Update app.py

app.py

@@ -1,4 +1,80 @@
-import torch
+"""
+🗣️ Translator - Translate text from one language to another.
+Application file made with Streamlit.
+Author:
+    - @ChainYo
+"""
+
+import re
+import streamlit as st
+
+from datetime import datetime
+from transformers import pipeline
+from available_models import MODELS
+
+
+st.set_page_config(page_title="Translator", page_icon="🗣️")
+st.title("🗣️ Translator")
+
+st.write("To add a new model, hit me up! ⬆️")
+
+with st.expander(label="❓ How does it work", expanded=True):
+  
+
+lang1, lang2 = st.columns(2)
+lang1.selectbox(
+    "Source Language", ["🇫🇷 French"], 
+    key="input_lang", index=1,
+)
+lang2.selectbox(
+    "Target Language", ["🇬🇧 English"], 
+    key="output_lang", index=0,
+)
+
+selected_model = MODELS[f"{st.session_state['input_lang']}->{st.session_state['output_lang']}"]
+
+
+if selected_model[0] == None:
+    st.write("No model available for this pair.")
+elif selected_model[0] == 0:
+    st.write("No translation necessary.")
+else:
+    st.markdown(f"""
+    **Selected model:** [{selected_model[0]}]({selected_model[1]})
+    """)
+
+    input_text = st.text_area("Enter text to translate:", height=400, key="input")
+    translate_text = st.button("Translate")
+
+    if translate_text:
+        with st.spinner(text="⚙️ Model loading..."):
+            task = pipeline(
+                "translation",
+                model=selected_model[0],
+                tokenizer=selected_model[0],
+            )
+
+        progress_bar = st.progress(0)
+        with st.spinner(text="🔄 Translating..."):
+            text_to_translate = re.split('(?<=[.!?]) +', input_text)
+            total_progress = len(text_to_translate)
+
+            for i, text in enumerate(text_to_translate):
+                translation = task(text)
+                text_to_translate[i] = translation[0]["translation_text"]
+                progress_bar.progress((i + 1) / total_progress)
+
+        st.success("🗣️ Translated!")
+        st.write(f"**Translation:** {' '.join(text_to_translate)}")
+  
+            
+            
+            
+            
+'''            
+                        
+            
+            import torch
 from transformers import BartTokenizer, BartForConditionalGeneration
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline
@@ -32,4 +108,7 @@ def article_summarization(result):
 
 iface = gr.Interface(fn=article_summarization,title="Summarization in English",description="facebook/bart-large-cnn for summarization in English", inputs=gr.inputs.Textbox(lines=50, placeholder="Enter newspaper article to be summarized"), outputs=["result"])
 
-iface.launch()
+iface.launch()
+
+
+'''