Delete app1.py

app1.py

@@ -1,311 +0,0 @@
-from langchain.document_loaders import PyPDFLoader, TextLoader, Docx2txtLoader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.document_loaders import PDFMinerLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain import HuggingFaceHub
-from langchain.chains.summarize import load_summarize_chain
-from langchain.chains.llm_summarization_checker.base import LLMSummarizationCheckerChain
-from langchain.prompts import PromptTemplate
-import os
-import gradio as gr
-import shutil
-import re
-import tempfile
-import cache
-from pathlib import Path
-
-
-api_token=os.environ['api']
-os.environ["HUGGINFACEHUB_API_TOKEN"]=api_token
-
-# api=userdata.get('api')
-# api_token=api
-# # api_token =
-# os.environ["HUGGINFACEHUB_API_TOKEN"]=api_token
-
-temp_dir = "/content/sample_data"
-
-# file_path_dummy = "/content/2401.10231.pdf"
-# if file_path_dummy.lower().endswith(".pdf") :
-#     loader = TextLoader(file_path_dummy)
-#     document= loader.load()
-# print(document)
-
-def data_ingestion(file_path):
-    if not os.path.exists(file_path):
-      raise ValueError(f"File path {file_path} does not exist.")
-
-    path = Path(file_path)
-    file_ext = path.suffix
-
-    # file_ext = os.path.splitext(file_path)[-1]
-    # if file_ext == ".pdf":
-
-    if file_path.lower().endswith(".pdf"):
-        loader = PDFMinerLoader(file_path)
-
-    elif file_path.lower().endswith(".txt"):
-        loader = TextLoader(file_path)
-
-    else:
-        loader = Docx2txtLoader(file_path)
-
-
-    # document= loader.load()
-
-    # loader = PDFMinerLoader(file_path)
-    document= loader.load()
-
-    length = len(document[0].page_content)
-
-    # Replace CharacterTextSplitter with RecursiveCharacterTextSplitter
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=0.03*length, chunk_overlap=0)
-    split_docs = text_splitter.split_documents(document)
-
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': 'cpu'})
-
-    llm = HuggingFaceHub(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
-                        model_kwargs={"temperature":1, "max_length":10000},
-                        huggingfacehub_api_token=api_token)
-
-    return split_docs
-
-# text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
-#     chunk_size=2000, chunk_overlap=0
-# )
-# split_docs = text_splitter.split_documents(document)
-
-# documents=split_text_into_batches(str(document),400)
-# len(documents)
-# documents[0]
-# #
-# from langchain.text_splitter import CharacterTextSplitter
-# text_splitter = CharacterTextSplitter(chunk_size=200, chunk_overlap=0)
-# documents = text_splitter.split_documents(document)
-# Embeddings
-
-# from langchain.chains.question_answering import load_qa_chain
-
-########## CHAIN 1 norm text
-
-def chain1():
-    prompt_template = """Your job is to write a summary of the document such that every summary of the text is of 2 sentences
-    here is the content of the section:
-    "{text}"
-
-    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"
-        "Given the new context, refine the original summary in English"
-        "If the context isn't useful, return the original summary." )
-
-    refine_prompt = PromptTemplate.from_template(refine_template)
-    chain1 = load_summarize_chain(
-        llm=HuggingFaceHub(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
-                        model_kwargs={"temperature":1, "max_length":10000},
-                        huggingfacehub_api_token=api_token),
-        chain_type="refine",
-        question_prompt=prompt,
-        # refine_prompt=refine_prompt,
-        return_intermediate_steps=False,
-        input_key="input_documents",
-        output_key="output_text",
-    )
-    return chain1
-
-# result = chain({"input_documents":split_docs}, return_only_outputs=True)
-
-########## CHAIN 2 research paper
-
-def chain2():
-    prompt_template = """Your job is to write a summary of the document such that every summary of the text is of 2 sentences
-    here is the content of the section:
-    "{text}"
-
-    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"
-        "Given the new context, refine the original summary in English"
-        "If the context isn't useful, return the original summary." )
-
-    refine_prompt = PromptTemplate.from_template(refine_template)
-    chain2 = load_summarize_chain(
-        llm = HuggingFaceHub(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
-                        model_kwargs={"temperature":1, "max_length":10000},
-                        huggingfacehub_api_token=api_token),
-        chain_type = "refine",
-        question_prompt = prompt,
-        # refine_prompt = refine_prompt,
-        return_intermediate_steps=False,
-        input_key="input_documents",
-        output_key="output_text",
-    )
-    return chain2
-
-# result = chain({"input_documents":split_docs}, return_only_outputs=True)
-
-########## CHAIN 3 arxiv_paper_1
-
-def chain3():
-    prompt_template = """You are being given a markdown document with headers, this is part of a larger arxiv paper. Your job is to write a summary of the document such that every summary of the text is of 2 sentences
-    here is the content of the section:
-    "{text}"
-
-    SUMMARY:"""
-    prompt = PromptTemplate.from_template(prompt_template)
-
-    refine_template = ("""You are presented with a collection of text snippets. Each snippet is a summary of a specific section from an academic paper published on arXiv. Your objective is to synthesize these snippets into a coherent, concise summary of the entire paper.
-
-        DOCUMENT SNIPPETS:
-        "{text}"
-
-        INSTRUCTIONS: Craft a concise summary below, capturing the essence of the paper based on the provided snippets.
-        It is also important that you highlight the key contributions of the paper, and 3 key takeaways from the paper.
