Update app.py

app.py

@@ -1,3 +1,48 @@
-'''
-contains the infernce code for fastsam.py
-'''
+# '''
+# contains the infernce code for fastsam.py
+# '''
+# import numpy as np 
+# import gradio as gr 
+# from PIL import Image 
+# from ultralytics import FastSAM 
+# from ultralytics.models.fastsam import FastSAMPrompt 
+
+# def inference(image):
+
+#     model = FastSAM('FastSAM.pt')
+
+#     ## inference results
+#     # Run inference on an image
+#     results = model(source, device='cpu', retina_masks=True, imgsz=1024, conf=0.4, iou=0.9)
+
+#     # Prepare a Prompt Process object
+#     prompt_process = FastSAMPrompt(source, results, device='cpu')
+    
+#     # Everything prompt
+#     ann = prompt_process.everything_prompt()
+
+#      # Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2]
+#     ann = prompt_process.box_prompt(bbox=[200, 200, 300, 300])
+    
+#     # Text prompt
+#     ann = prompt_process.text_prompt(text='a photo of a dog')
+    
+#     # Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2]
+#     ann = prompt_process.point_prompt(points=[[200,200]],pointlabel=[1])
+#     prompt_process.plot(annotations=ann,output='./')
+
+    
+
+# title = "Usage of FastSAM"
+# description = "Implementation of pre-trained fast-sam model for spaces."
+
+# demo = gr.Interface(inference,inputs=[gr.Image(shape=(32,32),labels="Input Image"),"checkbox"],
+#                     outputs = [gr.Image(shape=(32,32),label='Output').style(width=128,height=128)],
+#                     title = title, 
+#                     description = description)
+
+# demo.launch(debug = True)
+                    
+    
+    
+