Update app.py

app.py

@@ -35,19 +35,6 @@ Example:
 {{"Answer":["General"]}}
 '''
 
-"""
-template_json = '''
-Your task is to read the following text, convert it to json format using 'Answer' as key and return it.
-<text>
-{RESPONSE}
-</text>
-
-Your final response MUST contain only the response, no other text.
-Example:
-{{"Answer":["General"]}}
-'''
-"""
-
 json_output_parser = JsonOutputParser()
 
 # Define the classify_text function
@@ -55,16 +42,9 @@ def classify_text(text):
     global llm
 
     start = time.time()
-    lang = detect(text)
-
-    language_map = {"tr": "turkish",
-                    "en": "english",
-                    "ar": "arabic",
-                    "es": "spanish",
-                    "it": "italian",
-                    }
-    try:
-        lang = language_map[lang]
+    try: 
+        lang = detect(text)
+
     except:
         lang = "en"
 
@@ -75,36 +55,27 @@ def classify_text(text):
     formatted_prompt = prompt_classify.format(TEXT=text, LANG=lang)
     classify = llm.invoke(formatted_prompt)
 
-    '''
-    prompt_json = PromptTemplate(
-        template=template_json,
-        input_variables=["RESPONSE"]
-    )
-    '''
-
-    #formatted_prompt = template_json.format(RESPONSE=classify)
-    #response = llm.invoke(formatted_prompt)
-
     parsed_output = json_output_parser.parse(classify)
     end = time.time()
     duration = end - start
-    return parsed_output, duration #['Answer']
+    return lang, parsed_output["Answer"][0], duration #['Answer']
 
 # Create the Gradio interface
-def gradio_app(text):
-    classification, time_taken = classify_text(text)
-    return classification, f"Time taken: {time_taken:.2f} seconds"
-
 def create_gradio_interface():
     with gr.Blocks() as iface:
         text_input = gr.Textbox(label="Text")
+        lang_output = gr.Textbox(label="Detected Language")
         output_text = gr.Textbox(label="Detected Topics")
         time_taken = gr.Textbox(label="Time Taken (seconds)")
         submit_btn = gr.Button("Detect topic")
 
-        submit_btn.click(fn=classify_text, inputs=text_input, outputs=[output_text, time_taken])
+        def on_submit(text):
+            lang, classification, duration = classify_text(text)
+            return lang, classification, f"Time taken: {duration:.2f} seconds"
+
+        submit_btn.click(fn=on_submit, inputs=text_input, outputs=[lang_output, output_text, time_taken])
 
     iface.launch()
 
 if __name__ == "__main__":
-    create_gradio_interface()
+    create_gradio_interface()