app.py

import os
import json
import numpy as np
import gradio as gr
from huggingface_hub import hf_hub_download
from tensorflow import keras
from sentence_transformers import SentenceTransformer

# -----------------------
# Config
# -----------------------
REPO_ID = "Bocklitz-Lab/lit2vec-subfield-classifier-model"
EMBED_MODEL = "intfloat/e5-large-v2"
TEXT_PREFIX = {"abstract": "abstract: ", "summary": "summary: "}
DEFAULT_THRESHOLD = 0.5
TOPK_DEFAULT = 5

# -----------------------
# Load model + labels at startup
# -----------------------
# Keras model (saved as .h5 on the Hub)
MODEL_PATH = hf_hub_download(REPO_ID, filename="mlp_model.h5")
LABEL_MAP_PATH = hf_hub_download(REPO_ID, filename="label_mapping.json")

with open(LABEL_MAP_PATH, "r", encoding="utf-8") as f:
    mapping = json.load(f)
INDEX_TO_LABEL = {int(k): v for k, v in mapping["index_to_label"].items()}

# load Keras model for inference
MODEL = keras.models.load_model(MODEL_PATH, compile=False)

# SentenceTransformer encoder (CPU-only for portability)
ENCODER = SentenceTransformer(EMBED_MODEL, device="cpu")

def encode_text(text: str, text_type: str = "abstract") -> np.ndarray:
    """Encode text into normalized embedding compatible with the classifier."""
    prefix = TEXT_PREFIX.get(text_type, "")
    emb = ENCODER.encode([prefix + text], normalize_embeddings=True)  # shape: (1, D)
    return emb.astype("float32")

def predict(text: str, text_type: str, threshold: float, topk: int):
    """Return selected labels (by threshold), top-k labels, and a scores table."""
    text = (text or "").strip()
    if not text:
        return ("", "", [])

    X = encode_text(text, text_type=text_type)  # (1, D)
    probs = MODEL.predict(X, verbose=0)[0]      # (18,)

    # Thresholded predictions
    pred_ids = [i for i, p in enumerate(probs) if p >= threshold]
    pred_labels = [INDEX_TO_LABEL[i] for i in pred_ids]
    pred_display = ", ".join(pred_labels) if pred_labels else "—"

    # Top-k predictions (by score)
    topk = max(1, int(topk))
    order = np.argsort(-probs)[:topk]
    topk_items = [f"{INDEX_TO_LABEL[i]}: {probs[i]:.3f}" for i in order]
    topk_display = "\n".join(topk_items)

    # Build a table of all scores (sorted desc)
    sorted_ids = np.argsort(-probs)
    table = [[INDEX_TO_LABEL[i], float(probs[i])] for i in sorted_ids]

    return pred_display, topk_display, table

# -----------------------
# Gradio UI
# -----------------------
with gr.Blocks(fill_height=True) as demo:
    gr.Markdown(
        """
        # Lit2Vec Subfield Classifier
        Enter a **chemistry abstract or summary**. The app encodes it with `e5-large-v2` and predicts one or more **subfields** using the MLP model.

        **Model:** `Bocklitz-Lab/lit2vec-subfield-classifier-model`  
        **Encoder:** `intfloat/e5-large-v2`
        """
    )

    with gr.Row():
        text_type = gr.Radio(
            choices=["abstract", "summary"], value="abstract", label="Text type (prefix used for encoding)"
        )
        threshold = gr.Slider(0.0, 1.0, value=DEFAULT_THRESHOLD, step=0.01, label="Decision threshold")
        topk = gr.Slider(1, 10, value=TOPK_DEFAULT, step=1, label="Top-K to display")

    input_box = gr.Textbox(
        label="Paste abstract / summary",
        placeholder="Paste your chemistry abstract here…",
        lines=12,
        value="Ultraviolet B (UVB; 290~320nm) irradiation-induced lipid peroxidation induces inflammatory responses that lead to skin wrinkle formation and epidermal thickening. Peroxisome proliferator-activated receptor (PPAR) α/γ dual agonists have the potential to be used as anti-wrinkle agents because they inhibit inflammatory response and lipid peroxidation. In this study, we evaluated the function of 2-bromo-4-(5-chloro-benzo[d]thiazol-2-yl) phenol (MHY 966), a novel synthetic PPAR α/γ dual agonist, and investigated its anti-inflammatory and anti-lipid peroxidation effects. The action of MHY 966 as a PPAR α/γ dual agonist was also determined in vitro by reporter gene assay. Additionally, 8-week-old melanin-possessing hairless mice 2 (HRM2) were exposed to 150 mJ/cm2 UVB every other day for 17 days and MHY 966 was simultaneously pre-treated every day for 17 days to investigate the molecular mechanisms involved. MHY 966 was found to stimulate the transcriptional activities of both PPAR α and γ. In HRM2 mice, we found that the skins of mice exposed to UVB showed significantly increased pro-inflammatory mediator levels (NF-κB, iNOS, and COX-2) and increased lipid peroxidation, whereas MHY 966 co-treatment down-regulated these effects of UVB by activating PPAR α and γ. Thus, the present study shows that MHY 966 exhibits beneficial effects on inflammatory responses and lipid peroxidation by simultaneously activating PPAR α and γ. The major finding of this study is that MHY 966 demonstrates potential as an agent against wrinkle formation associated with chronic UVB exposure."
    )

    run_btn = gr.Button("Predict subfield(s)")

    with gr.Row():
        selected_labels = gr.Textbox(label="Predicted fields (thresholded)", lines=2)
        topk_labels = gr.Textbox(label="Top-K (scores)", lines=6)

    scores_table = gr.Dataframe(
        headers=["Subfield", "Score"],
        datatype=["str", "number"],
        label="All scores (sorted)",
        interactive=False
    )

    run_btn.click(
        fn=predict,
        inputs=[input_box, text_type, threshold, topk],
        outputs=[selected_labels, topk_labels, scores_table]
    )

if __name__ == "__main__":
    # On Spaces, Gradio sets host/port; keep defaults.
    demo.launch()