Spaces:
Sleeping
Sleeping
File size: 1,655 Bytes
9975621 bb76140 6c33624 f702458 6c33624 bb76140 6c33624 bb76140 9975621 ab80ee9 9975621 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
import streamlit as st
import pandas as pd
from transformers import MarianMTModel, MarianTokenizer
model_name = 'Helsinki-NLP/opus-mt-en-ur'
"""
MODEL_NAME = os.environ.get("model_name")
if not MODEL_NAME:
raise ValueError("MODEL_NAME is not set. Please add it in the Hugging Face Secrets.")
"""
model = MarianMTModel.from_pretrained(model_name)
tokenizer = MarianTokenizer.from_pretrained(model_name)
# Function to translate text from English to Urdu
def translate_text(text):
inputs = tokenizer(text, return_tensors="pt", padding=True)
translated = model.generate(**inputs)
return tokenizer.decode(translated[0], skip_special_tokens=True)
# Streamlit app
st.title("Dataset Translator From English to Urdu For Chatbot")
st.title("Upload Csv file for translation into Urdu. Remember Csv file must contain Only Question and Answer Column")
# Upload CSV file
uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
if uploaded_file:
# Read the file into a pandas DataFrame
data = pd.read_csv(uploaded_file)
# Translate questions and answers
if 'Question' in data.columns and 'Answer' in data.columns:
data['Question_Urdu'] = data['Question'].apply(translate_text)
data['Answer_Urdu'] = data['Answer'].apply(translate_text)
# Display the translated dataframe
st.write(data)
# Provide option to download the translated CSV
translated_file = data.to_csv(index=False)
st.download_button("Download Translated CSV", translated_file, "Diabetes_Translated_Urdu.csv")
else:
st.error("CSV file must contain 'Question' and 'Answer' columns")
|