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import gradio as gr
import requests
from transformers import pipeline
import nltk
nltk.download('punkt')
nltk.download('punkt_tab')
from nltk import sent_tokenize
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
from transformers import pipeline
import os
from dotenv import load_dotenv, dotenv_values
load_dotenv()
api_token_header = os.getenv("api_token_header")
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text2text-generation", model="SnypzZz/Llama2-13b-Language-translate", use_fast = False)
tokenizer = MBart50TokenizerFast.from_pretrained("SnypzZz/Llama2-13b-Language-translate", src_lang="en_XX")
model = None
model_loaded = False
def load_model():
global model, model_loaded
model = MBartForConditionalGeneration.from_pretrained("SnypzZz/Llama2-13b-Language-translate")
model_loaded =True
return model
def translation(text,dest_lang,dest_lang_code, src_lang_code):
if(dest_lang_code == src_lang_code):
return "Please select different languages to translate between."
headers = {"Authorization": f"Bearer {api_token_header}"}
# # Bengali Done
# if(dest_lang == "Bengali" and src_lang_code == "en_XX"):
# API_URL = "https://api-inference.huggingface.co/models/csebuetnlp/banglat5_nmt_en_bn"
# def query(payload):
# response = requests.post(API_URL, headers=headers, json=payload)
# return response.json()
# output = query({
# "inputs": text,
# })
# print(output)
# return output[0]['translation_text']
# else:
global model
if model:
pass
else:
model = load_model()
loaded_model = model
tokenizer = MBart50TokenizerFast.from_pretrained("SnypzZz/Llama2-13b-Language-translate", src_lang=src_lang_code)
#model_inputs = tokenizer(text, return_tensors="pt")
loaded_model_inputs = tokenizer(text, return_tensors="pt")
# translate
generated_tokens = loaded_model.generate(
**loaded_model_inputs,
forced_bos_token_id=tokenizer.lang_code_to_id[dest_lang_code]
)
output = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(output)
return output[0]
def main_translation(text,dest_lang_code,src_lang_code):
codes = {"en_XX":"English","bn_IN":"Bengali", "en_GB":"English","gu_IN":"Gujarati","hi_IN":"Hindi","ta_IN":"Tamil","te_IN":"Telugu","mr_IN":"Marathi"}
dest_lang = codes[dest_lang_code]
src_lang = codes[src_lang_code]
sentences = sent_tokenize(text)
output = ""
for line in sentences:
output += translation(line,dest_lang,dest_lang_code, src_lang_code)
return {"output":output}
def test(text, src, dest):
ans = main_translation(text,dest,src)
return ans['output']
demo = gr.Interface(
test,
["textbox",
gr.Dropdown(
[("English", "en_XX"), ("Hindi","hi_IN"), ("Bengali","bn_IN"), ("Gujarati","gu_IN"), ("Tamil","ta_IN"), ("Telugu","te_IN"), ("Marathi","mr_IN")], label="Source", info="Select the Source Language!"
),
gr.Dropdown(
[("English", "en_XX"), ("Hindi","hi_IN"), ("Bengali","bn_IN"), ("Gujarati","gu_IN"), ("Tamil","ta_IN"), ("Telugu","te_IN"), ("Marathi","mr_IN")], label="Destination", info="Select the Destination Language!"
),
],
outputs=["textbox"],
)
demo.launch(share=True) |