app.py

import streamlit as st
from PIL import Image
from ultralytics import YOLO
import torch

st.set_page_config(page_title="Animal Detection App", layout="centered")

# Load YOLOv8 model
@st.cache_resource
def load_model():
    return YOLO("yolov8s.pt")

model = load_model()

st.title("🐾 Animal Detection App")
st.write("Upload an image and let the YOLOv8 model detect animals!")

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

if uploaded_file:
    image = Image.open(uploaded_file).convert("RGB")
    st.image(image, caption="Uploaded Image", use_column_width=True)

    with st.spinner("Detecting..."):
        results = model(image)

        # Display detection results
       for r in results:
    rendered_img = r.plot()  # r.plot() gives the image with detections
    st.image(rendered_img, caption="Detected Image", use_container_width=True)

        result_img = Image.fromarray(results[0].plot()[:, :, ::-1])
        st.image(result_img, caption="Detected Animals", use_column_width=True)

        # Filter animal predictions
        animal_labels = ["cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "bird"]
        names = model.names
        detections = results[0].boxes.data.cpu().numpy()

        st.subheader("Detections:")
        for det in detections:
            class_id = int(det[5])
            label = names[class_id]
            if label in animal_labels:
                st.markdown(f"- **{label}** (Confidence: {det[4]:.2f})")