S2FApp/app.py

"""
Shape2Force (S2F) - GUI for force map prediction from bright field microscopy images.
"""
import os
import sys
import io
import cv2
cv2.utils.logging.setLogLevel(cv2.utils.logging.LOG_LEVEL_ERROR)

import numpy as np
import streamlit as st
from PIL import Image
import plotly.graph_objects as go
from plotly.subplots import make_subplots

# Ensure S2F is in path
S2F_ROOT = os.path.dirname(os.path.abspath(__file__))
if S2F_ROOT not in sys.path:
    sys.path.insert(0, S2F_ROOT)

from predictor import S2FPredictor
from utils.substrate_settings import list_substrates

st.set_page_config(page_title="Shape2Force (S2F)", page_icon="🔬", layout="centered")
st.markdown("""
    <style>
    section[data-testid="stSidebar"] { width: 380px !important; }
    </style>
    """, unsafe_allow_html=True)
st.title("🔬 Shape2Force (S2F)")
st.caption("Predict force maps from bright field microscopy images")

# Folders
ckp_folder = os.path.join(S2F_ROOT, "ckp")
sample_folder = os.path.join(S2F_ROOT, "sample")
ckp_files = []
if os.path.isdir(ckp_folder):
    ckp_files = sorted([f for f in os.listdir(ckp_folder) if f.endswith(".pth")])
SAMPLE_EXTENSIONS = (".tif", ".tiff", ".png", ".jpg", ".jpeg")
sample_files = []
if os.path.isdir(sample_folder):
    sample_files = sorted([f for f in os.listdir(sample_folder)
                          if f.lower().endswith(SAMPLE_EXTENSIONS)])

# Sidebar: model configuration
with st.sidebar:
    st.header("Model configuration")
    model_type = st.radio(
        "Model type",
        ["single_cell", "spheroid"],
        format_func=lambda x: "Single cell" if x == "single_cell" else "Spheroid",
        horizontal=False,
    )

    if ckp_files:
        checkpoint = st.selectbox(
            "Checkpoint",
            ckp_files,
            help="Select a .pth file from the ckp folder",
        )
    else:
        st.warning("No .pth files in ckp/ folder. Add checkpoints to load.")
        checkpoint = None

    substrate_config = None
    substrate_val = "fibroblasts_PDMS"
    use_manual = False
    if model_type == "single_cell":
        try:
            substrates = list_substrates()
            substrate_val = st.selectbox(
                "Substrate (from config)",
                substrates,
                help="Select a preset from config/substrate_settings.json",
            )
            use_manual = st.checkbox("Enter substrate values manually", value=False)
            if use_manual:
                st.caption("Enter pixelsize (µm/px) and Young's modulus (Pa)")
                manual_pixelsize = st.number_input("Pixelsize (µm/px)", min_value=0.1, max_value=50.0,
                                                   value=3.0769, step=0.1, format="%.4f")
                manual_young = st.number_input("Young's modulus (Pa)", min_value=100.0, max_value=100000.0,
                                               value=6000.0, step=100.0, format="%.0f")
                substrate_config = {"pixelsize": manual_pixelsize, "young": manual_young}
            else:
                substrate_config = None
        except FileNotFoundError:
            st.error("config/substrate_settings.json not found")

    st.divider()
    st.subheader("Display")
    display_size = st.slider("Image size (px)", min_value=200, max_value=800, value=350, step=50,
                             help="Adjust display size. Drag to pan, scroll to zoom.")

    st.divider()

