Update app.py

app.py

@@ -1,62 +1,59 @@
-import gradio as gr
+import streamlit as st
 from transformers import T5Tokenizer, T5ForConditionalGeneration, AutoTokenizer, AutoModelForSeq2SeqLM
 
-def translate_text(input_text, sselected_language, tselected_language, model_name):
-    langs = {"English": "en", "Romanian": "ro", "German": "de", "French": "fr", "Spanish": "es"}
-    sl = langs[sselected_language]
-    tl = langs[tselected_language]
-
-    if model_name == "Helsinki-NLP":
-        try:
-            model_name_full = f"Helsinki-NLP/opus-mt-{sl}-{tl}"
-            tokenizer = AutoTokenizer.from_pretrained(model_name_full)
-            model = AutoModelForSeq2SeqLM.from_pretrained(model_name_full)
-        except EnvironmentError:
-            model_name_full = f"Helsinki-NLP/opus-tatoeba-{sl}-{tl}"
-            tokenizer = AutoTokenizer.from_pretrained(model_name_full)
-            model = AutoModelForSeq2SeqLM.from_pretrained(model_name_full)
-    else:
-        tokenizer = T5Tokenizer.from_pretrained(model_name)
-        model = T5ForConditionalGeneration.from_pretrained(model_name)
+# Create the app layout
+st.header("Text Machine Translation")
+input_text = st.text_input("Enter text to translate:")
+# Create a list of options for the select box
+options = ["German", "Romanian", "English", "French", "Spanish"]
+langs = {"English":"en", "Romanian":"ro", "German":"de", "French":"fr", "Spanish":"es"}
+models = ["Helsinki-NLP", "t5-base", "t5-small", "t5-large"]
 
-    if model_name.startswith("Helsinki-NLP"):
+# Create two columns
+scol, tcol = st.columns(2)
+# Place select boxes in columns
+with scol:
+    sselected_language = st.selectbox("Source language:", options, index=0, placeholder="Select source language")
+with tcol:
+    tselected_language = st.selectbox("Target language:", options, index=1, placeholder="Select target language")
+model_name = st.selectbox("Select a model:", models, index=0, placeholder="Select language model")
+
+sl = langs[sselected_language]
+tl = langs[tselected_language]
+
+st.session_state["sselected_language"] = sselected_language
+st.session_state["tselected_language"] = tselected_language
+st.session_state["model_name"] = model_name
+
+if model_name == 'Helsinki-NLP':
+    try:
+        model_name = f"Helsinki-NLP/opus-mt-{sl}-{tl}"
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    except EnvironmentError:
+        model_name = f"Helsinki-NLP/opus-tatoeba-{sl}-{tl}"
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+else:
+    tokenizer = T5Tokenizer.from_pretrained(model_name)
+    model = T5ForConditionalGeneration.from_pretrained(model_name)
+st.write("Selected language combination:", sselected_language, " - ", tselected_language, "Selected model:", model_name)
+submit_button = st.button("Translate")
+translated_textarea = st.text("")
+
+# Handle the submit button click
+if submit_button:
+    if model_name.startswith('Helsinki-NLP'):
         prompt = input_text
     else:
-        prompt = f"translate {sselected_language} to {tselected_language}: {input_text}"
-
-    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+        prompt = f'translate {sselected_language} to {tselected_language}: {input_text}'
+    print(prompt)
+    input_ids = tokenizer.encode(prompt, return_tensors='pt')
+    # Perform translation
     output_ids = model.generate(input_ids)
+    # Decode the translated text
     translated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-
-    return translated_text
-
-options = ["German", "Romanian", "English", "French", "Spanish"]
-models = ["Helsinki-NLP", "t5-base", "t5-small", "t5-large"]
-
-def create_interface():
-    with gr.Blocks() as interface:
-        gr.Markdown("## Text Machine Translation")
-
-        with gr.Row():
-            input_text = gr.Textbox(label="Enter text to translate:", placeholder="Type your text here...")
-
-        with gr.Row():
-            sselected_language = gr.Dropdown(choices=options, value="English", label="Source language")
-            tselected_language = gr.Dropdown(choices=options, value="German", label="Target language")
-
-        model_name = gr.Dropdown(choices=models, value="Helsinki-NLP", label="Select a model")
-        translate_button = gr.Button("Translate")
-
-        translated_text = gr.Textbox(label="Translated text:", interactive=False)
-
-        translate_button.click(
-            translate_text, 
-            inputs=[input_text, sselected_language, tselected_language, model_name], 
-            outputs=translated_text
-        )
-
-    return interface
-
-# Launch the Gradio interface
-interface = create_interface()
-interface.launch()
+    # Display the translated text
+    print(translated_text)
+    st.write(f"Translated text from {sselected_language} to {tselected_language} using {model_name}")
+    translated_textarea = st.text(translated_text)