i hope it will work

Dockerfile

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+FROM python:3.9
+
+WORKDIR /app
+
+COPY ./requirements.txt .
+
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY . .
+
+EXPOSE 5000
+
+CMD ["python","app.py"]
+

app.py

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+# import os
+# import torch
+# import gradio s gr
+# import time
+# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+# from flores200_codes import flores_codes
+
+
+# def load_models():
+#     # build model and tokenizer
+#     model_name_dict = {'nllb-distilled-600M': 'facebook/nllb-200-distilled-600M',
+#                   #'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 = {}
+
+#     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-distilled-600M'
+
+#     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
+
+
+# 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.inputs.Dropdown(lang_codes, default='English', label='Source'),
+#               gr.inputs.Dropdown(lang_codes, default='Korean', label='Target'),
+#               gr.inputs.Textbox(lines=5, label="Input text"),
+#               ]
+
+#     outputs = gr.outputs.JSON()
+
+#     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 = [
+#     ['English', 'Korean', 'Hi. nice to meet you']
+#     ]
+
+#     gr.Interface(translation,
+#                  inputs,
+#                  outputs,
+#                  title=title,
+#                  description=description,
+#                  ).launch() 
+
+
+import os
+import time
+from flask import Flask, request, jsonify
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from flores200_codes import flores_codes
+
+app = Flask(__name__)
+
+def load_models():
+    model_name_dict = {'nllb-distilled-600M': 'facebook/nllb-200-distilled-600M'}
+    model_dict = {}
+
+    for call_name, real_name in model_name_dict.items():
+        print(f'\tLoading model: {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
+
+global model_dict
+model_dict = load_models()
+
+@app.route('/api/translate', methods=['POST'])
+def translate_text():
+    data = request.json
+    source_lang = data.get('source')
+    target_lang = data.get('target')
+    input_text = data.get('text')
+    
+    if not source_lang or not target_lang or not input_text:
+        return jsonify({"error": "source, target, and text fields are required"}), 400
+    
+    model_name = 'nllb-distilled-600M'
+    start_time = time.time()
+    source = flores_codes.get(source_lang)
+    target = flores_codes.get(target_lang)
+    
+    if not source or not target:
+        return jsonify({"error": "Invalid source or target language code"}), 400
+
+    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(input_text, max_length=400)
+    
+    end_time = time.time()
+    output_text = output[0]['translation_text']
+    
+    result = {
+        'inference_time': end_time - start_time,
+        'source': source_lang,
+        'target': target_lang,
+        'result': output_text
+    }
+    return jsonify(result)
+
+if __name__ == '__main__':
+    app.run(debug=True) 

