src/app.py

"""
Streamlit Web UI for the Diatom Classifier Pipeline.
"""

import os
import streamlit as st
import pandas as pd
import numpy as np
from pathlib import Path
from PIL import Image, ImageDraw, ImageFont
from dotenv import load_dotenv

# Load environment variables from .env file (if it exists)
load_dotenv()

# -- Setup Environment & Paths --
APP_ENV = os.environ.get("APP_ENV", "production")
REPO_ID = "kemalbsoylu/diatom-models"
BASE_DIR = Path(__file__).resolve().parent.parent

st.set_page_config(page_title="Diatom AI", page_icon="🔬", layout="wide")

@st.cache_resource
def load_models():
    from ultralytics import YOLO
    from fastai.vision.all import load_learner

    if APP_ENV == "development":
        # LOCAL: Use models stored on your hard drive
        yolo_path = BASE_DIR / "models" / "yolo_diatom_detector.pt"
        resnet_path = BASE_DIR / "models" / "v2_resnet18_weighted.pkl"
    else:
        # PRODUCTION: Download models from the Hugging Face Hub
        from huggingface_hub import hf_hub_download
        yolo_path = hf_hub_download(repo_id=REPO_ID, filename="yolo_diatom_detector.pt")
        resnet_path = hf_hub_download(repo_id=REPO_ID, filename="v2_resnet18_weighted.pkl")

    # Load them into memory
    yolo = YOLO(yolo_path)
    resnet = load_learner(resnet_path)
    return yolo, resnet

st.title("🔬 Diatom Detection & Classification AI")
st.markdown("""
Upload a microscope image. Use **Full Slide Analysis** to automatically detect and classify multiple diatoms, 
or use **Single Diatom Crop** if you already have a cropped image of a single diatom.
""")

with st.spinner("Loading AI Models into memory..."):
    yolo_model, resnet_model = load_models()
    if APP_ENV == "development":
        st.sidebar.success("🔧 Running in Development Mode (Local Models)")

# -- Sidebar Controls --
st.sidebar.header("Configuration")
app_mode = st.sidebar.radio("Select Analysis Mode:", ["Full Slide Analysis", "Single Diatom Crop"])

conf_threshold = 0.25
if app_mode == "Full Slide Analysis":
    conf_threshold = st.sidebar.slider("Detection Confidence", 0.1, 1.0, 0.25, 0.05)
    st.sidebar.markdown("*Lowering the threshold finds more diatoms but increases false positives. (Default: 0.25)*")

# -- Sidebar Footer (Portfolio & License) --
st.sidebar.markdown("---")
st.sidebar.markdown("### About")
st.sidebar.markdown("Developed by **Kemal Soylu**")
st.sidebar.markdown("[View Source Code on GitHub](https://github.com/kemalbsoylu/diatom-classifier)")
st.sidebar.markdown("""
<small>
<b>Licenses:</b> Code (MIT), Detector (AGPL-3.0).<br>
<b>Data:</b> Trained on dataset by Gündüz et al. (CC BY-NC-SA 4.0).
</small>
""", unsafe_allow_html=True)

# -- Main File Uploader --
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Silence PyCharm type warning
    assert not isinstance(uploaded_file, list)

    original_image = Image.open(uploaded_file).convert("RGB")

    st.markdown("---")
    st.subheader("Analysis Results")

    # -----------------------------------------------------
    # MODE 1: SINGLE CROP CLASSIFICATION
    # -----------------------------------------------------
    if app_mode == "Single Diatom Crop":
        with st.spinner("Classifying diatom..."):

            # Convert standard PIL Image directly to a numpy array
            img_array = np.array(original_image)

            # Predict directly on the array
            pred_class, pred_idx, probs = resnet_model.predict(img_array)
            conf = probs[pred_idx].item() * 100

            st.success("Classification Complete!")
            st.metric(label="Predicted Genus", value=f"**{pred_class}**", delta=f"{conf:.2f}% Confidence", border=True)

            st.info("Note: This mode bypassed the automatic detector and evaluated the entire image as a single diatom. For best results, ensure your image is cropped tightly around the diatom with a maximum of 15% background margin.")

        st.markdown("---")
        st.subheader("Image Viewer")
        st.image(original_image, caption="Original Upload", use_container_width=False)

    # -----------------------------------------------------
    # MODE 2: FULL SLIDE YOLO + RESNET
    # -----------------------------------------------------
    else:
        display_image = original_image.copy()
        report_data = []
        diatom_count = 0

        with st.spinner("Scanning slide & Classifying..."):
            results = yolo_model(original_image, conf=conf_threshold, verbose=False)[0]

            draw = ImageDraw.Draw(display_image)
            try:
                font = ImageFont.truetype("arial.ttf", 24)
            except IOError:
                font = ImageFont.load_default()

            for box in results.boxes.xyxy:
                diatom_count += 1
                x1, y1, x2, y2 = map(int, box.tolist())

                # Apply 15% margin for cropping
                box_w, box_h = x2 - x1, y2 - y1
                margin_x, margin_y = int(box_w * 0.15), int(box_h * 0.15)

                crop_x1 = max(0, x1 - margin_x)
                crop_y1 = max(0, y1 - margin_y)
                crop_x2 = min(original_image.width, x2 + margin_x)
                crop_y2 = min(original_image.height, y2 + margin_y)

                cropped_img = original_image.crop((crop_x1, crop_y1, crop_x2, crop_y2))

                # Convert cropped PIL Image to a numpy array
                img_array = np.array(cropped_img)

                # Classify directly with ResNet
                pred_class, pred_idx, probs = resnet_model.predict(img_array)
                conf = probs[pred_idx].item() * 100

                # Draw bounding box on display image
                draw.rectangle([x1, y1, x2, y2], outline="red", width=3)

                # Format text with the ID matching the CSV report
                label_text = f"#{diatom_count} {pred_class} ({conf:.1f}%)"

                # Calculate text background size for readability
                left, top, right, bottom = font.getbbox(label_text)
                text_width = right - left
                text_height = bottom - top

                # Draw solid red background for text (positioned inside top-left of the box)
                draw.rectangle([x1, y1, x1 + text_width + 6, y1 + text_height + 6], fill="red")

                # Draw white text over the red background
                draw.text((x1 + 3, y1 + 3), label_text, fill="white", font=font, stroke_width=0.5, stroke_fill="white")

                # Save to report
                report_data.append({
                    "ID": diatom_count,
                    "Genus": pred_class,
                    "Confidence": f"{conf:.2f}%"
                })

        # Render Full Slide Results
        if report_data:
            word = "diatom" if diatom_count == 1 else "diatoms"
            st.success(f"Successfully found {diatom_count} {word}!")

            df = pd.DataFrame(report_data)
            st.dataframe(df, use_container_width=True)

            csv = df.to_csv(index=False).encode('utf-8')
            st.download_button(
                label="📥 Download CSV Report",
                data=csv,
                file_name=f"analysis_{uploaded_file.name}.csv",
                mime="text/csv",
            )
        else:
            st.warning("No diatoms detected. Try lowering the detection confidence threshold in the sidebar.")

        st.markdown("---")
        st.subheader("Image Viewer")
        col1, col2 = st.columns(2)
        with col1:
            st.image(original_image, caption="Original Upload", use_container_width=True)
        with col2:
            st.image(display_image, caption="Analyzed Image", use_container_width=True)

# -- Footer AI Warning --
st.markdown("---")
st.caption("**Disclaimer:** This application utilizes artificial intelligence and may produce inaccurate results. Always verify critical findings with a qualified domain expert.")