Create app.py

app.py

@@ -0,0 +1,42 @@
+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
+        results.render()  # Draw on image
+        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})")