flores200_codes.py

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+codes_as_string = '''Acehnese (Arabic scrip)	ace_Arab
+Acehnese (Latin script)	ace_Latn
+Mesopotamian Arabic	acm_Arab
+Ta’izzi-Adeni Arabic	acq_Arab
+Tunisian Arabic	aeb_Arab
+Afrikaans	afr_Latn
+South Levantine Arabic	ajp_Arab
+Akan	aka_Latn
+Amharic	amh_Ethi
+North Levantine Arabic	apc_Arab
+Modern Standard Arabic	arb_Arab
+Modern Standard Arabic (Romanized)	arb_Latn
+Najdi Arabic	ars_Arab
+Moroccan Arabic	ary_Arab
+Egyptian Arabic	arz_Arab
+Assamese	asm_Beng
+Asturian	ast_Latn
+Awadhi	awa_Deva
+Central Aymara	ayr_Latn
+South Azerbaijani	azb_Arab
+North Azerbaijani	azj_Latn
+Bashkir	bak_Cyrl
+Bambara	bam_Latn
+Balinese	ban_Latn
+Belarusian	bel_Cyrl
+Bemba	bem_Latn
+Bengali	ben_Beng
+Bhojpuri	bho_Deva
+Banjar (Arabic script)	bjn_Arab
+Banjar (Latin script)	bjn_Latn
+Standard Tibetan	bod_Tibt
+Bosnian	bos_Latn
+Buginese	bug_Latn
+Bulgarian	bul_Cyrl
+Catalan	cat_Latn
+Cebuano	ceb_Latn
+Czech	ces_Latn
+Chokwe	cjk_Latn
+Central Kurdish	ckb_Arab
+Crimean Tatar	crh_Latn
+Welsh	cym_Latn
+Danish	dan_Latn
+German	deu_Latn
+Southwestern Dinka	dik_Latn
+Dyula	dyu_Latn
+Dzongkha	dzo_Tibt
+Greek	ell_Grek
+English	eng_Latn
+Esperanto	epo_Latn
+Estonian	est_Latn
+Basque	eus_Latn
+Ewe	ewe_Latn
+Faroese	fao_Latn
+Fijian	fij_Latn
+Finnish	fin_Latn
+Fon	fon_Latn
+French	fra_Latn
+Friulian	fur_Latn
+Nigerian Fulfulde	fuv_Latn
+Scottish Gaelic	gla_Latn
+Irish	gle_Latn
+Galician	glg_Latn
+Guarani	grn_Latn
+Gujarati	guj_Gujr
+Haitian Creole	hat_Latn
+Hausa	hau_Latn
+Hebrew	heb_Hebr
+Hindi	hin_Deva
+Chhattisgarhi	hne_Deva
+Croatian	hrv_Latn
+Hungarian	hun_Latn
+Armenian	hye_Armn
+Igbo	ibo_Latn
+Ilocano	ilo_Latn
+Indonesian	ind_Latn
+Icelandic	isl_Latn
+Italian	ita_Latn
+Javanese	jav_Latn
+Japanese	jpn_Jpan
+Kabyle	kab_Latn
+Jingpho	kac_Latn
+Kamba	kam_Latn
+Kannada	kan_Knda
+Kashmiri (Arabic script)	kas_Arab
+Kashmiri (Devanagari script)	kas_Deva
+Georgian	kat_Geor
+Central Kanuri (Arabic script)	knc_Arab
+Central Kanuri (Latin script)	knc_Latn
+Kazakh	kaz_Cyrl
+Kabiyè	kbp_Latn
+Kabuverdianu	kea_Latn
+Khmer	khm_Khmr
+Kikuyu	kik_Latn
+Kinyarwanda	kin_Latn
+Kyrgyz	kir_Cyrl
+Kimbundu	kmb_Latn
+Northern Kurdish	kmr_Latn
+Kikongo	kon_Latn
+Korean	kor_Hang
+Lao	lao_Laoo
+Ligurian	lij_Latn
+Limburgish	lim_Latn
+Lingala	lin_Latn
+Lithuanian	lit_Latn
+Lombard	lmo_Latn
+Latgalian	ltg_Latn
+Luxembourgish	ltz_Latn
+Luba-Kasai	lua_Latn
+Ganda	lug_Latn
+Luo	luo_Latn
+Mizo	lus_Latn
+Standard Latvian	lvs_Latn
+Magahi	mag_Deva
+Maithili	mai_Deva
+Malayalam	mal_Mlym
+Marathi	mar_Deva
+Minangkabau (Arabic script)	min_Arab
+Minangkabau (Latin script)	min_Latn
+Macedonian	mkd_Cyrl
+Plateau Malagasy	plt_Latn
+Maltese	mlt_Latn
+Meitei (Bengali script)	mni_Beng
+Halh Mongolian	khk_Cyrl
+Mossi	mos_Latn
+Maori	mri_Latn
+Burmese	mya_Mymr
+Dutch	nld_Latn
+Norwegian Nynorsk	nno_Latn
+Norwegian Bokmål	nob_Latn
+Nepali	npi_Deva
+Northern Sotho	nso_Latn
+Nuer	nus_Latn
+Nyanja	nya_Latn
+Occitan	oci_Latn
+West Central Oromo	gaz_Latn
+Odia	ory_Orya
+Pangasinan	pag_Latn
+Eastern Panjabi	pan_Guru
+Papiamento	pap_Latn
+Western Persian	pes_Arab
+Polish	pol_Latn
+Portuguese	por_Latn
+Dari	prs_Arab
+Southern Pashto	pbt_Arab
+Ayacucho Quechua	quy_Latn
+Romanian	ron_Latn
+Rundi	run_Latn
+Russian	rus_Cyrl
+Sango	sag_Latn
+Sanskrit	san_Deva
+Santali	sat_Olck
+Sicilian	scn_Latn
+Shan	shn_Mymr
+Sinhala	sin_Sinh
+Slovak	slk_Latn
+Slovenian	slv_Latn
+Samoan	smo_Latn
+Shona	sna_Latn
+Sindhi	snd_Arab
+Somali	som_Latn
+Southern Sotho	sot_Latn
+Spanish	spa_Latn
+Tosk Albanian	als_Latn
+Sardinian	srd_Latn
+Serbian	srp_Cyrl
+Swati	ssw_Latn
+Sundanese	sun_Latn
+Swedish	swe_Latn
+Swahili	swh_Latn
+Silesian	szl_Latn
+Tamil	tam_Taml
+Tatar	tat_Cyrl
+Telugu	tel_Telu
+Tajik	tgk_Cyrl
+Tagalog	tgl_Latn
+Thai	tha_Thai
+Tigrinya	tir_Ethi
+Tamasheq (Latin script)	taq_Latn
+Tamasheq (Tifinagh script)	taq_Tfng
+Tok Pisin	tpi_Latn
+Tswana	tsn_Latn
+Tsonga	tso_Latn
+Turkmen	tuk_Latn
+Tumbuka	tum_Latn
+Turkish	tur_Latn
+Twi	twi_Latn
+Central Atlas Tamazight	tzm_Tfng
+Uyghur	uig_Arab
+Ukrainian	ukr_Cyrl
+Umbundu	umb_Latn
+Urdu	urd_Arab
+Northern Uzbek	uzn_Latn
+Venetian	vec_Latn
+Vietnamese	vie_Latn
+Waray	war_Latn
+Wolof	wol_Latn
+Xhosa	xho_Latn
+Eastern Yiddish	ydd_Hebr
+Yoruba	yor_Latn
+Yue Chinese	yue_Hant
+Chinese (Simplified)	zho_Hans
+Chinese (Traditional)	zho_Hant
+Standard Malay	zsm_Latn
+Zulu	zul_Latn'''
+
+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

requirements.txt

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+httpx==0.24.1
+flask
+transformers
+torch