import os
import sys
import json
import pandas as pd
import gradio as gr

# 1) Adjust path before importing the loader
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
INFERENCE_PATH = os.path.join(BASE_DIR, "smi-ted", "inference")
sys.path.insert(0, INFERENCE_PATH)

from smi_ted_light.load import load_smi_ted

# 2) Load the SMI-TED Light model
MODEL_DIR = os.path.join(INFERENCE_PATH, "smi_ted_light")
model = load_smi_ted(
    folder=MODEL_DIR,
    ckpt_filename="smi-ted-Light_40.pt",
    vocab_filename="bert_vocab_curated.txt",
)

# 3) Single function to process either a single SMILES or a CSV of SMILES
def process_inputs(smiles: str, file_obj):
    # If a CSV file is provided, process in batch
    if file_obj is not None:
        try:
            df_in = pd.read_csv(file_obj.name)
            smiles_list = df_in.iloc[:, 0].astype(str).tolist()
            embeddings = []
            for sm in smiles_list:
                vec = model.encode(sm, return_torch=True)[0].tolist()
                embeddings.append(vec)
            # Build output DataFrame
            out_df = pd.DataFrame(embeddings)
            out_df.insert(0, "smiles", smiles_list)
            out_df.to_csv("embeddings.csv", index=False)
            msg = f"Processed batch of {len(smiles_list)} SMILES. Download embeddings.csv."
            return msg, gr.update(value="embeddings.csv", visible=True)
        except Exception as e:
            return f"Error processing batch: {e}", gr.update(visible=False)

    # Otherwise, process a single SMILES
    smiles = smiles.strip()
    if not smiles:
        return "Please enter a SMILES or upload a CSV file.", gr.update(visible=False)
    try:
        vec = model.encode(smiles, return_torch=True)[0].tolist()
        # Save CSV with header
        cols = ["smiles"] + [f"dim_{i}" for i in range(len(vec))]
        df_out = pd.DataFrame([[smiles] + vec], columns=cols)
        df_out.to_csv("embeddings.csv", index=False)
        return json.dumps(vec), gr.update(value="embeddings.csv", visible=True)
    except Exception as e:
        return f"Error extracting embedding: {e}", gr.update(visible=False)

# 4) Build the Gradio Blocks interface
with gr.Blocks() as demo:
    gr.Markdown(
        """
        # SMI-TED-Embeddings-Extraction
        **Single mode:** paste a SMILES string in the left box.  
        **Batch mode:** upload a CSV file where each row has a SMILES in the first column.  
        In both cases, an `embeddings.csv` file will be extracted for download, with the first column as SMILES and the embedding values in the following columns.
        """
    )

    with gr.Row():
        smiles_in = gr.Textbox(label="SMILES (single mode)", placeholder="e.g. CCO")
        file_in   = gr.File(label="SMILES CSV (batch mode)", file_types=[".csv"])

    generate_btn = gr.Button("Extract Embeddings")

    with gr.Row():
        output_msg   = gr.Textbox(label="Message / Embedding (JSON)", interactive=False, lines=2)
        download_csv = gr.File(label="Download embeddings.csv", visible=False)

    generate_btn.click(
        fn=process_inputs,
        inputs=[smiles_in, file_in],
        outputs=[output_msg, download_csv]
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0")