Update app.py

app.py

@@ -104,7 +104,7 @@ class Translators:
                 return self.HelsinkiNLP_mulroa()
         except KeyError as error:
             return f"Error: Translation direction {self.sl} to {self.tl} is not supported by Helsinki Translation Models", error
-
+   
     def LLaMAX(self):
         pipe = pipeline("text-generation", model="LLaMAX/LLaMAX3-8B")
         messages = [
@@ -163,8 +163,8 @@ class Translators:
         model = T5ForConditionalGeneration.from_pretrained(self.model_name, device_map="auto")
         prompt = f"translate {self.sl} to {self.tl}: {self.input_text}"
         input_ids = tokenizer.encode(prompt, return_tensors="pt")
-        output_ids = model.generate(input_ids, max_length=512)
-        translated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True).strip()
+        output_ids = model.generate(input_ids, max_length=512) # Perform translation
+        translated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True).strip() # Decode the translated text
         return translated_text
     
     def mbart_many_to_many(self):
@@ -519,70 +519,24 @@ st.session_state["sselected_language"] = sselected_language
 st.session_state["tselected_language"] = tselected_language
 st.session_state["model_name"] = model_name
 
-# magic = f'Selected language combination: {sselected_language} - {tselected_language}. Selected model: {model_name}'
-# st.write(magic)
-f'Selected language combination: {sselected_language} - {tselected_language}. Selected model: {model_name}'
+st.write(f'Selected language combination: {sselected_language} - {tselected_language}. Selected model: {model_name}')
 
 with st.container(border=None, width="stretch", height="content", horizontal=False, horizontal_alignment="center", vertical_alignment="center", gap="small"):
     submit_button = st.button("Translate")
-# Show text area with placeholder
+# Show text area with placeholder also before translating
 # translated_textarea = st.empty()
 # message_textarea = st.empty()
 # translated_textarea.text_area(":green[Translation:]", placeholder="Translation area", value='')
 # message_textarea.text_input(":blue[Messages:]", placeholder="Messages area", value='')
 
-# Handle the submit button click
-if submit_button:
+if submit_button: # Handle the submit button click
     with st.spinner("Translating...", show_time=True):
-        translated_text, message = translate_text(model_name, sselected_language, tselected_language, input_text)
-        # if model_name.startswith('Helsinki-NLP'):
-        #     # input_ids = tokenizer.encode(input_text, 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)
-        #     # Use a pipeline as a high-level helper
-        #     try:
-        #         model_name = f"Helsinki-NLP/opus-mt-{sl}-{tl}"
-        #         tokenizer = AutoTokenizer.from_pretrained(model_name)
-        #         model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-        #         pipe = pipeline("translation", model=model, tokenizer=tokenizer)
-        #     except (EnvironmentError, OSError):
-        #         model_name = f"Helsinki-NLP/opus-tatoeba-{sl}-{tl}"
-        #         tokenizer = AutoTokenizer.from_pretrained(model_name)
-        #         model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-        #         pipe = pipeline("translation", model=model, tokenizer=tokenizer)
-        #     translation = pipe(input_text)
-        #     translated_text = translation[0]['translation_text']
-    
-        # elif model_name.startswith('t5'):
-        #     tokenizer = T5Tokenizer.from_pretrained(model_name)
-        #     model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
-        #     prompt = f'translate {sselected_language} to {tselected_language}: {input_text}'
-        #     input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
-        #     # Perform translation
-        #     output_ids = model.generate(input_ids)
-        #     # Decode the translated text
-        #     translated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-        # elif 'Unbabel' in model_name:   
-        #     pipe = pipeline("text-generation", model=model_name, torch_dtype=torch.bfloat16, device_map="auto")
-        #     # We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
-        #     messages = [{"role": "user",
-        #                  "content": f"Translate the following text from {sselected_language} into {tselected_language}.\n{sselected_language}: {input_text}.\n{tselected_language}:"}]
-        #     prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
-        #     outputs = pipe(prompt, max_new_tokens=256, do_sample=False)
-        #     translated_text = outputs[0]["generated_text"]
-        #     start_marker = "<end_of_turn>"
-        #     if start_marker in translated_text:
-        #         translated_text = translated_text.split(start_marker)[1].strip()
-        #     translated_text = translated_text.replace('Answer:', '').strip() if translated_text.startswith('Answer:') else translated_text
-        
-    # Display the translated text
+        translated_text, message = translate_text(model_name, sselected_language, tselected_language, input_text)       
     print(f"Translated from {sselected_language} to {tselected_language} using {model_name}.", input_text, translated_text)
-    # message = f"Translated from {sselected_language} to {tselected_language} using {model_name}."
+    # Display the translated text
     # translated_textarea.text_area(":green[Translation:]", value=translated_text)
     # message_textarea.text_input(":blue[Message:]", value=message)
-    st.text_area(":green[Translation:]", placeholder="Translation area", value=translated_text)
+    st.text_area(":green[Translation:]", value=translated_text)
     # st.success(message, icon=":material/check:") st.info(message, icon="ℹ️"), st.warning(message, icon=":material/warning:"), error(message, icon=":material/error:"), st.exception
     st.info(message, icon=":material/info:")
     # st.text_input(":blue[Messages:]", value=message)