Upload app.py

app.py

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+import gradio as gr
+from PIL import Image
+import numpy as np
+import os
+import uuid
+
+def inference(input_img):
+    temp = uuid.uuid4()
+    shell = f"python yolov9/detect.py --source {input_img} --img 640 --device cpu --weights yolov9/runs/train/exp/weights/best.pt --name {temp}"
+    os.system(shell)
+    return f"yolov9/runs/detect/{temp}/{input_img.split('/')[-1]}"
+
+def inference_video(input_img):
+    org_img = input_img
+    return input_img
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # Vehicle detection using Yolo-v9
+        Upload the vehicle image or video for detection
+        """
+    )
+
+    with gr.Tab("Video"):
+        gr.Markdown(
+            """
+            Upload video mp4 file and detect the count of vehicles passing by
+            """
+        )
+        gr.Markdown(
+            """
+            Upload image file and detect vehicles present in the image
+            """
+        )
+        with gr.Row():
+            img_input = [gr.Video(label="Input Image",width=300, height=300)]
+            pred_outputs = [gr.Video(label="Output Image",width=300, height=300)]
+        
+        image_button = gr.Button("Predict")
+        image_button.click(inference, inputs=img_input, outputs=pred_outputs)
+
+    with gr.Tab("Image"):
+        gr.Markdown(
+            """
+            Upload image file and detect vehicles present in the image
+            """
+        )
+        with gr.Row():
+            img_input = [gr.Image(type="filepath",label="Input Image",width=300, height=300)]
+            pred_outputs = [gr.Image(label="Output Image",width=640, height=640)]
+        
+        image_button = gr.Button("Predict")
+        image_button.click(inference, inputs=img_input, outputs=pred_outputs)
+
+
+
+demo.launch(share=True)