable Free API |
An open-source text-to-text language model suitable for tasks like QA and summarization. Weights are downloadable for on-premises use, and detailed documentation is provided via the paper and website. [3][4] |
| Code Gemma | Open Source | Downloadable | Designed specifically for programming tasks, this open-source model offers downloadable weights to assist developers with code generation and similar activities. Refer to the associated paper, blog post, and Hugging Face collection for more information. [5][6][7] |
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
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)(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.
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.
(Recommended) Export Gemini site cookies via a browser extension. For instance, use Chrome extension ExportThisCookies, open, and copy the txt file contents.
Important
Experiment with different Google accounts and browser settings to find a working cookie. Success may vary by IP and account status. Once connected, a cookie typically remains effective over a month. Keep testing until successful.
Generate content: returns parsed response.
from gemini import Gemini cookies = {"" : "" } # 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 = {"" : "" } 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
Note
If the generate_content method returns an empty payload, try executing it again without reinitializing the Gemini object.
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 paprameterFor 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)Important
DO NOT send same prompt repeatly. If the session connects successfully and generate_content runs well, CLOSE Gemini website. If Gemini web stays open in the browser, cookies may expire faster.
The output of the generate_content function is GeminiModelOutput, with the following structure:
Gemini-API/gemini/src/model/output.py
Line 16 in fdf064c
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")Display images in IPython You can display the image or transmit it to another application in byte format.
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)Sync downloader
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 dirAsync downloader wrapper
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))GeminiImage.save method logic
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))Note
Use GeminiImage for image processing. web_images works without cookies, but for images like generated_image from Gemini, pass cookies. Cookies are needed to download images from Google's storage. Check the response or use existing cookies variable.
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")Sync 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 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 pathAsync downloader wrapper
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"))GeminiImage.save method logic
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_dictGoogle Workspace
Google Maps
Google Flights
Google Hotels
YouTube
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 = NoneIn 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 = 0Parse 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)Gemini-API/gemini/client.py
Line 323 in 31b8424
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=""
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 = ""
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-v0.1undi95/toppy-m-7b:free - Undi95/Toppy-M-7Bgryphe/mythomist-7b:free - Gryphe/MythoMist-7bnousresearch/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.
Modifications to the async client using my logic are needed, along with automatic cookie collection via browser_cookie3, and implementation of other Bard API features (such as code extraction, export to Replit, graph drawing, etc.).
Please note that while reviewing automatic cookie collection, it appears that cookies expire immediately upon sending a request for collection. Efforts to make it more user-friendly were unsuccessful. Also, the _sid value seems to work normally even when returned as None.
Lastly, if the CustomParser and ResponseParser algorithms do not function properly, new parsing methods can be updated through conditional statements in the relevant sections.
I do not plan to actively curate this repository. Please review HanaokaYuzu/Gemini-API first.
Thank you, and have a great day.
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.
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.
