Create app.py

app.py

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+import os
+import urllib.request
+import cv2
+import numpy as np
+from ultralytics import YOLO
+import gradio as gr
+
+# --- Setup writable paths for YOLO ---
+os.environ["YOLO_CONFIG_DIR"] = "/tmp"
+os.environ["HOME"] = "/tmp"
+
+# --- Download YOLOv8s weights if not already present ---
+MODEL_PATH = "/tmp/yolov8s.pt"
+if not os.path.exists(MODEL_PATH):
+    print("Downloading YOLOv8s weights...")
+    urllib.request.urlretrieve(
+        "https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8s.pt",
+        MODEL_PATH
+    )
+
+# --- Load the YOLOv8 model ---
+model = YOLO(MODEL_PATH)
+
+# --- Detection function ---
+def detect_objects(image):
+    """
+    Input: image (BGR)
+    Output: annotated image, detected object names
+    """
+    # Convert BGR (OpenCV) to RGB
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Resize for consistent detection
+    image_resized = cv2.resize(image_rgb, (640, 640))
+
+    # Run YOLO inference with confidence threshold 0.2
+    results = model(image_resized, conf=0.2)
+
+    # Annotated image
+    annotated_image = results[0].plot()
+
+    # Extract detected class names
+    if results[0].boxes is not None:
+        detected_classes = [model.names[int(c)] for c in results[0].boxes.cls]
+        detected_text = ", ".join(detected_classes) if detected_classes else "No objects detected"
+    else:
+        detected_text = "No objects detected"
+
+    return annotated_image, detected_text
+
+# --- Gradio Interface ---
+demo = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=[
+        gr.Image(type="numpy", label="Detected Objects"),
+        gr.Textbox(label="Objects Detected")
+    ],
+    title="🧠 Object Detection App",
+    description="Upload an image — YOLOv8s detects all objects and lists their names!"
+)
+
+# --- Launch the app ---
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)