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import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
from aksharamukha import transliterate
import torch
from dotenv import load_dotenv
import os
import requests
access_token = os.getenv('token')
# Set up device
device = "cuda" if torch.cuda.is_available() else "cpu"
chat_language = 'sin_Sinh'
trans_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
eng_trans_tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
device = "cuda" if torch.cuda.is_available() else "cpu"
translator = pipeline('translation', model=trans_model, tokenizer=eng_trans_tokenizer, src_lang="eng_Latn", tgt_lang=chat_language, max_length = 400, device=device)
# Initialize translation pipelines
pipe = pipeline("translation", model="thilina/mt5-sinhalese-english")
sin_trans_model = AutoModelForSeq2SeqLM.from_pretrained("thilina/mt5-sinhalese-english")
si_trans_tokenizer = AutoTokenizer.from_pretrained("thilina/mt5-sinhalese-english")
singlish_pipe = pipeline("text2text-generation", model="Dhahlan2000/Simple_Translation-model-for-GPT-v4")
# Translation functions
def translate_Singlish_to_sinhala(text):
translated_text = singlish_pipe(f"translate Singlish to Sinhala: {text}", clean_up_tokenization_spaces=False)[0]['generated_text']
return translated_text
def translate_english_to_sinhala(text):
# Split the text into sentences or paragraphs
parts = text.split("\n") # Split by new lines for paragraphs, adjust as needed
translated_parts = []
for part in parts:
translated_part = translator(part, clean_up_tokenization_spaces=False)[0]['translation_text']
translated_parts.append(translated_part)
# Join the translated parts back together
translated_text = "\n".join(translated_parts)
return translated_text.replace("ප් රභූවරුන්", "")
def translate_sinhala_to_english(text):
# Split the text into sentences or paragraphs
parts = text.split("\n") # Split by new lines for paragraphs, adjust as needed
translated_parts = []
for part in parts:
# Tokenize each part
inputs = si_trans_tokenizer(part.strip(), return_tensors="pt", padding=True, truncation=True, max_length=512)
# Generate translation
outputs = sin_trans_model.generate(**inputs)
# Decode translated text while preserving formatting
translated_part = si_trans_tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
translated_parts.append(translated_part)
# Join the translated parts back together
translated_text = "\n".join(translated_parts)
return translated_text
def transliterate_from_sinhala(text):
# Define the source and target scripts
source_script = 'Sinhala'
target_script = 'Velthuis'
# Perform transliteration
latin_text = transliterate.process(source_script, target_script, text)
# Convert to a list to allow modification
latin_text_list = list(latin_text)
# Replace periods with the following character
i = 0
for i in range(len(latin_text_list) - 1):
if latin_text_list[i] == '.':
latin_text_list[i] = ''
if latin_text_list[i] == '*':
latin_text_list[i] = ''
if latin_text_list[i] == '\"':
latin_text_list[i] = ''
# Convert back to a string
latin_text = ''.join(latin_text_list)
return latin_text.lower()
def transliterate_to_sinhala(text):
# Define the source and target scripts
source_script = 'Velthuis'
target_script = 'Sinhala'
# Perform transliteration
latin_text = transliterate.process(source_script, target_script, text)
return latin_text
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it", token = access_token)
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2b-it",
torch_dtype=torch.bfloat16,
token = access_token
)
def conversation_predict(input_text):
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)
return tokenizer.decode(outputs[0])
def ai_predicted(user_input):
user_input = translate_Singlish_to_sinhala(user_input)
user_input = transliterate_to_sinhala(user_input)
user_input = translate_sinhala_to_english(user_input)
print("You(english):", user_input,"\n")
# Get AI response
ai_response = conversation_predict(user_input)
# Split the AI response into separate lines
# ai_response_lines = ai_response.split("</s>")
print("AI(English):", ai_response,"\n")
response = translate_english_to_sinhala(ai_response)
response = transliterate_from_sinhala(response)
print(response)
return response
# Gradio Interface
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ai_predicted(message)
yield response
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch(share=True) |