Create app.py

app.py

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+import cv2
+import numpy as np
+import torch
+import gradio as gr
+from ultralytics import YOLO
+
+# Load YOLOv12x model
+MODEL_PATH = "yolov12x.pt"  # Ensure the model is uploaded to the Hugging Face Space
+model = YOLO(MODEL_PATH)
+
+# COCO dataset class IDs
+PERSON_CLASS_ID = 0  # "person"
+TRUCK_CLASS_ID = 7    # "truck"
+
+def count_objects(video_path):
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        return "Error: Unable to open video file."
+
+    frame_count = 0
+    object_counts = {"people": [], "trucks": []}
+    frame_skip = 5  # Process every 5th frame for efficiency
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break  # End of video
+
+        frame_count += 1
+        if frame_count % frame_skip != 0:
+            continue  # Skip frames to improve efficiency
+
+        # Run YOLOv12x inference
+        results = model(frame, verbose=False)
+
+        people_count, truck_count = 0, 0
+        for result in results:
+            for box in result.boxes:
+                class_id = int(box.cls.item())  # Get class ID
+                confidence = float(box.conf.item())  # Get confidence score
+
+                # Count objects based on their class IDs
+                if class_id == PERSON_CLASS_ID and confidence > 0.5:
+                    people_count += 1
+                elif class_id == TRUCK_CLASS_ID and confidence > 0.5:
+                    truck_count += 1
+
+        object_counts["people"].append(people_count)
+        object_counts["trucks"].append(truck_count)
+
+    cap.release()
+
+    return {
+        "Max People in a Frame": int(np.max(object_counts["people"])) if object_counts["people"] else 0,
+        "Max Trucks in a Frame": int(np.max(object_counts["trucks"])) if object_counts["trucks"] else 0
+    }
+
+# Gradio UI function
+def analyze_video(video_file):
+    result = count_objects(video_file)
+    return "\n".join([f"{key}: {value}" for key, value in result.items()])
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=analyze_video,
+    inputs=gr.Video(label="Upload Video"),
+    outputs=gr.Textbox(label="Analysis Result"),
+    title="YOLOv12x Object Counter",
+    description="Upload a video to count people and trucks using YOLOv12x."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    iface.launch()