app.py

import streamlit as st
from PIL import Image
from ultralytics import YOLO
import cv2
import numpy as np
import matplotlib.pyplot as plt

# Load the trained YOLO model from the models folder
MODEL_PATH = 'models/best.pt'
model = YOLO(MODEL_PATH)

# Streamlit App Title
st.title("BCCD Detection App")
st.write("Upload an image to detect blood cells")

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

if uploaded_file is not None:
    # Load the uploaded image using PIL
    image = Image.open(uploaded_file)
    
    # Display the uploaded image
    st.image(image, caption="Uploaded Image", use_column_width=True)

    # Convert PIL image to OpenCV format
    image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
    
    # Perform inference using YOLO model
    results = model(image_cv)

    # Draw the bounding boxes on the image
    annotated_image = results[0].plot()

    # Convert OpenCV BGR to RGB format (for Streamlit display)
    annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)

    # Display the result with bounding boxes
    st.image(annotated_image, caption="Detected Objects", use_column_width=True)

    # Show detailed prediction results
    st.write("### Prediction Results:")
    for box in results[0].boxes:
        class_id = int(box.cls[0])
        confidence = float(box.conf[0])
        x1, y1, x2, y2 = map(int, box.xyxy[0])

        st.write(f"- **Class:** {model.names[class_id]}")
        st.write(f"- **Confidence:** {confidence:.2f}")
        st.write(f"- **Bounding Box:** ({x1}, {y1}), ({x2}, {y2})")
        st.write("---")

# Footer
st.write("Developed by [Your Name]")