gokilashree commited on
Commit
70b822a
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1 Parent(s): 2b24f54

Update app.py

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Files changed (1) hide show
  1. app.py +8 -16
app.py CHANGED
@@ -11,12 +11,12 @@ model_name = "facebook/mbart-large-50-many-to-one-mmt"
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  tokenizer = MBart50Tokenizer.from_pretrained(model_name)
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  model = MBartForConditionalGeneration.from_pretrained(model_name)
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- # Use GPT-Neo for more powerful text generation
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- text_gen_model = "EleutherAI/gpt-neo-1.3B" # Use EleutherAI/gpt-neo-2.7B for even better results
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  pipe = pipeline(
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- "text-generation",
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- model=text_gen_model,
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- torch_dtype=torch.float32,
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  device_map="auto"
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  )
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@@ -32,17 +32,9 @@ def translate_and_generate_image(tamil_text):
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  translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
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  translated_text = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
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- # Step 2: Generate descriptive English text using GPT-Neo
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  prompt = f"Create a detailed description based on the following text: {translated_text}"
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- generated_text = pipe(
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- prompt,
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- max_length=100,
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- num_return_sequences=1,
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- temperature=0.7,
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- top_p=0.9,
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- top_k=50,
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- truncation=True
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- )[0]['generated_text']
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  # Step 3: Use the generated English text to create an image
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  def query(payload):
@@ -63,7 +55,7 @@ iface = gr.Interface(
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  gr.Textbox(label="Generated Descriptive Text"),
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  gr.Image(label="Generated Image")],
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  title="Tamil to English Translation, Text Generation, and Image Creation",
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- description="Translate Tamil text to English using Facebook's mbart-large-50 model, generate descriptive text using GPT-Neo, and create an image using the generated text.",
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  )
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  # Launch Gradio app
 
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  tokenizer = MBart50Tokenizer.from_pretrained(model_name)
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  model = MBartForConditionalGeneration.from_pretrained(model_name)
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+ # Use a more powerful text generation model, e.g., GPT-J-6B
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+ text_gen_model = "EleutherAI/gpt-j-6B" # Or use 'EleutherAI/gpt-neox-20b' for better results
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  pipe = pipeline(
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+ "text-generation",
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+ model=text_gen_model,
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+ torch_dtype=torch.float32,
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  device_map="auto"
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  )
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  translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
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  translated_text = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
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+ # Step 2: Generate high-quality English text using GPT-J
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  prompt = f"Create a detailed description based on the following text: {translated_text}"
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+ generated_text = pipe(prompt, max_length=150, temperature=0.7, top_p=0.9, top_k=50, truncation=True)[0]['generated_text']
 
 
 
 
 
 
 
 
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  # Step 3: Use the generated English text to create an image
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  def query(payload):
 
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  gr.Textbox(label="Generated Descriptive Text"),
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  gr.Image(label="Generated Image")],
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  title="Tamil to English Translation, Text Generation, and Image Creation",
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+ description="Translate Tamil text to English using Facebook's mbart-large-50 model, generate high-quality text using GPT-J, and create an image using the generated text.",
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  )
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  # Launch Gradio app