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import os
import requests
import gradio as gr
import matplotlib.pyplot as plt
from ase.build import bulk
from ase.visualize.plot import plot_atoms
from io import BytesIO
# 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}"}
# Helper function to generate structure visualization
def visualize_structure(material: str):
"""
Generate an atomic structure visualization for a given material.
Parameters:
- material (str): Chemical symbol of the material (e.g., 'Fe' for iron).
Returns:
- BytesIO object containing the image data if successful, None otherwise.
"""
try:
# Create a bulk structure; adjust 'crystalstructure' as needed
atoms = bulk(material, crystalstructure='fcc') # Default to face-centered cubic
except Exception as e:
print(f"Error creating structure for {material}: {e}")
return None
# Plot the atomic structure
fig, ax = plt.subplots(figsize=(4, 4))
plot_atoms(atoms, ax, radii=0.3, rotation=('45x,45y,0z'), show_unit_cell=2)
buf = BytesIO()
plt.tight_layout()
plt.savefig(buf, format="png")
buf.seek(0)
return buf
# Function to interact with Groq API and return 3 best materials with visuals
def recommend_materials(user_input):
"""
Recommend three materials for a given application and provide their visualizations.
Parameters:
- user_input (str): Description of the application.
Returns:
- Tuple containing:
- Recommendations and properties as a string.
- List of BytesIO objects containing images of the atomic structures.
"""
prompt = f"You are a materials science expert. Recommend the 3 best materials for the following application: '{user_input}'. " \
f"For each material, list key properties (e.g., mechanical, thermal, chemical)."
body = {
"model": "llama-3.1-8b-instant",
"messages": [{"role": "user", "content": prompt}]
}
response = requests.post(url, headers=headers, json=body)
if response.status_code != 200:
return f"Error: {response.json()}", [None, None, None]
reply = response.json()['choices'][0]['message']['content']
# Extract material names from the response
lines = reply.splitlines()
materials = []
for line in lines:
if line.strip().startswith(("1.", "2.", "3.")):
words = line.split()
if len(words) > 1:
material = words[1].strip(",.")
materials.append(material)
if len(materials) == 3:
break
# Generate visualizations for each material
images = [visualize_structure(mat) for mat in materials]
return reply, images
# Gradio Interface
interface = gr.Interface(
fn=recommend_materials,
inputs=gr.Textbox(lines=2, placeholder="e.g., High strength lightweight material for aerospace"),
outputs=[
gr.Textbox(label="Recommended Materials & Properties"),
gr.Image(label="Atomic Structure of Material 1"),
gr.Image(label="Atomic Structure of Material 2"),
gr.Image(label="Atomic Structure of Material 3")
],
title="Materials Science Expert",
description="Ask for the best materials for your application. Get 3 top recommendations with key properties and atomic structure visualizations."
)
# Launch Gradio app
if __name__ == "__main__":
interface.launch()
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