Update app.py

app.py

@@ -6,74 +6,30 @@ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 from flores200_codes import flores_codes
 
 
-def load_models():
-    # build model and tokenizer
-    model_name_dict = {#'nllb-finetuned-kutofa': 'ashourzadeh7/nllb-finetuned-kutofa',
-                  #'nllb-1.3B': 'facebook/nllb-200-1.3B',
-                  #'nllb-distilled-1.3B': 'facebook/nllb-200-distilled-1.3B',
-                  'nllb-3.3B': 'facebook/nllb-200-3.3B',
-                  }
 
-    model_dict = {}
+def transfer(input):
+    with open(input, 'r', encoding="utf-8") as f:
+        text = f.read()
 
-    for call_name, real_name in model_name_dict.items():
-        print('\tLoading model: %s' % call_name)
-        model = AutoModelForSeq2SeqLM.from_pretrained(real_name)
-        tokenizer = AutoTokenizer.from_pretrained(real_name)
-        model_dict[call_name+'_model'] = model
-        model_dict[call_name+'_tokenizer'] = tokenizer
-
-    return model_dict
-
-
-def translation(source, target, text):
-    if len(model_dict) == 2:
-        model_name = 'nllb-3.3B'
-
-    start_time = time.time()
-    source = flores_codes[source]
-    target = flores_codes[target]
-
-    model = model_dict[model_name + '_model']
-    tokenizer = model_dict[model_name + '_tokenizer']
-
-    translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=source, tgt_lang=target)
-    output = translator(text, max_length=400)
-
-    end_time = time.time()
-
-    output = output[0]['translation_text']
-    result = {'inference_time': end_time - start_time,
-              'source': source,
-              'target': target,
-              'result': output}
-    return result
+    output_file = "out.txt"
+    with open(output_file, 'w', encoding="utf-8") as f:
+        file = f.write(text)
+    return file
 
 
 if __name__ == '__main__':
-    print('\tinit models')
 
-    global model_dict
 
-    model_dict = load_models()
-    
-    # define gradio demo
-    lang_codes = list(flores_codes.keys())
+
     #inputs = [gr.inputs.Radio(['nllb-distilled-600M', 'nllb-1.3B', 'nllb-distilled-1.3B'], label='NLLB Model'),
-    inputs = [gr.components.Dropdown(label='Source', choices=lang_codes),
-              gr.components.Dropdown(label='Target', choices=lang_codes),
-              gr.components.Textbox(lines=5, label="Input text"),
-              ]
+    inputs = [gr.components.file(label="Input File")]
 
-    outputs = gr.components.JSON()
+    outputs = gr.components.file(label="Translated File", value=file)
 
     title = "NLLB distilled 600M demo"
 
     demo_status = "Demo is running on CPU"
     description = f"Details: https://github.com/facebookresearch/fairseq/tree/nllb. {demo_status}"
-    examples = [
-    ['فارسی', 'کردی', 'سلام، حالتون خوبه؟']
-    ]
 
     gr.Interface(translation,
                  inputs,