thinh-huynh-re commited on
Commit
e1e899c
·
1 Parent(s): ebc167e
Files changed (1) hide show
  1. app.py +8 -3
app.py CHANGED
@@ -1,8 +1,9 @@
 
1
  import os
 
2
  from typing import List, Tuple
3
- import multiprocessing
4
- import cv2
5
 
 
6
  import numpy as np
7
  import pandas as pd
8
  import streamlit as st
@@ -104,7 +105,7 @@ def inference(file_path: str):
104
  results: List[Tuple[str, float]] = []
105
  for index, value in zip(indices, values):
106
  predicted_label = model.config.id2label[index]
107
- print(f"Label: {predicted_label} - {value:.2f}%")
108
  results.append((predicted_label, value))
109
 
110
  return pd.DataFrame(results, columns=("Label", "Confidence"))
@@ -138,12 +139,16 @@ feature_extractor, model = load_model(model_name)
138
  VIDEO_TMP_PATH = os.path.join("tmp", "tmp.mp4")
139
  uploadedfile = st.file_uploader("Upload file", type=["mp4"])
140
 
 
141
  if uploadedfile is not None:
142
  with st.spinner():
143
  with open(VIDEO_TMP_PATH, "wb") as f:
144
  f.write(uploadedfile.getbuffer())
145
 
 
146
  with st.spinner("Processing..."):
147
  df = inference(VIDEO_TMP_PATH)
 
 
148
  st.dataframe(df)
149
  st.video(VIDEO_TMP_PATH)
 
1
+ import multiprocessing
2
  import os
3
+ import time
4
  from typing import List, Tuple
 
 
5
 
6
+ import cv2
7
  import numpy as np
8
  import pandas as pd
9
  import streamlit as st
 
105
  results: List[Tuple[str, float]] = []
106
  for index, value in zip(indices, values):
107
  predicted_label = model.config.id2label[index]
108
+ # print(f"Label: {predicted_label} - {value:.2f}%")
109
  results.append((predicted_label, value))
110
 
111
  return pd.DataFrame(results, columns=("Label", "Confidence"))
 
139
  VIDEO_TMP_PATH = os.path.join("tmp", "tmp.mp4")
140
  uploadedfile = st.file_uploader("Upload file", type=["mp4"])
141
 
142
+
143
  if uploadedfile is not None:
144
  with st.spinner():
145
  with open(VIDEO_TMP_PATH, "wb") as f:
146
  f.write(uploadedfile.getbuffer())
147
 
148
+ start_time = time.time()
149
  with st.spinner("Processing..."):
150
  df = inference(VIDEO_TMP_PATH)
151
+ end_time = time.time()
152
+ st.info(f"{end_time - start_time} seconds")
153
  st.dataframe(df)
154
  st.video(VIDEO_TMP_PATH)