Update app.py

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 from langchain.prompts import PromptTemplate
 from langchain_huggingface import HuggingFaceEndpoint
 from langchain_core.output_parsers import JsonOutputParser
-from langdetect import detect
 import time
 
 # Initialize the LLM and other components
@@ -16,15 +15,17 @@ llm = HuggingFaceEndpoint(
 
 template_classify = '''
 You are an irrelevant text detector bot.
-In social media, there are lots of bot accounts and they produce non related text on company hashtag.
-You will be provided company informations such as company name, company sector and information about company and text from social media.
-You will classify it as RELEVANT or IRRELEVANT.
+In social media, there are lots of bot accounts or real people and they produce irrelevant/non-related text about company and its services.
+You will be provided company informations such as company name, company sector and information about company.
+Using this informations about company, think about its services and sector, if given text is relevant to the company classify it as RELEVANT,
+if the given text is not relevant to the company classify it as IRRELEVANT
 
+Provided information:
 Company name: {COMPANY_NAME}
 Company sector: {COMPANY_SECTOR}
 About Company: {ABOUT_COMPANY}
 
-Detect following text as RELEVANT OR IRRELEVANT:
+Detect following text as RELEVANT OR IRRELEVANT based on Company Name, Company Sector, About Company:
 
 <text>  
 {TEXT}  
@@ -54,17 +55,6 @@ def classify_text(text, company_name_input, company_sector_input, about_company_
                                               ABOUT_COMPANY=about_company_input
                                              )
     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
@@ -90,4 +80,4 @@ def create_gradio_interface():
     iface.launch()
 
 if __name__ == "__main__":
-    create_gradio_interface()
+    create_gradio_interface()