Create app.py

app.py

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+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
+
+from google.colab import userdata
+api=userdata.get('api')
+api_token=api
+# api_token =
+os.environ["HUGGINFACEHUB_API_TOKEN"]=api_token
+
+temp_dir = "/content/sample_data"
+
+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":
+    #     # loader = PyPDFLoader(file_path)
+    #     loader = PDFMinerLoader(file_path)
+    #     document= loader.load()
+
+    # elif file_ext in {".docx", ".doc"}:
+    #     loader = Docx2txtLoader(file_path)
+    #     document= loader.load()
+
+    # elif file_ext == ".txt":
+    #     loader = TextLoader(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=500, 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 = """Write a concise summary of the following:
+    {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 = """This is a Research Paper,your job is to summarise the text portion without any symbols or special characters, skip the mathematical equations for now:
+    {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.
+        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
+
+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,scale=2,
+                # placeholder="Simplify."
+            )
+
+
+    btn.click(
+        fn=inference,
+        inputs=[checkbox_values, uploaded_file],
+        outputs=[txt],
+        queue=False
+    )
+# debug = True
+demo.launch(debug = True)
+