import gradio as gr import numpy as np import agent import os css_style = """ .gradio-container { font-family: "IBM Plex Mono"; } """ def agent_run(q, openai_api_key, mapi_api_key): os.environ["OPENAI_API_KEY"]=openai_api_key os.environ["MAPI_API_KEY"]=mapi_api_key agent_chain = agent.Agent(openai_api_key, mapi_api_key) try: out = agent_chain.run(q) except Exception as err: out = f"Something went wrong, please try again.\nError: {err}" return out with gr.Blocks(css=css_style) as demo: gr.Markdown(f''' # A LLM application developed during the LLM March *MADNESS* Hackathon - Developed by: Mayk Caldas ([@maykcaldas](https://github.com/maykcaldas)) and Sam Cox ([@SamCox822](https://github.com/SamCox822)) ## What is this? - This is a demo of a LLM agent that can answer questions about materials science using the [LangChain🦜️🔗](https://github.com/hwchase17/langchain/) and the [Materials Project API](https://materialsproject.org/). - Its behave is based on Large Language Models (LLM) and aim to be a tool to help scientists with quick predictions of a nunerous of properties of materials. It is a work in progress, so please be patient with it. ### Some keys are needed in order to use it: 1. An openAI API key ( [Check it here](https://platform.openai.com/account/api-keys) ) 2. A material project's API key ( [Check it here](https://materialsproject.org/api#api-key) ) ''') with gr.Accordion("List of properties we developed tools for", open=False): gr.Markdown(f""" Classification tasks: Stability, magnetism, gap_direct, metal, regression tasks: band_gap, volume, density, atomic_density, formation energy per atom, energy per atom, electronic energy, ionic energy, total energy """) openai_api_key = gr.Textbox( label="OpenAI API Key", placeholder="sk-...", type="password") mapi_api_key = gr.Textbox( label="Material Project API Key", placeholder="...", type="password") with gr.Tab("MAPI Query"): text_input = gr.Textbox(label="", placeholder="Enter question here...") text_output = gr.Textbox() text_button = gr.Button("Query!") text_button.click(agent_run, inputs=[text_input, openai_api_key, mapi_api_key], outputs=text_output) demo.launch()