Add application file

app.py

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+import gradio as gr
+import os
+import torch
+from PIL import Image
+
+                           
+#subprocess.run(["mv","content/custom_data.yaml","./yolov5/data"])         
+
+
+def load_model():
+ '''
+ Loading hub model & setting the preferences for the model  
+ '''
+ model = torch.hub.load('ultralytics/yolov5', 'custom', path='Content/cnn.pt')
+ model.conf = 0.38 
+ model.dnn=True
+ model.agnostic=True
+ return model
+
+model=load_model()
+#, force_reload=True
+def detect(inp):
+ #g = (size / max(inp.size))  #gain
+ #im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize 
+ results = model(inp,size=640)  # inference
+ print(results)
+ results.render()  # updates results.imgs with boxes and labels
+ return Image.fromarray(results.imgs[0])
+ 
+
+inp = gr.inputs.Image(type="pil", label="Original Image")
+output = gr.outputs.Image(type="pil", label="Output Image")
+
+
+io=gr.Interface(fn=detect, inputs=inp, outputs=output, title='Mosquito Habitat Identification',theme='peach')
+io.launch(debug=True,share=False)
+  
+#examples=['Content/4.jpg','Content/10.jpg','Content/18.jpg']
+
+                                             

packages.txt

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+libgl1-mesa-glx