app.py

import gc
import os
import warnings
from types import SimpleNamespace

import pandas as pd
import numpy as np
import streamlit as st
import torch

# Local imports
from generation_utils import (
    ReactionT5Dataset,
    decode_output,
    save_multiple_predictions,
)
from models import ReactionT5Yield2
from torch.utils.data import DataLoader
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from utils import seed_everything

warnings.filterwarnings("ignore")

# ------------------------------
# Page setup
# ------------------------------
st.set_page_config(
    page_title="ReactionT5",
    page_icon=None,
    layout="wide",
)

st.title("ReactionT5")
st.caption(
    "Predict reaction products, reactants, or yields from your inputs using a pretrained ReactionT5 model."
)

# ------------------------------
# Sidebar: configuration
# ------------------------------
with st.sidebar:
    st.header("Configuration")

    task = st.selectbox(
        "Task",
        options=["product prediction", "retrosynthesis prediction", "yield prediction"],
        index=0,
        help="Choose the task to run.",
    )

with st.expander("How to format your CSV", expanded=False):
    if task == "product prediction":
        st.markdown(
            """
- `REACTANT` column is required.  
- Optional columns: `REAGENT`, `SOLVENT`, `CATALYST`.  
- If a field lists multiple compounds, separate them with a dot (`.`).  
- For details, download **demo_reaction_data.csv** and check its contents.  
"""
        )
    elif task == "retrosynthesis prediction":
        st.markdown(
            """
- `PRODUCT` column is required.  
- No optional columns are used.  
- If a field lists multiple compounds, separate them with a dot (`.`).  
- For details, download **demo_retro_data.csv** and check its contents.  
"""
        )
    else:  # yield prediction
        st.markdown(
            """
- `REACTANT` and `PRODUCT` columns are required.  
- Optional columns: `REAGENT`, `SOLVENT`, `CATALYST`.  
- If a field lists multiple compounds, separate them with a dot (`.`).  
- For details, download **demo_yield_data.csv** and check its contents.  
- Output contains predicted **reaction yield** on a **0–100% scale**.
"""
        )

# ------------------------------
# Demo data download
# ------------------------------
import io


@st.cache_data(show_spinner=False)
def parse_csv_from_bytes(file_bytes: bytes) -> pd.DataFrame:
    # If your files are always UTF-8, this is fine:
    return pd.read_csv(io.BytesIO(file_bytes))
    # If you prefer explicit text decoding:
    # return pd.read_csv(io.StringIO(file_bytes.decode("utf-8")))


@st.cache_data(show_spinner=False)
def load_demo_csv_as_bytes() -> bytes:
    demo_df = pd.read_csv("data/demo_reaction_data.csv")
    return demo_df.to_csv(index=False).encode("utf-8")


st.download_button(
    label="Download demo_reaction_data.csv",
    data=load_demo_csv_as_bytes(),
    file_name="demo_reaction_data.csv",
    mime="text/csv",
    use_container_width=True,
)

st.divider()

# ------------------------------
# Sidebar: configuration
# ------------------------------
with st.sidebar:
    st.header("Configuration")
    # Model options tied to task
    if task == "product prediction":
        model_options = [
            "sagawa/ReactionT5v2-forward",
            "sagawa/ReactionT5v2-forward-USPTO_MIT",
        ]
        model_help = "Recommended models for product prediction."
        input_max_length_default = 400
        output_max_length_default = 300
        from task_forward.train import preprocess_df
    elif task == "retrosynthesis prediction":
        model_options = [
            "sagawa/ReactionT5v2-retrosynthesis",
            "sagawa/ReactionT5v2-retrosynthesis-USPTO_50k",
        ]
        model_help = "Recommended models for retrosynthesis prediction."
        input_max_length_default = 100
        output_max_length_default = 400
        from task_retrosynthesis.train import preprocess_df
    else:  # yield prediction
        model_options = ["sagawa/ReactionT5v2-yield"]  # default as requested
        model_help = "Default model for yield prediction."
        input_max_length_default = 400
        from task_yield.train import preprocess_df

    model_name_or_path = st.selectbox(
        "Model",
        options=model_options,
        index=0,
        help=model_help,
    )
    if task != "yield prediction":
        num_beams = st.slider(
            "Beam size",
            min_value=1,
            max_value=10,
            value=5,
            step=1,
            help="Number of beams for beam search.",
        )

