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()