Update app.py

app.py

@@ -27,23 +27,23 @@ class Detect():
         import langid
         result: tuple[str, float] = langid.classify(self.text)
         langcode, langecode_probabilities = result
-        return langcode, round(number=langecode_probabilities * 10, ndigits=2)
+        return langcode, abs(round(number=langecode_probabilities * 10, ndigits=2))
 
 def detect_language(input_text: str, used_libraries: list[str]) -> tuple[str, str]:
     """
-    Detects the input text from the source language to the target language  using a specified model.
+    Detects the language of the input text.
 
     Parameters:
         input_text (str): The source text to be translated
         
     Returns:
-        tuple: 
-            detected_text(str): The input text translated to the selected target language
-            confidence(str):  The confidence score as float
+        list: 
+            detected_text(str): The language code of the input text
+            confidence(float):  The confidence score as float
     
     Example:
         >>> detect_language("Hello world")
-        ("en", 1.0)
+        ["en", 1.0]
     """
     detectinstance = Detect(input_text)
     detections = []
@@ -60,30 +60,24 @@ def detect_language(input_text: str, used_libraries: list[str]) -> tuple[str, st
     
 with gr.Blocks() as interface:
     gr.Markdown("### Language Detection with Gradio API and MCP Server")
-    input_text = gr.Textbox(label="Enter text to detect:", placeholder="Type/copy text here, maximum 512 tokens",
+    input_text = gr.Textbox(label="Enter text to detect:", placeholder="Type/copy text here, maximum 512 characters",
                             autofocus=True, submit_btn='Detect Language', max_length=512) 
-    # with gr.Row(variant="compact"):
-    #     detected_text = gr.Textbox(label="Detected language:", placeholder="Display field for detected language", interactive=False, buttons=["copy"], 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)
-    used_libraries = gr.CheckboxGroup(choices=libraries, label="Detection libraries", info="Detection libraries")
+    with gr.Row(variant="compact"):
+        used_libraries = gr.CheckboxGroup(choices=libraries, value=libraries, label="Detection libraries", info="Detection libraries", show_select_all=True)
     dataframe = gr.Dataframe(
-            headers=["Language", "Score"],
+            headers=["Language  code", "Score"],
             datatype=["str", "float"],  # type: array
             row_count=len(libraries),
             column_count=2,
             column_limits=(2, 3),
+            label='Language detection dataframe'
         )
     input_text.submit(
     fn=detect_language,
     inputs=[input_text, used_libraries],
     outputs=[dataframe]
     )
-    # input_text.submit(
-    # fn=detect_language,
-    # inputs=[input_text, used_libraries],
-    # outputs=[detected_text, confidence, execution_time]
-    # )
+    
 if __name__ == "__main__":
     interface.launch(mcp_server=True, footer_links=["api", "settings"])
     # interface.queue().launch(server_name="0.0.0.0", show_error=True, server_port=7860, mcp_server=True)