Update app.py

app.py

@@ -1,4 +1,38 @@
 import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("EzekielMW/Eksl_dataset")
+model = AutoModelForSeq2SeqLM.from_pretrained("EzekielMW/Eksl_dataset")
+
+def translate(text, source_language, target_language):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    inputs = tokenizer(text.lower(), return_tensors="pt").to(device)
+    inputs['input_ids'][0][0] = tokenizer.convert_tokens_to_ids(source_language)
+    translated_tokens = model.to(device).generate(
+        **inputs,
+        forced_bos_token_id=tokenizer.convert_tokens_to_ids(target_language),
+        max_length=100,
+        num_beams=5,
+    )
+    result = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
+
+    if target_language == 'ksl':
+        result = result.upper()
+
+    return result
+
+st.title('Translation App')
+
+# Text input
+text = st.text_input('Enter text to translate')
+source_language = st.selectbox('Source Language', ['eng', 'swa', 'ksl'])
+target_language = st.selectbox('Target Language', ['eng', 'swa', 'ksl'])
+
+if st.button('Translate'):
+    if text:
+        translation = translate(text, source_language, target_language)
+        st.write(f'Translation: {translation}')
+    else:
+        st.write('Please enter text to translate.')