Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from transformers import MarianMTModel, MarianTokenizer
+
+# Load the model and tokenizer
+model_name = 'Helsinki-NLP/opus-mt-en-ur'
+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("Diabetes Dataset Translator")
+
+# 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")