https://github.com/branchingbad/gemini-api-tester
This project provides a complete environment to test the Google Gemini API using both a simple Flask web frontend and a standalone Python command-line interface (CLI) script. It uses Docker/Podman for containerization to ensure a consistent and portable setup.
https://github.com/branchingbad/gemini-api-tester
dockerfile flask-application gemini gemini-ai gemini-api gemini-flash gemini-pro imagen python
Last synced: 8 months ago
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This project provides a complete environment to test the Google Gemini API using both a simple Flask web frontend and a standalone Python command-line interface (CLI) script. It uses Docker/Podman for containerization to ensure a consistent and portable setup.
- Host: GitHub
- URL: https://github.com/branchingbad/gemini-api-tester
- Owner: BranchingBad
- License: apache-2.0
- Created: 2025-09-27T02:52:47.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-09-29T23:15:53.000Z (9 months ago)
- Last Synced: 2025-10-08T01:53:03.903Z (9 months ago)
- Topics: dockerfile, flask-application, gemini, gemini-ai, gemini-api, gemini-flash, gemini-pro, imagen, python
- Language: HTML
- Homepage:
- Size: 45.9 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Gemini API Tester
This project provides a complete environment to test the Google Gemini API using both a simple **Flask web frontend** and a **standalone Python command-line interface (CLI) script**. It uses **Docker/Podman** for containerization to ensure a consistent and portable setup.
---
## The following models are currently supported
* **gemini-2.5-flash-lite (Lite)**
* **gemini-2.5-flash** (Image input optional.)
* **gemini-2.5-pro (Advanced)**
## Image Generation Model
* **gemini-2.5-flash-image-preview (Image Generation)** (Text-to-Image and conversational editing.) (Needs testing but I hit the qouta limit for the day)
---
## 1. Prerequisites
You must have the following installed:
* **Python 3.9+** (For running the `gemini-test.py` script locally).
* **Docker or Podman** (For running the web application).
* **A Gemini API Key** (Set as an environment variable).
### Setting the GEMINI_API_KEY
The backend and CLI script both require the `GEMINI_API_KEY` environment variable to be set. The API key is stored as an enviroment variable for security.
More information can be found at https://ai.google.dev/gemini-api/docs/api-key
**Linux/macOS (For current session):**
```bash
export GEMINI_API_KEY="YOUR_API_KEY_HERE"
Windows (Command Prompt):
Bash
set GEMINI_API_KEY="YOUR_API_KEY_HERE"
2. Using the CLI Script (gemini-test.py)
The standalone Python script is useful for quick, command-line testing.
Usage:
Bash
# Default model (gemini-2.5-flash)
python gemini-test.py "Explain why the sky is blue."
# Specify a different model
python gemini-test.py --model gemini-2.5-pro "Write a full whitepaper on the future of AI."
# Image Generation Example (will save a .png file)
python gemini-test.py --model gemini-2.5-flash-image-preview "A photorealistic image of a futuristic castle on a moonlit ocean."
3. Running the Web UI with Podman/Docker
The web application is containerized using the provided Dockerfile and is designed to run on port 5000 inside the container.
Step 1: Build the Container Image
This command builds the image, tags it as gemini-frontend, and passes your local GEMINI_API_KEY environment variable into the image using a build argument.
Bash
podman build -t gemini-frontend --build-arg GEMINI_API_KEY=$GEMINI_API_KEY .
# OR (for Docker)
# docker build -t gemini-frontend --build-arg GEMINI_API_KEY=$GEMINI_API_KEY .
Step 2: Run the Container
Run the image, mapping the container's internal port 5000 to an external port (e.g., 8080) on your host machine. You can manually replace $GEMINI_API_KEY with your API key if you wish. It is recomened to use an enviroment variable (attackers can check command line history for previously used keys. Ask Gemini why this is best practice if you need).
Bash
podman run -d -p 8080:5000 --name gemini-app -e GEMINI_API_KEY=$GEMINI_API_KEY gemini-frontend
# OR (for Docker)
# docker run -d -p 8080:5000 --name gemini-app gemini-frontend
Step 3: Access the Application
Open your web browser and navigate to:
http://localhost:8080
You can now use the interface to test multimodal (image + text) and text-only models, as well as the new image generation model.