Development Status :: 2 - Pre-Alpha
Not fully prepared yet.
A Python wrapper, python-gemini-api, interacts with Google Gemini via reverse engineering. Reconstructing with REST syntax for users facing frequent authentication errors or unable to authenticate properly in Google Authentication.
Collaborated competently with Antonio Cheong.
What is Gemini?
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. Paper, Official Website, Official API, API Documents.
pip install python-gemini-api
pip install git+https://github.com/dsdanielpark/Gemini-API.git
Warning Cookies can change quickly. Don't reopen the same session or repeat prompts too often; they'll expire faster.
- Go to https://gemini.google.com/ and wait for it to load.
- (Recommended) While on the gemini website, export cookies using a Chrome extension. If using ExportThisCookies extension, open the downloaded txt file and copy its contents exactly as they are.
- Or, press F12 → Network → Send prompt to webui gemini → Click post address starting with "https://gemini.google.com/_/BardChatUi/data/assistant.lamda.BardFrontendService/StreamGenerate" → Copy cookies → Format as a dictionary manually. Refer to this image.
After changed Bard to Gemini, multiple cookies, often updated, are needed based on region or Google account. Thus, automatic cookie renewal logic is crucial.
You must appropriately set the cookies_dict parameter to Gemini class. When using the auto_cookies argument to automatically collect cookies, keep the Gemini web page opened that receives Gemini's response open in your web browser.
from gemini import Gemini
cookies = {
"key": "value"
}
GeminiClient = Gemini(cookies=cookies)
# GeminiClient = Gemini(cookie_fp="folder/cookie_file.json") # Or use cookie file path
# GeminiClient = Gemini(auto_cookies=True) # Or use auto_cookies paprameterCan update cookies automatically using broser_cookie3. For the first attempt, manually download the cookies to test the functionality.
Before proceeding, ensure that the GeminiClient object is defined without any errors.
prompt = "Hello, Gemini. What's the weather like in Seoul today?"
response = GeminiClient.generate_content(prompt)
print(response)prompt = "Hello, Gemini. Give me a beautiful photo of Seoul's scenery."
response = GeminiClient.generate_content(prompt)
print("\n".join(response.images)) # Print images
for i, image in enumerate(response.images): # Save images
image.save(path="folder_path/", filename=f"seoul_{i}.png")
As an experimental feature, it is possible to ask questions with an image. However, this functionality is only available for accounts with image upload capability in Gemini's web UI.
prompt = "What is in the image?"
image = open("folder_path/image.jpg", "rb").read() # (jpeg, png, webp) are supported.
response = GeminiClient.generate_content(prompt, image)
Text To Speech(TTS) from Gemini
Business users and high traffic volume may be subject to account restrictions according to Google's policies. Please use the Official Google Cloud API for any other purpose.
text = "Hello, I'm developer in seoul" # Gemini will speak this sentence
response = GeminiClient.generate_content(prompt)
audio = GeminiClient.speech(text)
with open("speech.ogg", "wb") as f:
f.write(bytes(audio["audio"]))
If you are working behind a proxy, use the following.
proxies = {
"http": "http://proxy.example.com:8080",
"https": "https://proxy.example.com:8080"
}
GeminiClient = Gemini(cookies=cookies, proxies=proxies, timeout=30)
GeminiClient.generate_content("Hello, Gemini. Give me a beautiful photo of Seoul's scenery.")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.
# 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}
GeminiClient = Gemini(cookies=cookies, proxies=proxies, timeout=30)
GeminiClient.generate_content("Hello, Gemini. Give me a beautiful photo of Seoul's scenery.")You can continue the conversation using a reusable session. However, this feature is limited, and it is difficult for a package-level feature to perfectly maintain context. You can try to maintain the consistency of conversations same way as other LLM services, such as passing some sort of summary of past conversations to the DB.
from gemini import Gemini, HEADERS
import requests
cookies = {
"key": "value"
}
session = requests.Session()
session.headers = HEADERS
session.cookies.update(cookies)
GeminiClient = Gemini(session=session, timeout=30)
response = GeminiClient.generate_content("Hello, Gemini. What's the weather like in Seoul today?")
# Continued conversation without set new session
response = GeminiClient.generate_content("What was my last prompt?")- Chat Gemini
- Get image links
- Multi-language Gemini
- Export Conversation
- Export Code to Repl.it
- Executing Python code received as a response from Gemini
- Max_token, Max_sentences
- Translation to another programming language
How to use open-source Gemma
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. Their small size enables deployment in resource-limited settings, broadening access to cutting-edge AI. 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]))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.
You can find most help on the FAQ and Issue pages. Alternatively, utilize the official Gemini API at Google AI Studio.
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.
We would like to express our sincere gratitude to all the contributors.
Core maintainers:
MIT license, 2024, Minwoo(Daniel) Park. 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] Github acheong08/Bard
[2] Github dsdanielpark/Bard-API
[3] Github GoogleCloudPlatform/generative-ai
[4] Google AI Studio
Warning Users bear all legal responsibilities when using the GeminiAPI package, which offers easy access to Google Gemini for developers. This unofficial Python package isn't affiliated with Google and may lead to Google account restrictions if used excessively or commercially due to its reliance on Google account cookies. Frequent changes in Google's interface, Google's API policies, and your country/region, as well as the status of your Google account, may affect functionality. Utilize the issue page and discussion page.
Copyright (c) 2024 Minwoo(Daniel) Park, South Korea