Spaces:
Sleeping
Sleeping
File size: 2,188 Bytes
8f0f112 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | # =============================================================================
# BacSense v2 — Usage Examples
# pip install -r requirements.txt
# =============================================================================
from inference import BacSense
# ── Basic usage ───────────────────────────────────────────────────
model = BacSense("bacsense_v2_package")
model.warmup() # pre-load at startup (optional but recommended)
result = model.predict("image.png", verbose=True)
print(result["prediction"]) # Escherichia coli
print(result["confidence"]) # 0.9621
print(result["gram"]) # Negative
print(result["shape"]) # Rod
print(result["risk"]) # High
# ── Batch prediction ──────────────────────────────────────────────
results = model.predict_batch(["img1.png", "img2.png", "img3.png"])
for r in results:
print(r["prediction"], r["confidence"])
# ── Streamlit app ─────────────────────────────────────────────────
# import streamlit as st
# from inference import BacSense
#
# @st.cache_resource
# def load_model():
# m = BacSense("bacsense_v2_package")
# m.warmup()
# return m
#
# model = load_model()
# st.title("BacSense v2 — Bacterial Classifier")
# uploaded = st.file_uploader("Upload microscopy image", type=["png","jpg","jpeg"])
# if uploaded:
# with open("temp_input.png", "wb") as f:
# f.write(uploaded.read())
# result = model.predict("temp_input.png", verbose=True)
# st.success(f"Prediction: {result['prediction']}")
# st.metric("Confidence", f"{result['confidence']:.2%}")
# col1, col2, col3 = st.columns(3)
# col1.metric("Gram Stain", result["gram"])
# col2.metric("Shape", result["shape"])
# col3.metric("Risk Level", result["risk"])
# if result["routed_to_specialist"]:
# st.info("Specialist classifier was used for E.coli / P.aeruginosa disambiguation")
|