Upload app.py

app.py

@@ -15,25 +15,25 @@ llm = HuggingFaceEndpoint(
 )
 llm_engine_hf = ChatHuggingFace(llm=llm)
 
-# Update the template to extract topic information
 template_classify = '''
-Please read the following text written in {LANG} language and extract the main topics discussed in it.
-You can list more than one topic or topics sentence by sentence. List the topics clearly.
+Please carefully read the following text. The text is written in {LANG} language:
 
 <text>  
 {TEXT}  
 </text>
+
+After reading it, I want you to extract topic informations from text.
 '''
 
 template_json = '''
-Your task is to read the following extracted topics and convert them into JSON format using 'Topics' as the key.
-
+Your task is to read the following text, convert it to json format using 'Answer' as key and return it.
 <text>
 {RESPONSE}
 </text>
 
-The final response should be in this format:
-{{"Topics": ["Topic1", "Topic2", ...]}}
+Your final response MUST contain only the response, no other text.
+Example:
+{{"Answer":"Positive"}}
 '''
 json_output_parser = JsonOutputParser()
 
@@ -62,7 +62,7 @@ def classify_text(text):
     parsed_output = json_output_parser.parse(response)
     end = time.time()
     duration = end - start
-    return parsed_output, duration
+    return parsed_output, duration #['Answer']
 
 # Create the Gradio interface
 def gradio_app(text):
@@ -71,12 +71,12 @@ def gradio_app(text):
 
 def create_gradio_interface():
     with gr.Blocks() as iface:
-        text_input = gr.Textbox(label="Text to Classify")
-        output_text = gr.Textbox(label="Extracted Topics")
+        text_input = gr.Textbox(label="Text")
+        output_text = gr.Textbox(label="Classify")
         time_taken = gr.Textbox(label="Time Taken (seconds)")
-        submit_btn = gr.Button("Extract Topics")
+        submit_btn = gr.Button("Classify")
 
-        submit_btn.click(fn=gradio_app, inputs=text_input, outputs=[output_text, time_taken])
+        submit_btn.click(fn=classify_text, inputs=text_input, outputs=[output_text, time_taken])
 
     iface.launch()