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import os
import re
from functools import lru_cache
import gradio as gr
# --- Space-friendly settings ---
# Keep caches persistent if you enabled Space storage
os.environ.setdefault("HF_HOME", "/data/huggingface")
os.environ.setdefault("GRADIO_ANALYTICS_ENABLED", "false")
# ---------- Lazy model loaders (no heavy work at import time) ----------
@lru_cache(maxsize=1)
def get_embedder():
# lazy import to avoid blocking the frontend
from sentence_transformers import SentenceTransformer
return SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
@lru_cache(maxsize=1)
def get_ref_emb():
from sentence_transformers import util # imported where needed
embedder = get_embedder()
reference_text = "Patient diagnosed with anaemia and low haemoglobin."
return embedder.encode(reference_text, convert_to_tensor=True)
@lru_cache(maxsize=1)
def get_llm():
from transformers import pipeline
# keep it light; GPT-2 works on CPU
return pipeline("text-generation", model="gpt2", max_new_tokens=80)
# ------------------------ Functions per approach ------------------------
def rule_based(text: str) -> str:
if re.search(r"\banae?mia\b", text, flags=re.IGNORECASE):
return "Anaemia detected (keyword match)"
return "Anaemia not detected (no keyword match)"
def ml_based(text: str) -> str:
# load lazily (first call)
from sentence_transformers import util
embedder = get_embedder()
ref_emb = get_ref_emb()
emb = embedder.encode(text, convert_to_tensor=True)
score = float(util.cos_sim(emb, ref_emb).item())
verdict = "Anaemia likely" if score > 0.45 else "Anaemia unlikely"
return f"Similarity: {score:.2f}\n{verdict}"
def ai_based(text: str) -> str:
llm = get_llm()
prompt = (
"Determine if the following clinical note suggests anaemia. "
"Answer clearly in one short paragraph with reasoning.\n\n"
f"{text}\n"
)
out = llm(prompt)[0]["generated_text"]
return out
# ----------------------------- UI -----------------------------
def build_ui():
with gr.Blocks(title="RB vs ML vs AI — Anaemia Demo", theme=gr.themes.Soft()) as demo:
gr.Markdown("## Clinical Text Understanding — Three Approaches")
with gr.Row():
default_note = "The patient presents with fatigue and very low haemoglobin."
input_box = gr.Textbox(value=default_note, label="Clinical note", lines=4)
with gr.Tabs():
with gr.Tab("Rule-Based"):
rb_btn = gr.Button("Run Rule-Based")
rb_out = gr.Textbox(label="Result", lines=3)
rb_btn.click(fn=rule_based, inputs=input_box, outputs=rb_out)
with gr.Tab("Machine Learning (Embeddings)"):
ml_btn = gr.Button("Run ML")
ml_out = gr.Textbox(label="Result", lines=4)
ml_btn.click(fn=ml_based, inputs=input_box, outputs=ml_out)
with gr.Tab("AI / Foundation Model"):
ai_btn = gr.Button("Run AI")
ai_out = gr.Textbox(label="Result", lines=8)
ai_btn.click(fn=ai_based, inputs=input_box, outputs=ai_out)
gr.Markdown(
"Notes: models are loaded lazily on first run to keep the UI responsive in Spaces."
)
return demo
demo = build_ui()
# Queue + launch: SSR off is important for Spaces that show a blank/broken view.
demo.queue(concurrency_count=1, max_size=10).launch(
server_name="0.0.0.0",
server_port=7860,
ssr_mode=False,
show_error=True,
inbrowser=False,
share=False,
)
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