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| 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, serp_api_key): | |
| os.environ["OPENAI_API_KEY"]=openai_api_key | |
| os.environ["MAPI_API_KEY"]=mapi_api_key | |
| os.environ["SERPAPI_API_KEY"]=serp_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 an app that can answer questions about material science using the [LangChain🦜️🔗](https://github.com/hwchase17/langchain/) and the [Materials Project API](https://materialsproject.org/). | |
| - Its behavior is based on Large Language Models (LLM), and it aims to be a tool to help scientists with quick predictions of numerous properties of materials. | |
| It is a work in progress, so please be patient with it. We are working on a systematic validation. | |
| ### Some keys are needed 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) ) | |
| 3. A SERP API key ( [Check it here](https://serpapi.com/account-api) ) | |
| - Only used if the chain runs a web search to answer the question. | |
| ''') | |
| with gr.Accordion("List of properties we developed tools for", open=False): | |
| gr.Markdown(f""" | |
| - Classification tasks: "Is the material AnByCz stable?" | |
| - Stable, | |
| - Magnetic, | |
| - Gap direct, and | |
| - Metal. | |
| - Regression tasks: "What is the band gap of the material AnByCz?" | |
| - Band gap, | |
| - Volume, | |
| - Density, | |
| - Atomic density, | |
| - Formation energy per atom, | |
| - Energy per atom, | |
| - Electronic energy, | |
| - Ionic energy, and | |
| - Total energy. | |
| - Reaction procedure for synthesis proposal: "Give me a reaction procedure to synthesize the material AnByCz"(under development) | |
| """) | |
| 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") | |
| serp_api_key = gr.Textbox( | |
| label="Serp API Key", placeholder="...", type="password") | |
| with gr.Tab("MAPI Query"): | |
| text_input = gr.Textbox(label="", placeholder="Enter question here...") | |
| text_output = gr.Textbox(placeholder="Your answer will appear here...") | |
| text_button = gr.Button("Ask!") | |
| text_button.click(agent_run, inputs=[text_input, openai_api_key, mapi_api_key, serp_api_key], outputs=text_output) | |
| demo.launch() | |