Create app.py

app.py

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+import cv2
+import numpy as np
+import gradio as gr
+import os
+
+# ✅ Load YOLO model files (Ensure these files are uploaded to the Space)
+yolo_config = "yolov3.cfg"
+yolo_weights = "yolov3.weights"
+yolo_classes = "coco.names"
+
+# ✅ Load class labels
+with open(yolo_classes, "r") as f:
+    classes = [line.strip() for line in f.readlines()]
+
+# ✅ Load YOLO model
+net = cv2.dnn.readNet(yolo_weights, yolo_config)
+layer_names = net.getLayerNames()
+output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
+
+# ✅ Object Detection Function
+def detect_objects(image):
+    img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  # Convert from RGB to BGR
+    height, width, _ = img.shape
+
+    # Convert image to blob
+    blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
+    net.setInput(blob)
+    outs = net.forward(output_layers)
+
+    # Process detected objects
+    class_ids, confidences, boxes = [], [], []
+    for out in outs:
+        for detection in out:
+            scores = detection[5:]
+            class_id = np.argmax(scores)
+            confidence = scores[class_id]
+            if confidence > 0.5:
+                center_x, center_y, w, h = (detection[0:4] * [width, height, width, height]).astype("int")
+                x = int(center_x - w / 2)
+                y = int(center_y - h / 2)
+                boxes.append([x, y, w, h])
+                confidences.append(float(confidence))
+                class_ids.append(class_id)
+
+    # Non-maximum suppression
+    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
+    colors = np.random.uniform(0, 255, size=(len(classes), 3))
+
+    # Draw bounding boxes
+    for i in indexes.flatten():
+        x, y, w, h = boxes[i]
+        label = f"{classes[class_ids[i]]}: {confidences[i]:.2f}"
+        color = colors[class_ids[i]]
+        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
+        cv2.putText(img, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert back to RGB
+    return img_rgb
+
+# ✅ Gradio Interface
+demo = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Image(type="numpy"),
+    title="YOLOv3 Object Detection",
+    description="Upload an image to detect objects using YOLOv3.",
+)
+
+# ✅ Launch Gradio App
+demo.launch()