Delete sum_model.py

sum_model.py

@@ -1,153 +0,0 @@
-from ipex_llm.langchain.llms import TransformersLLM
-from langchain import LLMChain
-from langchain.chains.summarize import load_summarize_chain
-from langchain.docstore.document import Document
-from langchain.prompts import PromptTemplate
-from langchain.chains.combine_documents.stuff import StuffDocumentsChain
-from langchain.chains import MapReduceDocumentsChain, ReduceDocumentsChain
-from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
-
-
-class Sum():
-    def __init__(self, args):
-        self.llm_version = args.llm_version
-        # self.max_tokens = args.qa_max_new_tokens
-
-    def summarize_refine(self, script):
-        text_splitter = CharacterTextSplitter(chunk_size=1024, separator="\n", chunk_overlap=0)
-        texts = text_splitter.split_text(script)
-        docs = [Document(page_content=t) for t in texts]
-        llm = TransformersLLM.from_model_id_low_bit(f"checkpoint\\{self.llm_version}")
-
-        prompt_template = """Write a concise summary of the following:
-        {text}
-        CONCISE SUMMARY:"""
-        prompt = PromptTemplate.from_template(prompt_template)
-        refine_template = (
-            "Your job is to produce a final summary\n"
-            "We have provided an existing summary up to a certain point: {existing_answer}\n"
-            "We have the opportunity to refine the existing summary"
-            "(only if needed) with some more context below.\n"
-            "------------\n"
-            "{text}\n"
-            "------------\n"
-            "If the context isn't useful, return the original summary."
-        )
-        refine_prompt = PromptTemplate.from_template(refine_template)
-        chain = load_summarize_chain(
-            llm=llm,
-            chain_type="refine",
-            question_prompt=prompt,
-            refine_prompt=refine_prompt,
-            return_intermediate_steps=True,
-            input_key="input_documents",
-            output_key="output_text",
-        )
-        result = chain({"input_documents": docs}, return_only_outputs=True)
-
-        return result
-
-    def summarize_mapreduce(self, script):
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)
-        texts = text_splitter.split_text(script)
-        text = [Document(page_content=t) for t in texts]
-
-        llm = TransformersLLM.from_model_id_low_bit(f"checkpoint\\{self.llm_version}")
-
-        # Map
-        map_template = """The following is a meeting recording
-        =========
-        {texts}
-        =========
-        Based on this list of recordings, please summary the main idea briefly
-        Helpful Answer:"""
-        map_prompt = PromptTemplate.from_template(map_template)
-        map_chain = LLMChain(llm=llm, prompt=map_prompt, llm_kwargs={"max_new_tokens": 512})
-
-        # Reduce
-        reduce_template = """The following is set of summaries:
-        =========
-        {texts}
-        =========
-        Take these and distill it into a final, consolidated summary of the meeting. 
-        Helpful Answer:"""
-        reduce_prompt = PromptTemplate.from_template(reduce_template)
-        reduce_chain = LLMChain(llm=llm, prompt=reduce_prompt, llm_kwargs={"max_new_tokens": 4096})
-
-        # Takes a list of documents, combines them into a single string, and passes this to an LLMChain
-        combine_documents_chain = StuffDocumentsChain(
-            llm_chain=reduce_chain, document_variable_name="texts"
-        )
-
-        # Combines and iteratively reduces the mapped documents
-        reduce_documents_chain = ReduceDocumentsChain(
-            combine_documents_chain=combine_documents_chain,
-            collapse_documents_chain=combine_documents_chain,
-            token_max=4000,
-        )
-
-        # Combining documents by mapping a chain over them, then combining results
-        map_reduce_chain = MapReduceDocumentsChain(
-            llm_chain=map_chain,
-            reduce_documents_chain=reduce_documents_chain,
-            document_variable_name="texts",
-            return_intermediate_steps=False,
-        )
-
-        result = map_reduce_chain({"input_documents": text}, return_only_outputs=True)
-        # print("-." * 40)
-        # print(result)
-        result = result['output_text'].split("Helpful Answer:").strip()[-1]
-        return result
-
-    def summarize(self, script):
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=0)
-        texts = text_splitter.split_text(script)
-
-        prompt_template = """The following is a piece of meeting recording:
-        <<<{text}>>>
-        Based on recording, summary the main idea fluently. 
-        JUST SUMMARY!NO OTHER WORDS!
-        SUMMARY:"""
-
-        reduce_template = """The following is a meeting recording pieces:
-        <<<{text}>>>
-        Take these and distill it into a final, consolidated summary of the meeting. 
-        JUST SUMMARY!NO OTHER WORDS!
-        SUMMARY:"""
-
-        print(len(texts))
-        for text in texts:
-            print(text)
-            print("\n")
-
-        llm = TransformersLLM.from_model_id_low_bit(
-            f"checkpoint\\{self.llm_version}")
-        sum_split = []
-
-        for text in texts:
-            response = llm(prompt=prompt_template.format(text=text), max_new_tokens=1024)
-            print(response)
-            response_answer = response.split("SUMMARY:")
-
-            sum_split.append(response_answer[1])
-
-        sum_all = "\n".join(sum_split)
-
-        result = llm(prompt=reduce_template.format(text=sum_all), max_new_tokens=4000)
-        result_split = result.split("SUMMARY:")
-        return result_split[1]
-
-# # for test
-# import argparse
-#
-# parser = argparse.ArgumentParser()
-# parser.add_argument("--llm_version", default="Llama-2-7b-chat-hf-INT4", help="LLM model version")
-# args = parser.parse_args()
-# file_path = "../test.txt"
-# with open(file_path, "r", encoding="utf-8") as file:
-#     content = file.read()
-# Sumbot = Sum(args)
-# result = Sumbot.summarize_map(content)
-# print("-." * 20)
-# print(result)