    seed = st.number_input(
        "Random seed",
        min_value=0,
        max_value=2**32 - 1,
        value=42,
        step=1,
        help="Seed for reproducibility.",
    )

    with st.expander("Advanced generation", expanded=False):
        input_max_length = st.number_input(
            "Input max length",
            min_value=8,
            max_value=1024,
            value=input_max_length_default,
            step=8,
        )
        if task != "yield prediction":
            output_max_length = st.number_input(
                "Output max length",
                min_value=8,
                max_value=1024,
                value=output_max_length_default,
                step=8,
            )
        output_min_length = st.number_input(
            "Output min length",
            min_value=-1,
            max_value=1024,
            value=-1,
            step=1,
            help="Use -1 to let the model decide.",
        )
        batch_size = st.number_input(
            "Batch size", min_value=1, max_value=16, value=1, step=1
        )
        num_workers = st.number_input(
            "DataLoader workers",
            min_value=0,
            max_value=8,
            value=4,
            step=1,
            help="Set to 0 if multiprocessing is restricted in your environment.",
        )

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    st.caption(f"Detected device: **{device.type.upper()}**")


# ------------------------------
# Cached loaders
# ------------------------------
@st.cache_resource(show_spinner=False)
def load_tokenizer(model_ref: str):
    resolved = os.path.abspath(model_ref) if os.path.exists(model_ref) else model_ref
    return AutoTokenizer.from_pretrained(resolved, return_tensors="pt")


@st.cache_resource(show_spinner=True)
def load_model(model_ref: str, device_str: str, task: str):
    resolved = os.path.abspath(model_ref) if os.path.exists(model_ref) else model_ref
    if task != "yield prediction":
        model = AutoModelForSeq2SeqLM.from_pretrained(resolved)
    else:
        model = ReactionT5Yield2.from_pretrained(resolved)
    model.to(torch.device(device_str))
    model.eval()
    return model


@st.cache_data(show_spinner=False)
def df_to_csv_bytes(df: pd.DataFrame) -> bytes:
    return df.to_csv(index=False).encode("utf-8")


# ------------------------------
# Main interaction
# ------------------------------
left, right = st.columns([1.4, 1.0], vertical_alignment="top")

with left:
    with st.form("predict_form", clear_on_submit=False):
        uploaded = st.file_uploader(
            "Upload a CSV file with reactions",
            type=["csv"],
            accept_multiple_files=False,
            help="Must contain a REACTANT column. Optional: REAGENT, SOLVENT, CATALYST.",
        )
        run = st.form_submit_button("Predict", use_container_width=True)

    if uploaded is not None:
        try:
            file_bytes = uploaded.getvalue()
            raw_df = parse_csv_from_bytes(file_bytes)
            # raw_df = pd.read_csv(uploaded)
            st.subheader("Input preview")
            st.dataframe(raw_df.head(20), use_container_width=True)
        except Exception as e:
            st.error(f"Failed to read CSV: {e}")

with right:
    st.subheader("Notes")
    if task == "product prediction":
        st.markdown(
            f"""
    - Approximate time: about **3 seconds per reaction** when `beam size = 5` (varies by hardware).  
    - Output contains predicted **sets of reactant SMILES** and their log-likelihoods, sorted by log-likelihood (index 0 is most probable).
    """
        )
    elif task == "retrosynthesis prediction":
        st.markdown(
            f"""
    - Approximate time: about **5 seconds per reaction** when `beam size = 5` (varies by hardware).  
    - Output contains predicted **sets of reactant SMILES** and their log-likelihoods, sorted by log-likelihood (index 0 is most probable).
    """
        )
    else:  # yield prediction
        st.markdown(
            f"""
    - Approximate time: about **0.25 seconds per reaction** when `batch size = 1` (varies by hardware).  
    - Output contains predicted **reaction yield** on a **0–100% scale**.
    """
        )
    st.info(
        "In this space, CPU is used for inference. So the speed is slower than using a GPU."
    )

# ------------------------------
# Inference
# ------------------------------
if "results_df" not in st.session_state:
    st.session_state["results_df"] = None

if "last_error" not in st.session_state:
    st.session_state["last_error"] = None

if run:
    if uploaded is None:
        st.warning("Please upload a CSV file before running prediction.")
    else:
        # Build config object expected by your dataset/utils
        CFG = SimpleNamespace(
            task=task,
            num_beams=int(num_beams) if task != "yield prediction" else None,
            num_return_sequences=int(num_beams)
            if task != "yield prediction"
            else None,  # tie to beams by default
            model_name_or_path=model_name_or_path,
            input_column="input",
            input_max_length=int(input_max_length)
            if task != "yield prediction"
            else None,
            output_max_length=int(output_max_length)
            if task != "yield prediction"
            else None,
            output_min_length=int(output_min_length)
            if task != "yield prediction"
            else None,
            seed=int(seed),
            batch_size=int(batch_size),
            debug=False
        )

        seed_everything(seed=CFG.seed)

