Update app.py

app.py

@@ -1,45 +1,16 @@
-from ultralytics import YOLO
-from PIL import Image
 import gradio as gr
-from huggingface_hub import snapshot_download
-import os
 
+def greet(name):
+    return f"Hello, {name}!"
 
+with gr.Blocks() as demo:
+    with gr.Row():
+        gr.Markdown("## Welcome to My Gradio App!")
+    with gr.Row():
+        name_input = gr.Textbox(label="Enter your name")
+        output = gr.Textbox(label="Greeting")
+    with gr.Row():
+        greet_button = gr.Button("Greet")
+        greet_button.click(greet, inputs=name_input, outputs=output)
 
-# Define the path to the YOLOv8 mo
-model_path = "best_int8_openvino_model"   
-
-#Organizations model path location
-MODEL_REPO_ID = "best_int8_openvino_modelaunghlaing/testmodel"
-
-#load model
-def load_model(repo_id):
-    download_dir = snapshot_download(repo_id)
-    print(download_dir)
-    path  = os.path.join(download_dir, "best_int8_openvino_model")
-    print(path)
-    detection_model = YOLO(path, task='detect')
-    return detection_model
-    
-detection_model = load_model(MODEL_REPO_ID)
-
-#Student ID
-student_info = "Student Id: 6319250G, Name: AUNG HTUN"
-
-#prdeict 
-def predict(pilimg):
-    source = pilimg
-    result = detection_model.predict(source, conf=0.5, iou=0.5)
-    img_bgr = result[0].plot()
-    out_pilimg = Image.fromarray(img_bgr[..., ::-1])  # RGB-order PIL image
-    return out_pilimg
-    
-#UI interface
-gr.Markdown("# Two Object Detection (Shark/Mask)")
-gr.Markdown(student_info)
-gr.Interface(fn=predict,
-             inputs=gr.Image(type="pil",label="Input"),
-             outputs=gr.Image(type="pil",label="Output"),
-             title="Two Object Detection (Shark/Mask)",
-             description=student_info,
-             ).launch(share=True)
+demo.launch()