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README.md

@@ -1,13 +1,13 @@
 ---
-title: MachineTranslation
-emoji: 👀
+title: Ep
+emoji: 🏢
 colorFrom: pink
-colorTo: green
+colorTo: pink
 sdk: gradio
-sdk_version: 4.22.0
+sdk_version: 3.27.0
 app_file: app.py
 pinned: false
-license: apache-2.0
+license: mit
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import torch
+import gradio as gr
+import time
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+codes_as_string = '''Assamese	asm_Beng
+Awadhi	awa_Deva
+Bengali	ben_Beng
+Bhojpuri	bho_Deva
+Standard Tibetan	bod_Tibt
+Dzongkha	dzo_Tibt
+English	eng_Latn
+Gujarati	guj_Gujr
+Hindi	hin_Deva
+Chhattisgarhi	hne_Deva
+Kannada	kan_Knda
+Kashmiri (Arabic script)	kas_Arab
+Kashmiri (Devanagari script)	kas_Deva
+Mizo	lus_Latn
+Magahi	mag_Deva
+Maithili	mai_Deva
+Malayalam	mal_Mlym
+Marathi	mar_Deva
+Meitei (Bengali script)	mni_Beng
+Burmese	mya_Mymr
+Nepali	npi_Deva
+Odia	ory_Orya
+Punjabi	pan_Guru
+Sanskrit	san_Deva
+Santali	sat_Olck
+Sindhi	snd_Arab
+Tamil	tam_Taml
+Telugu	tel_Telu
+Urdu	urd_Arab
+Vietnamese	vie_Latn'''
+
+
+
+def load_models():
+    # build model and tokenizer
+    model_name_dict = {
+                  'nllb-1.3B': "ychenNLP/nllb-200-distilled-1.3B-easyproject",
+                  }
+
+    model_dict = {}
+
+    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("facebook/nllb-200-distilled-600M")
+        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-1.3B'
+
+    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()
+
+    full_output = output
+    output = output[0]['translation_text']
+    # result = {'inference_time': end_time - start_time,
+    #           'source': source,
+    #           'target': target,
+    #           'result': output,
+    #           'full_output': full_output}
+    return output
+
+
+if __name__ == '__main__':
+    print('\tinit models')
+    codes_as_string = codes_as_string.split('\n')
+
+    flores_codes = {}
+    for code in codes_as_string:
+        lang, lang_code = code.split('\t')
+        flores_codes[lang] = lang_code
+
+    global model_dict
+
+    model_dict = load_models()
+    
+    # define gradio demo
+    lang_codes = list(flores_codes.keys())
+    
+    inputs = [gr.inputs.Dropdown(lang_codes, default='English', label='Source'),
+              gr.inputs.Dropdown(lang_codes, default='Hindi', label='Target'),
+              gr.inputs.Textbox(lines=5, label="Input text"),
+              ]
+
+    outputs = gr.inputs.Textbox(label="Output text")
+
+    title = "Machine Translation Demo"
+
+    demo_status = "Machine Translation System."
+    description = f"{demo_status}"
+
+    gr.Interface(translation,
+                 inputs,
+                 outputs,
+                 title=title,
+                 description=description,
+                 examples=examples,
+                 examples_per_page=50,
+                 theme="JohnSmith9982/small_and_pretty"
+                 ).launch()

requirements.txt

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+git+https://github.com/huggingface/transformers
+gradio
+torch