app.py

# Import librairies
from pathlib import Path
from typing import Optional
from uuid import uuid4
import hashlib
import json
import gradio as gr
import gemmi
from gradio_molecule3d import Molecule3D
from modal_app import app, chai1_inference, download_inference_dependencies, here

# Definition of the tools for the MCP server 

# Function to return a fasta file
def create_fasta_file(sequence: str, name: Optional[str] = None) -> str:
    """Create a FASTA file from a protein sequence string with a unique name.
    
    Args:
        sequence (str): The protein sequence string with optional line breaks
        name (str, optional): The name/identifier for the sequence. Defaults to "PROTEIN"
    
    Returns:
        str: Name of the created FASTA file
    """
    # Remove any trailing/leading whitespace but preserve line breaks
    lines = sequence.strip().split('\n')
    
    # Check if the first line is a FASTA header
    if not lines[0].startswith('>'):
        # If no header provided, add one
        if name is None:
            name = "PROTEIN"
        sequence = f">{name}\n{sequence}"
    
    # Create FASTA content (preserving line breaks)
    fasta_content = sequence
    
    # Generate a unique file name
    unique_id = hashlib.sha256(uuid4().bytes).hexdigest()[:8]
    file_name = f"chai1_{unique_id}_input.fasta"
    file_path = here / "inputs" / file_name
    
    # Write the FASTA file
    with open(file_path, "w") as f:
        f.write(fasta_content)
    
    return file_name

# Function to create a custom JSON config file
def create_custom_config(
    num_trunk_recycles: int = 3,
    num_diffn_timesteps: int = 200,
    seed: int = 42,
    use_esm_embeddings: bool = True,
    use_msa_server: bool = True,
    output_file: Optional[str] = None
) -> str:
    """Create a custom JSON configuration file for Chai1 inference.
    
    Args:
        num_trunk_recycles (int, optional): Number of trunk recycles. Defaults to 3.
        num_diffn_timesteps (int, optional): Number of diffusion timesteps. Defaults to 200.
        seed (int, optional): Random seed. Defaults to 42.
        use_esm_embeddings (bool, optional): Whether to use ESM embeddings. Defaults to True.
        use_msa_server (bool, optional): Whether to use MSA server. Defaults to True.
        output_file (str, optional): Path to save the config file. If None, saves to default location.
    
    Returns:
        str: Path to the created config file
    """
    config = {
        "num_trunk_recycles": num_trunk_recycles,
        "num_diffn_timesteps": num_diffn_timesteps,
        "seed": seed,
        "use_esm_embeddings": use_esm_embeddings,
        "use_msa_server": use_msa_server
    }
    
    if output_file is None:
        output_file = here / "inputs" / "chai1_custom_inference.json"
        
    with open(output_file, "w") as f:
        json.dump(config, f, indent=4)
        
    return str(output_file)

# Function to compute Chai1 inference
def compute_Chai1(
    fasta_file: Optional[str] = "",
    inference_config_file: Optional[str] = "",
):
    """Compute a Chai1 simulation.

    Args:
        x (float | int): The number to square.

    Returns:
        float: The square of the input number.
    """
    with app.run():
        
        force_redownload = False
        
        print("🧬 checking inference dependencies")
        download_inference_dependencies.remote(force=force_redownload)

        # Define fasta file
        if not fasta_file:
            fasta_file = here / "inputs" / "chai1_default_input.fasta"   
        print(f"🧬 running Chai inference on {fasta_file}")
        fasta_file = here / "inputs" / fasta_file
        print(fasta_file)
        fasta_content = Path(fasta_file).read_text()

        # Define inference config file
        if not inference_config_file:
            inference_config_file = here / "inputs" / "chai1_quick_inference.json"
        print(f"🧬 loading Chai inference config from {inference_config_file}")
        inference_config = json.loads(Path(inference_config_file).read_text())

        # Generate a unique run ID
        run_id = hashlib.sha256(uuid4().bytes).hexdigest()[:8]  # short id
        print(f"🧬 running inference with {run_id=}")

        results = chai1_inference.remote(fasta_content, inference_config, run_id)

        # Define output directory
        output_dir = Path("./results")
        output_dir.mkdir(parents=True, exist_ok=True)

        print(f"🧬 saving results to disk locally in {output_dir}")
        for ii, (scores, cif) in enumerate(results):
            (Path(output_dir) / f"{run_id}-scores.model_idx_{ii}.npz").write_bytes(scores)
            (Path(output_dir) / f"{run_id}-preds.model_idx_{ii}.cif").write_text(cif)
        
        # Take the last cif file and convert it to pdb
        cif_name = str(output_dir)+"/"+str(run_id)+"-preds.model_idx_"+str(ii)+".cif"
        pdb_name = cif_name.split('.cif')[0] + '.pdb'
        st = gemmi.read_structure(cif_name)
        st.write_minimal_pdb(pdb_name)
        
        return pdb_name


# Create the Gradio interface
reps = [{"model": 0,"style": "cartoon","color": "hydrophobicity"}]

with gr.Blocks() as demo:
    
    gr.Markdown(
    """
    # Chai1 Simulation Interface
    This interface allows you to run Chai1 simulations on a given Fasta sequence file.     
    """) 
    
    with gr.Tab("Configuration 📦"):
        text_input = gr.Textbox(placeholder="Fasta format sequences", label="Fasta content", lines=10)
        text_output = gr.Textbox(placeholder="Fasta file name", label="Fasta file name")
        text_button = gr.Button("Create Fasta file")
        text_button.click(fn=create_fasta_file, inputs=[text_input], outputs=[text_output])
        
        gr.Markdown(
        """
        You can input a Fasta file containing the sequence of the molecule you want to simulate.
        The output will be a 3D representation of the molecule based on the Chai1 model.
        ## Instructions
        1. Upload a Fasta sequence file containing the molecule sequence.
        2. Click the "Run" button to start the simulation.
        3. The output will be a 3D visualization of the molecule.
        ## Example Input
        You can use the default Fasta file provided in the inputs directory, or upload your own.
        ## Output
        The output will be a 3D representation of the molecule, which you can interact with.
        ## Note
        Make sure to have the necessary dependencies installed and the Chai1 model available in the specified directory.
        ## Disclaimer
        This interface is for educational and research purposes only. The results may vary based on the input sequence and the Chai1 model's capabilities.
        ## Contact
        For any issues or questions, please contact the developer or refer to the documentation.
        ## Example Fasta File
        ```
        >protein|name=example-protein
        AGSHSMRYFSTSVSRPGRGEPRFIAVGYVDDTQFVRFD      
        """) 
       
    with gr.Tab("Run folding simulation 🚀"): 
        inp1 = gr.Textbox(placeholder="Fasta Sequence file", label="Input Fasta file")
        inp2 = gr.Textbox(placeholder="Config file", label="JSON Config file")
        btn = gr.Button("Run")
        out = Molecule3D(label="Molecule3D", reps=reps)
        btn.click(fn=compute_Chai1, inputs=[inp1 , inp2], outputs=[out])

# Launch both the Gradio web interface and the MCP server
if __name__ == "__main__":
    demo.launch(mcp_server=True)