-        Lastly you should provide a list of 5 questions that you would ask the author of the paper if you had the chance. Remove all the backslash n (\n)
-        SUMMARY:
-        """
-        )
-
-    refine_prompt = PromptTemplate.from_template(refine_template)
-    chain3 = load_summarize_chain(
-        llm=HuggingFaceHub(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
-                        model_kwargs={"temperature":1, "max_length":10000},
-                        huggingfacehub_api_token=api_token),
-        chain_type="refine",
-        question_prompt=prompt,
-        # refine_prompt=refine_prompt,
-        return_intermediate_steps=False,
-        input_key="input_documents",
-        output_key="output_text",
-    )
-    return chain3
-# result = chain({"input_documents":split_docs}, return_only_outputs=True)
-# chain.run(document)
-# print(result["output_text"])
-
-def chain_function(checkbox_values):
-    if "Research Paper" in checkbox_values:
-        output = chain3()
-    elif "Legal Document" in checkbox_values:
-        output = chain2()
-    elif "Study Material" in checkbox_values:
-        output = chain1()
-    else:
-        output = "Please select a document type to run."
-    return output
-
-def result(chain, split_docs):
-    summaries = []
-    for doc in split_docs:
-        result = chain({"input_documents": [doc]})
-        # result = chain({"input_documents": [doc]}, return_only_outputs=True)
-        summaries.append(result["output_text"])
-    text_concat = ""
-    for i in summaries:
-      text_concat += i
-    # output = re.sub(r'\n',"  ","   ",text_concat)
-    return text_concat
-
-title = """<p style="font-family:Century Gothic; text-align:center; font-size: 100px">S  I  M  P  L  I  F  Y</p>"""
-
-# description = r"""<p style="font-family: Century Gothic; text-align:center; font-size: 100px">S  I  M  P  L  I  F  Y</p>
-# """
-
-# article = r"""
-# If PhotoMaker is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/PhotoMaker' target='_blank'>Github Repo</a>. Thanks!
-# [![GitHub Stars](https://img.shields.io/github/stars/TencentARC/PhotoMaker?style=social)](https://github.com/TencentARC/PhotoMaker)
-# ---
-# 📝 **Citation**
-# <br>
-# If our work is useful for your research, please consider citing:
-# ```bibtex
-# @article{li2023photomaker,
-#   title={PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding},
-#   author={Li, Zhen and Cao, Mingdeng and Wang, Xintao and Qi, Zhongang and Cheng, Ming-Ming and Shan, Ying},
-#   booktitle={arXiv preprint arxiv:2312.04461},
-#   year={2023}
-# }
-# ```
-# 📋 **License**
-# <br>
-# Apache-2.0 LICENSE. Please refer to the [LICENSE file](https://huggingface.co/TencentARC/PhotoMaker/blob/main/LICENSE) for details.
-# 📧 **Contact**
-# <br>
-# If you have any questions, please feel free to reach me out at <b>zhenli1031@gmail.com</b>.
-# """
-
-# tips = r"""
-# ### Usage tips of PhotoMaker
-# 1. Upload more photos of the person to be customized to **improve ID fidelty**. If the input is Asian face(s), maybe consider adding 'asian' before the class word, e.g., `asian woman img`
-# 2. When stylizing, does the generated face look too realistic? Adjust the **Style strength** to 30-50, the larger the number, the less ID fidelty, but the stylization ability will be better.
-# 3. If you want to generate realistic photos, you could try switching to our other gradio application [PhotoMaker](https://huggingface.co/spaces/TencentARC/PhotoMaker).
-# 4. For **faster** speed, reduce the number of generated images and sampling steps. However, please note that reducing the sampling steps may compromise the ID fidelity.
-# """
-
-# def process_file(file_obj):
-#     destination_path = "/content/sample_data"  # Replace with your desired path
-#     shutil.copy(file_obj, destination_path)  # Save file to specified path
-#     return os.path.join(destination_path, file_obj)
-def process_file(list_file_obj):
-    # list_file_path = [x.name for x in list_file_obj if x is not None]
-    # file_content = file_obj.data
-    # with tempfile.TemporaryFile() as temp_file:
-    #     temp_file.write(file_content)
-    #     temp_file_path = temp_file.name
-    return list_file_obj[0].name
-
-def inference(checkbox_values, uploaded_file):
-    file_path = process_file(uploaded_file)
-    split_docs = data_ingestion(file_path)
-    chain = chain_function(checkbox_values)
-    summary = result(chain, split_docs)
-    return summary
-
-def main():
-    with gr.Blocks(theme="monochrome") as demo:
-        gr.Markdown(title)
-
-        with gr.Row():
-            with gr.Column():
-                checkbox_values = gr.CheckboxGroup(["Research Paper", "Legal Document", "Study Material"], label="Choose the document type")
-                uploaded_file = gr.Files(height=100, file_count="multiple", file_types=["text", ".docx", "pdf"], interactive=True, label="Upload your File.")
-                btn = gr.Button("Submit")  # Place the button outside the Row for vertical alignment
-            with gr.Column():
-                txt = gr.Textbox(
-                    show_label=False,
-                    # placeholder="Simplify."
-                )
-
-
-        btn.click(
-            fn=inference,
-            inputs=[checkbox_values, uploaded_file],
-            outputs=[txt],
-            queue=True
-    )
-    demo.queue()
-    demo.launch()
-
-if __name__ == "__main__":
-    # debug = True
-    # demo.queue()
-    # demo.launch()
-    main()
-