Create app.py

app.py

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+import gradio as gr
+import torch
+import cv2
+import numpy as np
+
+# Load the pre-trained YOLOv5 model
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
+
+def detect_people(video):
+    # Initialize the video capture object
+    cap = cv2.VideoCapture(video.name)
+
+    # Initialize the output variable
+    num_people_detected = 0
+
+    # Loop through each frame of the video
+    while cap.isOpened():
+        # Read the frame
+        ret, frame = cap.read()
+
+        # If there are no more frames, break out of the loop
+        if not ret:
+            break
+
+        # Run the YOLOv5 model on the frame to detect people
+        results = model(frame, size=640)
+
+        # Get the number of people detected in the frame
+        num_people_detected += len(results.xyxy[0])
+
+    # Release the video capture object
+    cap.release()
+
+    # Return the number of people detected
+    return num_people_detected
+
+# Define the input and output interfaces for the Gradio app
+inputs = gr.inputs.Video(label="Upload a video")
+outputs = gr.outputs.Textbox(label="Number of people detected")
+
+# Create the Gradio app
+gr.Interface(detect_people, inputs, outputs, title="Object Detection App", description="Upload a video to detect the number of people in it.").launch()