Delete app-single.py

app-single.py

@@ -1,438 +0,0 @@
-"""
-This code uses the PyMuPDF package.
-
-PyMuPDF is AGPL licensed, please refer to:
-https://pymupdf.readthedocs.io/en/latest/about.html#license-and-copyright
-"""
-
-"""
-Code below is based on an implementation by Sunil Kumar Dash:
-
-MIT License
-
-Copyright (c) 2023 Sunil Kumar Dash
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
-"""
-
-from huggingface_hub import InferenceClient
-from langchain_openai import AzureOpenAIEmbeddings
-#from langchain_community.chat_models import AzureChatOpenAI
-from langchain_openai import AzureChatOpenAI
-
-
-
-
-
-from typing import Any
-import gradio as gr
-from langchain_openai import OpenAIEmbeddings
-from langchain_community.vectorstores import Chroma
-import chromadb
-#to handle the tenant issue
-chromadb.api.client.SharedSystemClient.clear_system_cache()
-
-from langchain.chains import ConversationalRetrievalChain
-from langchain_openai import ChatOpenAI
-
-from langchain_community.document_loaders import PyMuPDFLoader
-from langchain.schema.document import Document
-
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.memory import ConversationBufferMemory
-from langchain_community.llms import HuggingFaceEndpoint
-from langchain_community.embeddings import HuggingFaceEmbeddings 
-
-
-# for hugging face llm
-from transformers import AutoTokenizer
-import transformers
-import torch
-import tqdm 
-import accelerate
-
-
-import pymupdf
-from PIL import Image
-import os
-import re
-import uuid
-
-import os
-import wget
-import subprocess
-import urllib.request
-import requests
-
-from pathlib import Path
-from unidecode import unidecode
-
-api_key = os.getenv("OPENAI_API_KEY")
-user_agent = os.getenv("USER_AGENT")
-
-dr_link_url1 = os.getenv("DR_LINK_1")
-dr_link_url2 = os.getenv("DR_LINK_2") 
-azure_endpt = os.getenv("AZURE_ENDPT") 
-
-
-"""
-enable_box = gr.Textbox(
-    value=None, placeholder="Upload your OpenAI API key", interactive=True
-)
-disable_box = gr.Textbox(value="OpenAI API key is set", interactive=False)
-"""
-
-def set_apikey(api_key: str):
-    print("API Key set")
-    app.OPENAI_API_KEY = api_key
-    #return disable_box
-
-"""
-def enable_api_box():
-    return enable_box
-"""
-
-def add_text(history, text: str):
-    if not text:
-        raise gr.Error("enter text")
-    print("in add_text history="+str(history))
-    print("in add_text text="+str(text))
-    
-    history = history + [(text, "")]
-    return history
-
-
-class my_app:
-    def __init__(self, OPENAI_API_KEY: str = None) -> None:
-        print("init")
-        self.OPENAI_API_KEY: str = api_key
-        #self.chain = None
-        #self.chat_history: list = []
-        #self.N: int = 0
-        self.count: int = 0
-
-    def __call__(self, file: str) -> Any:
-        print("call")
-        #if self.count == 0:
-            #vincent added
-            #self.chain = None
-            #self.chat_history: list = []
-            #self.N: int = 0
-            #self.count: int = 0
-            
-            #self.chain = self.build_chain(file)
-            #self.count += 1
-            
-        #vincent added
-        
-        #else:
-            #self.chain = self.build_chain(file)
-            #self.count += 1
-            
-        #return self.chain
-    
-
-def process_file2(file: str):
-    
-    loader = PyMuPDFLoader(file.name)
-    documents = loader.load()
-    pattern = r"/([^/]+)$"
-    match = re.search(pattern, file.name)
-    try:
-        file_name = match.group(1)
-    except:
-        file_name = os.path.basename(file)
-        
-    return documents, file_name
-
-def create_collection_name(filepath):
-    # Extract filename without extension
-    collection_name = Path(filepath).stem
-    # Fix potential issues from naming convention
-    ## Remove space
-    collection_name = collection_name.replace(" ","-") 
-    ## ASCII transliterations of Unicode text
-    collection_name = unidecode(collection_name)
-    ## Remove special characters
-    #collection_name = re.findall("[\dA-Za-z]*", collection_name)[0]
-    collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
-    ## Limit length to 50 characters
-    collection_name = collection_name[:50]
-    ## Minimum length of 3 characters
-    if len(collection_name) < 3:
-        collection_name = collection_name + 'xyz'
-    ## Enforce start and end as alphanumeric character
-    if not collection_name[0].isalnum():
-        collection_name = 'A' + collection_name[1:]
-    if not collection_name[-1].isalnum():
-        collection_name = collection_name[:-1] + 'Z'
-    print('Filepath: ', filepath)
-    print('Collection name: ', collection_name)
-    return collection_name
-
-def build_qa_chain(collection_name, vector_db, file: str):
-    print("in build_qa_chain="+file.name) 
-    documents, file_name = process_file2(file)
-    # Load embeddings model
-    #embeddings = OpenAIEmbeddings(openai_api_key=self.OPENAI_API_KEY)
-
-    #vincent for old LLM
-    """
-    embeddings = AzureOpenAIEmbeddings(
-    model="text-embedding-ada-002",
-    # dimensions: Optional[int] = None, # Can specify dimensions with new text-embedding-3 models   
