fffiloni commited on
Commit
91c5ebc
1 Parent(s): 003efa5

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +8 -4
app.py CHANGED
@@ -1,6 +1,7 @@
1
  import spaces
2
  import gradio as gr
3
  from gradio_client import Client
 
4
  import cv2
5
  from moviepy.editor import *
6
 
@@ -48,7 +49,7 @@ def extract_frames(video_in, interval=24, output_format='.jpg'):
48
 
49
  # Check if successful read and not past end of video
50
  if success:
51
- print('Read a new frame:', success)
52
 
53
  # Save current frame if it meets criteria
54
  if count % interval == 0:
@@ -70,6 +71,7 @@ def extract_frames(video_in, interval=24, output_format='.jpg'):
70
 
71
  return frames
72
 
 
73
  from transformers import AutoModelForCausalLM, AutoTokenizer
74
  from PIL import Image
75
 
@@ -79,11 +81,12 @@ model = AutoModelForCausalLM.from_pretrained(
79
  model_id, trust_remote_code=True, revision=revision
80
  )
81
  tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
 
82
 
83
- @spaces.GPU()
84
  def process_image(image_in):
85
- '''
86
- client = Client("https://vikhyatk-moondream1.hf.space/")
87
  result = client.predict(
88
  image_in, # filepath in 'image' Image component
89
  "Describe precisely the image in one sentence.", # str in 'Question' Textbox component
@@ -98,6 +101,7 @@ def process_image(image_in):
98
  result = model.answer_question(enc_image, "Describe the image in one sentence.", tokenizer)
99
  print(result)
100
  return result
 
101
 
102
  def extract_audio(video_path):
103
  video_clip = VideoFileClip(video_path)
 
1
  import spaces
2
  import gradio as gr
3
  from gradio_client import Client
4
+ client = Client("https://vikhyatk-moondream1.hf.space/")
5
  import cv2
6
  from moviepy.editor import *
7
 
 
49
 
50
  # Check if successful read and not past end of video
51
  if success:
52
+ #print('Read a new frame:', success)
53
 
54
  # Save current frame if it meets criteria
55
  if count % interval == 0:
 
71
 
72
  return frames
73
 
74
+ '''
75
  from transformers import AutoModelForCausalLM, AutoTokenizer
76
  from PIL import Image
77
 
 
81
  model_id, trust_remote_code=True, revision=revision
82
  )
83
  tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
84
+ '''
85
 
86
+ #@spaces.GPU()
87
  def process_image(image_in):
88
+
89
+
90
  result = client.predict(
91
  image_in, # filepath in 'image' Image component
92
  "Describe precisely the image in one sentence.", # str in 'Question' Textbox component
 
101
  result = model.answer_question(enc_image, "Describe the image in one sentence.", tokenizer)
102
  print(result)
103
  return result
104
+ '''
105
 
106
  def extract_audio(video_path):
107
  video_clip = VideoFileClip(video_path)