        # Load model & tokenizer
        with st.status("Loading model and tokenizer...", expanded=False) as status:
            try:
                tokenizer = load_tokenizer(CFG.model_name_or_path)
                CFG.tokenizer = tokenizer
                model = load_model(CFG.model_name_or_path, device.type, task)
                status.update(label="Model ready.", state="complete")
            except Exception as e:
                st.session_state["last_error"] = f"Failed to load model: {e}"
                status.update(label="Model load failed.", state="error")
                st.stop()

        # Prepare data
        file_bytes = uploaded.getvalue()
        input_df = parse_csv_from_bytes(file_bytes)
        if task != "yield prediction":
            input_df = preprocess_df(input_df, drop_duplicates=False)
        else:
            input_df = preprocess_df(input_df, cfg=CFG,drop_duplicates=False)

        # Dataset & loader
        dataset = ReactionT5Dataset(CFG, input_df)
        dataloader = DataLoader(
            dataset,
            batch_size=CFG.batch_size,
            shuffle=False,
            num_workers=int(num_workers),
            pin_memory=(device.type == "cuda"),
            drop_last=False,
        )

        if task == "yield prediction":
            # Use custom inference function for yield prediction
            prediction = []
            total = len(dataloader)
            progress = st.progress(0, text="Predicting yields...")
            info_placeholder = st.empty()
            for i, inputs in enumerate(dataloader, start=1):
                inputs = {k: v.to(device) for k, v in inputs.items()}
                with torch.no_grad():
                    y_preds = model(inputs)
                prediction.extend(y_preds.to("cpu").numpy())
                del y_preds
                progress.progress(i / total, text=f"Predicting yields... {i}/{total}")
                info_placeholder.caption(f"Processed batch {i} of {total}")

            prediction = np.concatenate(prediction)
            output_df = input_df.copy()
            output_df["prediction"] = prediction
            output_df["prediction"] = output_df["prediction"].clip(lower=0.0, upper=100.0)
            st.session_state["results_df"] = output_df
            st.success("Prediction complete.")
        else:
            # Generation loop with progress
            all_sequences, all_scores = [], []
            total = len(dataloader)
            progress = st.progress(0, text="Generating predictions...")
            info_placeholder = st.empty()

            for i, inputs in enumerate(dataloader, start=1):
                inputs = {k: v.to(device) for k, v in inputs.items()}
                with torch.no_grad():
                    output = model.generate(
                        **inputs,
                        min_length=CFG.output_min_length,
                        max_length=CFG.output_max_length,
                        num_beams=CFG.num_beams,
                        num_return_sequences=CFG.num_return_sequences,
                        return_dict_in_generate=True,
                        output_scores=True,
                    )
                sequences, scores = decode_output(output, CFG)
                all_sequences.extend(sequences)
                if scores:
                    all_scores.extend(scores)

                del output
                if device.type == "cuda":
                    torch.cuda.empty_cache()
                gc.collect()

                progress.progress(i / total, text=f"Generating predictions... {i}/{total}")
                info_placeholder.caption(f"Processed batch {i} of {total}")

            progress.empty()
            info_placeholder.empty()

            # Save predictions
            try:
                output_df = save_multiple_predictions(
                    input_df, all_sequences, all_scores, CFG
                )
                st.session_state["results_df"] = output_df
                st.success("Prediction complete.")
            except Exception as e:
                st.session_state["last_error"] = f"Failed to assemble output: {e}"
                st.error(st.session_state["last_error"])
                st.stop()

# ------------------------------
# Results
# ------------------------------
if st.session_state.get("results_df") is not None:
    st.subheader("Results preview")
    st.dataframe(st.session_state["results_df"].head(50), use_container_width=True)

    st.download_button(
        label="Download predictions as CSV",
        data=df_to_csv_bytes(st.session_state["results_df"]),
        file_name="output.csv",
        mime="text/csv",
        use_container_width=True,
    )

if st.session_state.get("last_error"):
    st.error(st.session_state["last_error"])