Update app.py

app.py

@@ -1,7 +1,8 @@
 import gradio as gr
+import langcodes
 from transformers import pipeline
 from huggingface_hub import InferenceClient
-from language_directions import *
+from langdetect import detect, DetectorFactory
 # from IPython.display import Audio as IPythonAudio
 
 playground = gr.Blocks()
@@ -22,6 +23,17 @@ ner_pipe = pipeline("ner", model="dslim/bert-base-NER")
 #     audio = IPythonAudio(audio_data, rate=sampling_rate)
 #     return audio_data, sampling_rate
 
+def detect_language(text):
+    DetectorFactory.seed = 0  # Ensure consistent results
+    return detect(text)
+    
+def language_name_to_code(language_name):
+    try:
+        language = langcodes.find(language_name)
+        return language.language
+    except langcodes.LanguageTagError:
+        return None
+
 def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p,):
     messages = [{"role": "system", "content": system_message}]
 
@@ -64,12 +76,12 @@ def translate(input_text, source, target):
 
 def summarize_translate(input_text, target_lang):
     output = summary_pipe(input_text)
-    summary_origin = output[0]['summary_text']
-    source = 'en'
-    target_lang_dict = get_all_source_languages()
-    target = target_lang_dict[target_lang]
-    summary_translated = translate(summary_origin,source,target)
-    return summary_origin, summary_translated[0]
+    input_text_summary = output[0]['summary_text']
+    # source = 'en'
+    source = detect_language(input_text_summary)
+    target = language_name_to_code(target_lang)
+    summary_translated = translate(input_text_summary,source,target)
+    return input_text_summary, summary_translated[0]
 
 def merge_tokens(tokens):
     merged_tokens = []