A smart AI assistant can be built with a combination of LangChain, the Google Gemini API, and advanced helpers, all wrapped in a Streamlit web interface. This system provides a chat console capable of web queries, Wikipedia lookups, arithmetic operations, memory recall, and full session tracking. It runs in a browser with minimal boilerplate and offers support for Python 3.10 and above alongside a modern Node.js runtime to guarantee compatibility.
Installation requires a set of Python and Node.js modules. Streamlit forms the front end, LangChain powers the agent logic, and connectors such as Wikipedia and DuckDuckGo deliver external lookup. Networking tools like ngrok or LocalTunnel handle secure forwarding for a locally hosted service. Installation commands use pip or pipx for Python modules and npm or yarn for JavaScript packages. Once libraries are in place, imports are declared and core components can be defined.
Credentials live in environment variables. A Google Gemini API key and an authentication token for the tunneling service are set before startup. Assigning these to the runtime environment lets the LangChain agent invoke the Gemini Pro model without exposing secrets in code.
The InnovativeAgentTools class bundles custom helper routines. A Calculator tool evaluates mathematical expressions safely. MemoryTools store and retrieve user context over multiple exchanges. An additional service returns the current date and time on demand. Together these elements empower the Streamlit agent to reason, recall, and reply in a contextually aware manner.
Tool registration and model integration happen in the MultiAgentSystem class. Within its initializer, Gemini Pro loads via LangChain and each utility—including web search, memory, and the calculator—is instantiated. A purpose-built prompt guides agents through the ReAct workflow, selecting the right tool at each step. The chat method processes inputs, triggers tool execution when necessary, and formats the model’s output for display.
Streamlit drives the user experience. Layout configuration and custom styling craft a polished interface. A sidebar section accepts API credentials and toggles for each tool. The main area shows conversation history, a text input field, and preset buttons for example queries on search, math, or memory. Interactions appear instantly in the browser, making the system feel like a real-time assistant.
For public exposure, a helper function sets up ngrok tunneling through the pyngrok library. The routine applies an auth token, confirms the connection, and opens a forwardable URL. If the token is absent or invalid, the code logs clear guidance on alternate approaches using LocalTunnel or Serveo, letting users host from platforms such as Google Colab without hassle.
A simple main() function serves as the entry point. It calls the Streamlit app constructor and wraps initialization in a try-except block. Any missing key or misconfigured tool triggers an informative message so that errors do not terminate the session abruptly.
A companion run_in_colab() routine automates deployment in a Google Colab notebook. It installs required packages, writes out a streamlit_app.py file containing the full application logic, and checks for a tunnel token. Fallback guidelines walk through alternate methods if needed. The overall aim is to launch the interactive interface in just a handful of notebook cells. This process reduces manual setup and keeps code consistent across multiple environments.
The final orchestration step varies based on runtime context. A start_streamlit_with_ngrok() function launches the Streamlit server in the background and calls ngrok to produce a shareable link. If that attempt does not succeed, a try_alternative_tunnels() procedure invokes LocalTunnel or Serveo. A check inside the __main__ block detects whether the code is running in Colab or on a desktop machine and selects the appropriate workflow, simplifying distribution.
Once all pieces are in place, the result is a responsive Streamlit application hosting an AI assistant. It can answer questions, perform calculations, remember user details, and even provide a public URL. By combining a modern Python framework with LangChain’s multi-tool architecture, this setup lays the groundwork for future interactive AI services.
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