Added phi_1.5

run.py

@@ -4,16 +4,32 @@ import numpy as np
 import torch
 model = torch.hub.load('ultralytics/yolov5', 'custom', path='last2.pt', force_reload=True)
 
+from transformers import AutoModelForCausalLM, AutoTokenizer
+llm = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5", trust_remote_code=True)
+
+def give(results):
+  prompt=""
+  if int(results.pred[0][-1][-1].numpy())==0:
+      prompt="Great focus. You are doing great!"
+  elif int(results.pred[0][-1][-1].numpy())==1:
+      prompt="You look dizzy."
+  else:
+      prompt="You are getting distracted."
+  inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False)
+  outputs = llm.generate(**inputs, max_length=30)
+  return tokenizer.batch_decode(outputs)[0]
+
 def detect(im):
     results = model(im)
-    return results
+    return [results,give(results)]
     #return [np.squeeze(results.render())]
     #return [im]
 
 demo = gr.Interface(
     detect,
     [gr.Image(source="webcam", tool=None)],
-    ["text"],
+    ["text","text"],
 )
 if __name__ == "__main__":
     demo.launch(share=True)