kusumakar commited on
Commit
9dc2e90
1 Parent(s): 84d6084

Update app.py

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Files changed (1) hide show
  1. app.py +15 -16
app.py CHANGED
@@ -1,8 +1,12 @@
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- import openai
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- openai.api_key = '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'
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  import numpy as np
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  from PIL import Image
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  from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel, GPT2Tokenizer, GPT2LMHeadModel
 
 
 
 
 
 
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  # Directory path to the saved model on Google Drive
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  model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
@@ -18,23 +22,18 @@ def generate_captions(image):
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  return generated_caption
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  def generate_paragraph(caption):
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- prompt = "Generate a paragraph based on the following caption: " + caption
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-
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- # Make the API call to GPT-3
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- response = openai.Completion.create(
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- engine='text-davinci-003', # Specify the GPT-3 model
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- prompt=prompt,
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- max_tokens=200, # Adjust the desired length of the generated text
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- n = 1, # Set the number of completions to generate
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- stop=None, # Specify an optional stop sequence
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- temperature=0.7 # Adjust the temperature for randomness (between 0 and 1)
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- )
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-
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- # Extract the generated text from the API response
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- generated_text = response.choices[0].text.strip()
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  return generated_text
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  # create the Streamlit app
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  def app():
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  st.title('Image from your Side, Detailed description from my site')
 
 
 
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  import numpy as np
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  from PIL import Image
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  from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel, GPT2Tokenizer, GPT2LMHeadModel
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+ import torch
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+ from transformers import BartTokenizer, BartForConditionalGeneration
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+
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+ # Load pre-trained BART model and tokenizer
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+ tokenizer_2 = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
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+ model_2 = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
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  # Directory path to the saved model on Google Drive
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  model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 
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  return generated_caption
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  def generate_paragraph(caption):
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+ # Tokenize the caption
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+ inputs = tokenizer_2([caption], max_length=1024, truncation=True, padding="longest", return_tensors="pt")
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+
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+ # Generate text
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+ output = model_2.generate(inputs.input_ids, attention_mask=inputs.attention_mask, max_length=200, num_beams=4, length_penalty=2.0, early_stopping=True)
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+
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+ # Decode the generated output
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+ generated_text = tokenizer_2.decode(output[0], skip_special_tokens=True)
 
 
 
 
 
 
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  return generated_text
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+
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  # create the Streamlit app
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  def app():
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  st.title('Image from your Side, Detailed description from my site')