# Main area: image input
img_source = st.radio("Image source", ["Upload", "Sample"], horizontal=True, label_visibility="collapsed")
img = None
uploaded = None
selected_sample = None

if img_source == "Upload":
    uploaded = st.file_uploader(
        "Upload bright field image",
        type=["tif", "tiff", "png", "jpg", "jpeg"],
        help="Bright field microscopy image (grayscale or RGB)",
    )
    if uploaded:
        bytes_data = uploaded.read()
        nparr = np.frombuffer(bytes_data, np.uint8)
        img = cv2.imdecode(nparr, cv2.IMREAD_GRAYSCALE)
        uploaded.seek(0)  # reset for potential re-read
else:
    if sample_files:
        selected_sample = st.selectbox(
            "Select sample image",
            sample_files,
            format_func=lambda x: x,
        )
        if selected_sample:
            sample_path = os.path.join(sample_folder, selected_sample)
            img = cv2.imread(sample_path, cv2.IMREAD_GRAYSCALE)
        # Show sample thumbnails
        st.caption("Sample images (add more to the `sample/` folder)")
        n_cols = min(4, len(sample_files))
        cols = st.columns(n_cols)
        for i, fname in enumerate(sample_files[:8]):  # show up to 8
            with cols[i % n_cols]:
                path = os.path.join(sample_folder, fname)
                sample_img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
                if sample_img is not None:
                    st.image(sample_img, caption=fname, width='content')
    else:
        st.info("No sample images found. Add images to the `sample/` folder, or use Upload.")

run = st.button("Run prediction", type="primary")
has_image = img is not None

if run and checkpoint and has_image:
    with st.spinner("Loading model and predicting..."):
        try:
            predictor = S2FPredictor(
                model_type=model_type,
                checkpoint_path=checkpoint,
                ckp_folder=ckp_folder,
            )
            if img is not None:
                sub_val = substrate_val if model_type == "single_cell" and not use_manual else "fibroblasts_PDMS"
                heatmap, force, pixel_sum = predictor.predict(
                    image_array=img,
                    substrate=sub_val,
                    substrate_config=substrate_config if model_type == "single_cell" else None,
                )

                st.success("Prediction complete!")

                # Metrics
                col1, col2, col3, col4 = st.columns(4)
                with col1:
                    st.metric("Sum of all pixels", f"{pixel_sum:.2f}")
                with col2:
                    st.metric("Cell force (scaled)", f"{force:.2f}")
                with col3:
                    st.metric("Heatmap max", f"{np.max(heatmap):.4f}")
                with col4:
                    st.metric("Heatmap mean", f"{np.mean(heatmap):.4f}")

                # Visualization - Plotly with zoom/pan
                fig_pl = make_subplots(rows=1, cols=2, subplot_titles=["", ""])
                fig_pl.add_trace(go.Heatmap(z=img, colorscale="gray", showscale=False), row=1, col=1)
                fig_pl.add_trace(go.Heatmap(z=heatmap, colorscale="Jet", zmin=0, zmax=1, showscale=True), row=1, col=2)
                fig_pl.update_layout(
                    height=display_size,
                    margin=dict(l=10, r=10, t=10, b=10),
                    xaxis=dict(scaleanchor="y", scaleratio=1),
                    xaxis2=dict(scaleanchor="y2", scaleratio=1),
                )
                fig_pl.update_xaxes(showticklabels=False)
                fig_pl.update_yaxes(showticklabels=False, autorange="reversed")
                st.plotly_chart(fig_pl, use_container_width=True)

                # Download
                heatmap_uint8 = (np.clip(heatmap, 0, 1) * 255).astype(np.uint8)
                heatmap_rgb = cv2.applyColorMap(heatmap_uint8, cv2.COLORMAP_JET)
                heatmap_rgb = cv2.cvtColor(heatmap_rgb, cv2.COLOR_BGR2RGB)
                pil_heatmap = Image.fromarray(heatmap_rgb)
                buf_hm = io.BytesIO()
                pil_heatmap.save(buf_hm, format="PNG")
                buf_hm.seek(0)
                st.download_button("Download Heatmap", data=buf_hm.getvalue(),
                                   file_name="s2f_heatmap.png", mime="image/png")

        except Exception as e:
            st.error(f"Prediction failed: {e}")
            import traceback
            st.code(traceback.format_exc())

elif run and not checkpoint:
    st.warning("Please add checkpoint files to the ckp/ folder and select one.")
elif run and not has_image:
    st.warning("Please upload an image or select a sample.")

# Footer
st.sidebar.divider()
st.sidebar.caption("Place .pth checkpoints in the ckp/ folder")