Update app.py

app.py

@@ -5,7 +5,6 @@ import cv2
 import numpy as np
 from ultralytics import YOLO
 from PIL import Image
-import pandas as pd
 
 # -------------------------------
 # Streamlit Page Config
@@ -13,25 +12,20 @@ import pandas as pd
 st.set_page_config(page_title="YOLOv8 Object Detection", page_icon="🤖", layout="wide")
 
 # -------------------------------
-# App Title
+# App Title & Description
 # -------------------------------
-st.title("🤖 YOLOv8 Object Detection App")
+st.title("🤖 YOLOv8 Object Detection")
 st.markdown(
     """
-    Upload an image and let **YOLOv8** detect objects in real-time.  
-    You can adjust detection parameters in the sidebar.  
-    """
-)
+    Welcome to the **YOLOv8 Object Detection App** 🎯  
 
-# -------------------------------
-# Sidebar Controls
-# -------------------------------
-st.sidebar.header("⚙️ Detection Settings")
-confidence = st.sidebar.slider("Confidence Threshold", 0.1, 1.0, 0.25, 0.05)
-iou = st.sidebar.slider("IoU Threshold", 0.1, 1.0, 0.45, 0.05)
+    - Upload an image and let YOLOv8 detect objects in real-time.  
+    - This demo is built with **[Ultralytics YOLOv8](https://docs.ultralytics.com/)** and **[Streamlit](https://streamlit.io/)**.  
+    - Download the annotated image with detected objects.  
 
-st.sidebar.markdown("---")
-st.sidebar.info("Built with [YOLOv8](https://docs.ultralytics.com/) and [Streamlit](https://streamlit.io/)")
+    ---
+    """
+)
 
 # -------------------------------
 # Load YOLO Model
@@ -52,8 +46,9 @@ if uploaded_file is not None:
     image = Image.open(uploaded_file).convert("RGB")
     img_array = np.array(image)
 
+    # Perform Inference
     with st.spinner("🔍 Detecting objects..."):
-        results = model.predict(img_array, conf=confidence, iou=iou)
+        results = model(img_array)
 
     # Annotated results
     annotated_img = results[0].plot()
@@ -65,28 +60,8 @@ if uploaded_file is not None:
     with col2:
         st.image(annotated_img, caption="Detected Objects", use_column_width=True)
 
-    # -------------------------------
-    # Show Detection Details
-    # -------------------------------
-    st.subheader("📊 Detection Results")
-
-    if len(results[0].boxes) > 0:
-        data = []
-        for box in results[0].boxes:
-            cls = model.names[int(box.cls)]
-            conf = float(box.conf)
-            x1, y1, x2, y2 = box.xyxy[0].tolist()
-            data.append([cls, round(conf, 3), int(x1), int(y1), int(x2), int(y2)])
-
-        df = pd.DataFrame(data, columns=["Class", "Confidence", "X1", "Y1", "X2", "Y2"])
-        st.dataframe(df, use_container_width=True)
-
-        # Option to download CSV
-        csv = df.to_csv(index=False).encode("utf-8")
-        st.download_button("📥 Download Results as CSV", csv, "detections.csv", "text/csv")
-
-    else:
-        st.warning("No objects detected.")
+    # Show some info about the result
+    st.success(f"✅ Objects detected: {len(results[0].boxes)}")
 
     # Option to download the annotated image
     result_bgr = cv2.cvtColor(annotated_img, cv2.COLOR_RGB2BGR)