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import os | |
import requests | |
import gradio as gr | |
# Retrieve the API key from the environment variable | |
groq_api_key = os.getenv("GROQ_API_KEY") | |
if not groq_api_key: | |
raise ValueError("GROQ_API_KEY is missing! Set it in the Hugging Face Spaces 'Secrets'.") | |
# Define the API endpoint and headers | |
url = "https://api.groq.com/openai/v1/chat/completions" | |
headers = {"Authorization": f"Bearer {groq_api_key}"} | |
# Function to interact with Groq API | |
def chat_with_groq(user_input): | |
# Check if question is related to materials science | |
keywords = [ | |
"material", "materials", "alloy", "composite", "polymer", "ceramic", | |
"application", "mechanical properties", "thermal properties", "corrosion", | |
"creep", "fatigue", "strength", "tensile", "impact", "fracture", "modulus" | |
] | |
if not any(word in user_input.lower() for word in keywords): | |
return "β οΈ I am an expert in Materials Science, ask me anything about it and I will try my best to answer. Anything outside, feel free to use ChatGPT! π" | |
system_prompt = ( | |
"You are an expert materials scientist. When a user asks about the best materials for a specific application, " | |
"provide the top 3 material choices. First, list the key properties required for that application. Then show a clean, " | |
"side-by-side comparison in markdown table format of the three materials, with the properties as rows and materials as columns. " | |
"Include their relevant mechanical, thermal, and chemical properties. Conclude with a brief summary of which might be best depending on the scenario." | |
) | |
body = { | |
"model": "llama-3.1-8b-instant", | |
"messages": [ | |
{"role": "system", "content": system_prompt}, | |
{"role": "user", "content": user_input} | |
] | |
} | |
response = requests.post(url, headers=headers, json=body) | |
if response.status_code == 200: | |
return response.json()['choices'][0]['message']['content'] | |
else: | |
return f"Error: {response.json()}" | |
# Build Gradio interface with better layout and custom styling | |
with gr.Blocks(title="Materials Science Expert Chatbot", css=""" | |
#orange-btn { | |
background-color: #f97316 !important; | |
color: white !important; | |
border: none; | |
font-weight: bold; | |
} | |
""") as demo: | |
gr.Markdown("## π§ͺ Materials Science Expert\nAsk about the best materials for any engineering or industrial application.") | |
with gr.Row(): | |
with gr.Column(scale=3): | |
user_input = gr.Textbox( | |
lines=2, | |
placeholder="e.g. Best materials for high-temperature turbine blades...", | |
label="Ask your question" | |
) | |
with gr.Column(scale=1, min_width=100): | |
submit_btn = gr.Button("Submit", variant="primary", elem_id="orange-btn") | |
gr.Markdown("#### π Popular Materials Science related questions") | |
gr.Markdown(""" | |
- What are the best corrosion-resistant materials for marine environments (e.g., desalination)? | |
- Which materials are ideal for solar panel coatings and desert heat management? | |
- What materials are used for aerospace structures in extreme climates? | |
- Best high-strength materials for construction in the Gulf region? | |
- What advanced materials are used in electric vehicles and batteries in the UAE? | |
- How can one leverage AI/ML techniques in Materials Science? | |
- Iβm a recent high school graduate interested in science. How can I explore Materials Science with AI/ML? | |
- ------------------------------------------------------------------------- | |
""") | |
output = gr.Markdown() | |
submit_btn.click(chat_with_groq, inputs=user_input, outputs=output) | |
# Launch the app | |
if __name__ == "__main__": | |
demo.launch() | |