Update app.py

app.py

@@ -10,36 +10,25 @@ all_langs = {iso[0]: (iso[1], iso[2], iso[3]) for iso in non_empty_isos} # {'Rom
 iso1toall = {iso[1]: (iso[0], iso[2], iso[3]) for iso in non_empty_isos} # {'ro': ('Romanian', 'rum', 'ron')}
 DEFAULTS = None
   
-models = ["Helsinki-NLP", "QUICKMT", "Argos", "HPLT", "HPLT-OPUS", "Google",
+libraries = ["langdetect", "QUICKMT", "Argos", "HPLT", "HPLT-OPUS", "Google",
           "Helsinki-NLP/opus-mt-tc-bible-big-mul-mul", "Helsinki-NLP/opus-mt-tc-bible-big-mul-deu_eng_nld",
           "Helsinki-NLP/opus-mt-tc-bible-big-mul-deu_eng_fra_por_spa", "Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-mul",
           "Helsinki-NLP/opus-mt-tc-bible-big-roa-deu_eng_fra_por_spa", "Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-roa", "Helsinki-NLP/opus-mt-tc-bible-big-roa-en",
-          "facebook/nllb-200-distilled-600M", "facebook/nllb-200-distilled-1.3B", "facebook/nllb-200-1.3B", "facebook/nllb-200-3.3B",
-          "facebook/mbart-large-50-many-to-many-mmt", "facebook/mbart-large-50-one-to-many-mmt", "facebook/mbart-large-50-many-to-one-mmt",
-          "facebook/hf-seamless-m4t-medium", "facebook/seamless-m4t-large", "facebook/seamless-m4t-v2-large",
-          "facebook/m2m100_418M", "facebook/m2m100_1.2B",
           "alirezamsh/small100", "naist-nlp/mitre_466m", "naist-nlp/mitre_913m",
           "bigscience/mt0-small", "bigscience/mt0-base", "bigscience/mt0-large", "bigscience/mt0-xl",
-          "bigscience/bloomz-560m", "bigscience/bloomz-1b1", "bigscience/bloomz-1b7", "bigscience/bloomz-3b",
-          "google/madlad400-3b-mt", "jbochi/madlad400-3b-mt",
-          "NiuTrans/LMT-60-0.6B", "NiuTrans/LMT-60-1.7B", "NiuTrans/LMT-60-4B",
-          "Lego-MT/Lego-MT", "BSC-LT/salamandraTA-2b-instruct",
           "winninghealth/WiNGPT-Babel", "winninghealth/WiNGPT-Babel-2", "winninghealth/WiNGPT-Babel-2.1",
-          "Unbabel/Tower-Plus-2B", "utter-project/EuroLLM-1.7B", "utter-project/EuroLLM-1.7B-Instruct",
-          "yanolja/YanoljaNEXT-Rosetta-4B-2511", "yanolja/YanoljaNEXT-Rosetta-4B",
-          "google-t5/t5-small", "google-t5/t5-base", "google-t5/t5-large",
           "google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large", "google/flan-t5-xl"]
 
 def timer(func):
     from time import time
     def translate(input_text) -> tuple[str, str]:
         start_time = time()
-        translated_text, message_text = func(input_text)
+        detected_lang, confidence = func(input_text)
         end_time = time()
         execution_time = end_time - start_time
         # print(f"Function {func.__name__!r} executed in {execution_time:.2f} seconds.")
-        message_text = f'Executed in {execution_time:.2f} seconds! {message_text}' 
-        return translated_text, message_text
+        execution_times = f'Executed in {execution_time:.2f} seconds!' 
+        return detected_lang, confidence, execution_times
     return translate
 
 @timer
@@ -53,7 +42,7 @@ def detect_language(input_text: str) -> tuple[str, str]:
     Returns:
         tuple: 
             detected_text(str): The input text translated to the selected target language
-            confidence(str):  A descriptive message summarizing the translation process. Example: "Translated from English to German with Helsinki-NLP."
+            confidence(str):  The confidence score as float
     
     Example:
         >>> detect_language("Hello world")
@@ -67,16 +56,18 @@ def detect_language(input_text: str) -> tuple[str, str]:
     return langcode, round(number=langecode_probabilities[0].prob * 100, ndigits=2)
     
 with gr.Blocks() as interface:
-    gr.Markdown("### Machine Text Translation with Gradio API and MCP Server")
+    gr.Markdown("### Language Detection with Gradio API and MCP Server")
     input_text = gr.Textbox(label="Enter text to detect:", placeholder="Type your text here, maximum 512 tokens",
                             autofocus=True, submit_btn='Detect Language', max_length=512) 
     with gr.Row(variant="compact"):
         detected_text = gr.Textbox(label="Translated text:", placeholder="Display field for translation", interactive=False, buttons=["copy"], lines=1)
-        confidence = gr.Textbox(label="Confidence:", placeholder="Display field for confidence score", interactive=False, lines=1)
+        confidence = gr.Textbox(label="Confidence:", placeholder="Display field for confidence score", interactive=False, buttons=["copy"], lines=1)
+        execution_time = gr.Textbox(label="Execution time:", placeholder="Display field for execution time", interactive=False, lines=1)
+    gr.CheckboxGroup(choices=["langdetect", "X", "XX"], label="Detection libraries", info="Detection libraries")
     input_text.submit(
     fn=detect_language,
     inputs=[input_text],
-    outputs=[detected_text, confidence]
+    outputs=[detected_text, confidence, execution_time]
     )
 if __name__ == "__main__":
     interface.launch(mcp_server=True, footer_links=["api", "settings"])