sohailq commited on
Commit
99d7a63
1 Parent(s): b39033e

Update app.py

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Files changed (1) hide show
  1. app.py +35 -7
app.py CHANGED
@@ -1,20 +1,47 @@
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- import gradio as gr
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- from transformers import pipeline
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  #from fairseq.models.transformer import TransformerModel
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  # Load the English to Urdu translation model from the transformers library
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- model_name_or_path = "Helsinki-NLP/opus-mt-en-ur"
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  #model_name_or_path = TransformerModel.from_pretrained('samiulhaq/iwslt-bt-en-ur')
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- translator = pipeline("translation", model=model_name_or_path, tokenizer=model_name_or_path)
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  # Create a Gradio interface for the translation app
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- def translate(text):
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  # Use the translator pipeline to translate the input text
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- result = translator(text, max_length=500)
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- return result[0]['translation_text']
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  input_text = gr.inputs.Textbox(label="Input English Text")
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  output_text = gr.outputs.Textbox(label="Output Urdu Text")
@@ -22,3 +49,4 @@ app = gr.Interface(fn=translate, inputs=input_text, outputs=output_text)
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  # Launch the app
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  app.launch()
 
 
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+ #import gradio as gr
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+ #from transformers import pipeline
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  #from fairseq.models.transformer import TransformerModel
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  # Load the English to Urdu translation model from the transformers library
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+ #model_name_or_path = "Helsinki-NLP/opus-mt-en-ur"
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  #model_name_or_path = TransformerModel.from_pretrained('samiulhaq/iwslt-bt-en-ur')
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+ #translator = pipeline("translation", model=model_name_or_path, tokenizer=model_name_or_path)
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  # Create a Gradio interface for the translation app
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+ #def translate(text):
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  # Use the translator pipeline to translate the input text
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+ # result = translator(text, max_length=500)
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+ # return result[0]['translation_text']
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+
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+ #input_text = gr.inputs.Textbox(label="Input English Text")
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+ #output_text = gr.outputs.Textbox(label="Output Urdu Text")
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+ #app = gr.Interface(fn=translate, inputs=input_text, outputs=output_text)
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+
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+ # Launch the app
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+ #app.launch()
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+
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+
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+ import gradio as gr
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+ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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+
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+ # Load the English to Urdu translation model from the transformers library
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+ model_name_or_path = "aryanc55/english-urdu"
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+ tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
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+ model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)
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+
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+ # Create a Gradio interface for the translation app
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+ def translate(text):
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+ # Tokenize the input text
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+ inputs = tokenizer(text, return_tensors="pt")
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+
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+ # Use the model to generate the translated text
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+ outputs = model.generate(inputs["input_ids"], max_length=500, early_stopping=True)
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+ translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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+
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+ return translated_text
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  input_text = gr.inputs.Textbox(label="Input English Text")
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  output_text = gr.outputs.Textbox(label="Output Urdu Text")
 
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  # Launch the app
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  app.launch()
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+