breadlicker45 commited on
Commit
33262af
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verified ·
1 Parent(s): 8bc78e2

Update app.py

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Files changed (1) hide show
  1. app.py +16 -11
app.py CHANGED
@@ -21,9 +21,9 @@ def load_model():
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  "google/paligemma2-28b-pt-896", use_auth_token=token
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  )
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  model = AutoModelForImageTextToText.from_pretrained(
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- "google/paligemma2-28b-pt-896", use_auth_token=token, torch_dtype=torch.float16
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  )
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-
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  # Move model to GPU if available
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  if torch.cuda.is_available():
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  model = model.to("cuda")
@@ -32,16 +32,18 @@ def load_model():
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  @spaces.GPU # Decorate the function that uses the GPU
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- def process_image(image):
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  """Extract text from image using PaliGemma2."""
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  processor, model = load_model()
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- # Preprocess the image
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- inputs = processor(images=image, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu", dtype=torch.float16)
 
 
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  # Generate predictions
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  with torch.no_grad():
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- generated_ids = model.generate(**inputs)
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  text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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  return text
@@ -49,10 +51,13 @@ def process_image(image):
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  if __name__ == "__main__":
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  iface = gr.Interface(
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- fn=process_image,
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- inputs=gr.Image(type="pil", label="Upload an image containing text"),
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- outputs=gr.Textbox(label="Extracted Text"),
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- title="Text Reading from Images using PaliGemma2",
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- description="Upload an image containing text and the model will extract the text.",
 
 
 
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  )
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  iface.launch()
 
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  "google/paligemma2-28b-pt-896", use_auth_token=token
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  )
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  model = AutoModelForImageTextToText.from_pretrained(
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+ "google/paligemma2-28b-pt-896", use_auth_token=token, torch_dtype=torch.bfloat16
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  )
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+
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  # Move model to GPU if available
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  if torch.cuda.is_available():
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  model = model.to("cuda")
 
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  @spaces.GPU # Decorate the function that uses the GPU
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+ def process_image_and_text(image, text_input):
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  """Extract text from image using PaliGemma2."""
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  processor, model = load_model()
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+ # Preprocess the image and text
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+ inputs = processor(text=text_input, images=image, return_tensors="pt").to(
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+ "cuda" if torch.cuda.is_available() else "cpu", dtype=torch.bfloat16
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+ )
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  # Generate predictions
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  with torch.no_grad():
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+ generated_ids = model.generate(**inputs, max_new_tokens=100)
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  text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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  return text
 
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  if __name__ == "__main__":
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  iface = gr.Interface(
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+ fn=process_image_and_text,
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+ inputs=[
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+ gr.Image(type="pil", label="Upload an image containing text"),
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+ gr.Textbox(label="Enter Text Prompt"),
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+ ],
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+ outputs=gr.Textbox(label="Extracted/Generated Text"),
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+ title="Text Reading/Generation with PaliGemma2",
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+ description="Upload an image and enter a text prompt. The model will generate text based on both.",
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  )
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  iface.launch()