class WForceConversationSummarizationTask:
    def get_chain(self):
        template = """Read a support chat between a customer and a support agent. \
Based on the informations given in the chat, answer in a few words (max. 80) what is the main subject of the conversation,\
stay conscious and be precise, explain what was defined and the result of the service without inferring information. At the end, give a list of 5 keywords which address the main topics, separated by comma. \
You must answer in Portuguese, with only a json containing two attributes: text and keywords.\
The suport chat data is: {question} \
"""

        prompt = PromptTemplate(template=template, input_variables=["question"])

        return LLMChain(
            llm=AzureChatOpenAI(openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"], azure_deployment=os.environ["AZURE_GPT35_DEPLOYMENT_NAME"], temperature=0.3),
            prompt=prompt,
            output_parser=WForceConversationSummarizationOutputParser()
        )

class WForceConversationSummarizationTask:
    def get_chain(self):
        template = """Read a support chat between a customer and a support agent. \
Based on the informations given in the chat, answer in a few words (max. 80) what is the main subject of the conversation,\
stay conscious and be precise, explain what was defined and the result of the service without inferring information. At the end, give a list of 5 keywords which address the main topics, separated by comma. \
You must answer in Portuguese, with only a json containing two attributes: text and keywords.\
The suport chat data is: {question} \
"""

        prompt = PromptTemplate(template=template, input_variables=["question"])

        return LLMChain(
            llm=AzureChatOpenAI(openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"], azure_deployment=os.environ["AZURE_GPT35_DEPLOYMENT_NAME"], temperature=0.3),
            prompt=prompt,
            output_parser=WForceConversationSummarizationOutputParser()
        )

task = WForceConversationSummarizationTask()
response = task.get_chain().invoke(input={"question": conversation}, config={"callbacks": [CallbackHandler()]})

task = WForceConversationSummarizationTask()
response = task.get_chain().invoke(input={"question": conversation}, config={"callbacks": [CallbackHandler()]})