After exploring how Gemini handles rich documents, I wanted to go further: how can I turn these AI interactions into deployable apps? The "Develop GenAI Apps with Gemini and Streamlit" skill badge bridges Gemini’s backend intelligence with Streamlit’s frontend simplicity and Cloud Run’s scalability.

What I Built

1. Prompting for Text Generation

• Explored various prompting styles in Gemini via the Python SDK
• Learned to write effective system prompts, control tone, format, and safety filters
• Achieved consistent and useful responses

2. Function Calling with Gemini

• Integrated function calls, enabling Gemini to trigger code (e.g., fetch_weather(location))
• Used Gemini to call functions dynamically based on natural-language queries

3. Building a Streamlit App

• Created a clean interface for interacting with Gemini
• Included text input, model response display, and optional function outputs

4. Deploying with Cloud Run

• Containerized the app using Docker
• Pushed via Cloud Shell
• Deployed to Cloud Run for autoscaling and public access

Why It Matters

• Rapidly prototype AI solutions
• Integrate backend logic
• Share globally, instantly

This is useful for startups, hackathons, enterprise demos, or building your GenAI portfolio.

What’s Next?

Combining this with other tools like Firebase or a database backend. The ability to go from idea → LLM → full app in hours is what makes this space so exciting.

#GenAIExchange #Gemini #GoogleCloud #Streamlit #CloudRun #AIApps #LLMDeployment #PythonAI #FunctionCalling #GenerativeAI #FullStackAI