kwabs22 commited on
Commit
37f1dd5
1 Parent(s): 032673b

remove unneeded comments

Browse files
Files changed (1) hide show
  1. app.py +3 -13
app.py CHANGED
@@ -9,15 +9,7 @@ def clear_model(model):
9
  del model
10
  gc.collect()
11
 
12
-
13
  def process_image_and_question(image, question):
14
- # Placeholder for your image processing and question answering
15
- # Replace this with your actual model processing
16
- # For example:
17
- # enc_image = model.encode_image(image)
18
- # answer = model.answer_question(enc_image, question, tokenizer)
19
- # return answer
20
-
21
  FinalOutput = ""
22
  model_id = "vikhyatk/moondream1"
23
 
@@ -32,15 +24,13 @@ def process_image_and_question(image, question):
32
  model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
33
  tokenizer = Tokenizer.from_pretrained(model_id)
34
 
35
- # Assuming you have a correct way to process the image
36
- #image = Image.open('/content/_57e22ed5-217c-4004-a279-eeecc18cbd55.jpg') #/content/Bard_Generated_Image (3).jpg')
37
- # This part of the code is incorrect for a standard transformers model
38
  enc_image = model.encode_image(image)
39
  FinalOutput += model.answer_question(enc_image, "how many people are there? also explain if the image is weird?", tokenizer)
40
 
41
  model_size = asizeof.asizeof(model)
42
  tokenizer_size = asizeof.asizeof(tokenizer)
43
- FinalOutput += f"\nModel size in RAM: {model_size} bytes, Tokenizer size in RAM: {tokenizer_size} bytes"
 
44
 
45
  #model load and set-up = 1 min and inference on CPU = 2 min
46
  return FinalOutput
@@ -50,7 +40,7 @@ iface = gr.Interface(fn=process_image_and_question,
50
  inputs=[gr.Image(type="pil"), gr.Textbox(lines=2, placeholder="Ask a question about the image...")],
51
  outputs="text",
52
  title="Image Question Answering",
53
- description="Upload an image and ask a question about it. ( 2 - 3 min response time expected )")
54
 
55
  # Launch the interface
56
  iface.launch()
 
9
  del model
10
  gc.collect()
11
 
 
12
  def process_image_and_question(image, question):
 
 
 
 
 
 
 
13
  FinalOutput = ""
14
  model_id = "vikhyatk/moondream1"
15
 
 
24
  model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
25
  tokenizer = Tokenizer.from_pretrained(model_id)
26
 
 
 
 
27
  enc_image = model.encode_image(image)
28
  FinalOutput += model.answer_question(enc_image, "how many people are there? also explain if the image is weird?", tokenizer)
29
 
30
  model_size = asizeof.asizeof(model)
31
  tokenizer_size = asizeof.asizeof(tokenizer)
32
+ FinalOutput += f"\n\nExpected Ram usage: +- 9.5 gb \nModel size in RAM: {model_size} bytes, Tokenizer size in RAM: {tokenizer_size} bytes"
33
+ #clear_model(model) #Not needed due to try except check
34
 
35
  #model load and set-up = 1 min and inference on CPU = 2 min
36
  return FinalOutput
 
40
  inputs=[gr.Image(type="pil"), gr.Textbox(lines=2, placeholder="Ask a question about the image...")],
41
  outputs="text",
42
  title="Image Question Answering",
43
+ description="Upload an image and ask a question about it. ( 3 - 4 min response time expected )")
44
 
45
  # Launch the interface
46
  iface.launch()