Delete generate_text.py

generate_text.py

@@ -1,78 +0,0 @@
-from langchain.base_language import BaseLanguageModel
-from langchain.chains import LLMChain, SequentialChain
-from langchain.chat_models import ChatAnthropic
-from langchain.chat_models import ChatOpenAI
-from langchain.llms import HuggingFaceHub
-from langchain.prompts import (
-    PromptTemplate,
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-
-
-class GenerateStyleText:
-    example: str
-    prompt: str
-    llm: BaseLanguageModel
-
-    def __init__(self, example=None, prompt=None, llm=None):
-        self.example = example
-        self.prompt = prompt
-        self.llm = llm
-
-    def set_imp_llm(self, model):
-        if model == 'GPT3':
-            self.llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k")
-        elif model == "GPT4":
-            self.llm = ChatOpenAI(model_name="gpt-4")
-        elif model == "Claude":
-            self.llm = ChatAnthropic()
-        else:
-            self.llm = HuggingFaceHub(repo_id=model)
-
-    def run(self):
-        return self.process()
-
-    def process(self):
-        seq_chain = SequentialChain(
-            chains=[self.get_extract_tone_chain(), self.get_generate_text_chain(self.prompt),
-                    self.get_apply_style_chain()],
-            input_variables=["text"], verbose=True)
-        result = seq_chain({'text': self.example, "style": ""})
-        return str(result.get('result'))
-
-    def create_chain(self, chat_prompt, output_key):
-        return LLMChain(llm=self.llm,
-                        prompt=chat_prompt, output_key=output_key)
-
-    def get_extract_tone_chain(self):
-        template = """Based on the tone and writing style in the seed text, create a style guide for a blog or 
-        publication that captures the essence of the seed’s tone. Emphasize engaging techniques that help readers 
-        feel connected to the content.
-        """
-        system_message_prompt = SystemMessagePromptTemplate.from_template(template)
-        human_template = "{text}"
-        human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
-        chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
-
-        return self.create_chain(chat_prompt, "style")
-
-    def get_generate_text_chain(self, prompt):
-        template = """Generate a text following the user_request(use same language of the request):
-                      {user_request}
-                      """.replace("{user_request}", prompt)
-        return self.create_chain(PromptTemplate.from_template(template),
-                                 "generated_text")
-
-    def get_apply_style_chain(self):
-        template = """STYLE:
-                    {style}
-                    REWRITE THE TEXT BELLOW APPLYING THE STYLE ABOVE(use same language of the request), 
-                    ONLY GENERATE NEW TEXT BASED ON THE STYLE CONTEXT, DO NOT COPY STYLE EXACT PARTS:
-                    {generated_text}
-        """
-
-        prompt = PromptTemplate.from_template(template=template)
-        prompt.partial(style="")
-        return self.create_chain(prompt, "result")