-    azure_endpoint=azure_endpt , # If not provided, will read env variable AZURE_OPENAI_ENDPOINT
-    openai_api_key=api_key, # Can provide an API key directly. If missing read env variable AZURE_OPENAI_API_KEY   
-    #openai_api_version="2023-05-15", # If not provided, will read env variable AZURE_OPENAI_API_VERSION
-    openai_api_version="2023-05-15", # If not provided, will read env variable AZURE_OPENAI_API_VERSION
-    )
-    """    
-    
-    #vincent for new LLM
-    embeddings = HuggingFaceEmbeddings()
-    
-    
-    #vincent added to handle the tenant problem 20250211
-    chromadb.api.client.SharedSystemClient.clear_system_cache()   
-    new_client = chromadb.EphemeralClient()
-    memory = ConversationBufferMemory(
-        memory_key="chat_history",
-        output_key='answer',
-        return_messages=True
-    )
-    
-    # added by vincent
-    text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=10)
-    chunked_documents = text_splitter.split_documents(documents)
-
-    #list_file_path = [x.name for x in list_file_obj if x is not None]
-    list_file_path = file.name 
-    # Create collection_name for vector database
-    # vincent fix InvalidCollectionException 20250212
-    #collection_name = create_collection_name(list_file_path[0])
-    collection_name = "pdf_docs_l_"+file.name[-10:]
-    
-    vector_db = Chroma.from_documents(
-            documents=chunked_documents,
-            embedding=embeddings,
-            client=new_client,
-            #collection_name=file_name,
-            #persist_directory = "db_" + file_name,
-            collection_name=collection_name,
-        )
-    """
-    chain = ConversationalRetrievalChain.from_llm(
-    ChatOpenAI(temperature=0.0, openai_api_key=self.OPENAI_API_KEY),
-    retriever=pdfsearch.as_retriever(search_kwargs={"k": 1}),
-    return_source_documents=True,
-    )
-    """
-
-    #vincent added for old LLM
-    """
-    chain = ConversationalRetrievalChain.from_llm(
-    #ChatOpenAI(temperature=0.0, openai_api_key=self.OPENAI_API_KEY),
-    
-    AzureChatOpenAI(
-                temperature=0.0, openai_api_key=api_key, api_version="2024-08-01-preview", 
-                model_name="gpt-4o", azure_endpoint=azure_endpt),
-    #vincent modified
-    retriever=vector_db.as_retriever(),
-    #retriever=pdfsearch.as_retriever(search_kwargs={"k": 1}),            
-    return_source_documents=True,
-    chain_type="stuff",     
-    memory=memory,
-    )
-    
-    """
-    #vincent for new LLM
-    llm_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
-    #llm_model = "meta-llama/Llama-2-7b-chat-hf"
-    llm = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            task="text-generation",  # Explicitly specify task
-            # model_kwargs={"temperature": temperature, "max_new_tokens": 250, "top_k": top_k}
-            temperature = 0.01,
-            max_new_tokens = 250,
-            top_k = 3,
-        )
-    
-    chain = ConversationalRetrievalChain.from_llm(
-        llm,
-        retriever=vector_db.as_retriever(),
-        chain_type="stuff", 
-        memory=memory,
-        # combine_docs_chain_kwargs={"prompt": your_prompt})
-        return_source_documents=True,
-        #return_generated_question=False,
-        verbose=False,
-    )
-    
-    #vincent added 20250211
-    app.count += 1
-    return collection_name, vector_db, chain
-
-
-def get_response(collection_name, vector_db, qa_chain, history, query, file):
-    #vincent added
-    set_apikey(api_key)
-    #print("in get_response count=" + str(app.count))
-    if not file:
-        raise gr.Error(message="Upload a PDF")
-        
-    formatted_chat_history = list(history)
-    formatted_chat_history = formatted_chat_history[:len(formatted_chat_history)-1]
-    print("in get_response query="+ query)
-    #print("in get_response chat_history="+ str(app.chat_history))
-    print("in get_response formatted_chat_history="+ str(formatted_chat_history))
-    #print("in get_response history="+ str(history))
-
-    chat_history_tuples = []
-    for message in formatted_chat_history:
-        chat_history_tuples.append((message[0], message[1]))
-
-    #vincent added 20250211
-    if app.count == 0:
-        collection_name, vector_db, qa_chain = build_qa_chain(collection_name, vector_db, file)    
-    result = qa_chain.invoke(
-        {"question": query, "chat_history": chat_history_tuples}, return_only_outputs=True
-        #{"question": query, "chat_history": format_chat_history(query, history)}, return_only_outputs=True
-    )
-    
-    
-    #app.chat_history += [(query, result["answer"])]
-    ##app.N = list(result["source_documents"][0])[1][1]["page"]
-    for char in result["answer"]:
-        history[-1][-1] += char
-        yield collection_name, vector_db, qa_chain, history, ""
-
-    #print("answer:"+ result["answer"])
-
-
-def render_file(file):
-    #print("in render_file="+file.name+" count="+str(app.count))
-    doc = pymupdf.open(file.name)
-    # vincent: issue in N
-    page = doc[N]
-    # Render the page as a PNG image with a resolution of 150 DPI
-    pix = page.get_pixmap(dpi=150)
-    image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
-    return image
-
-
-def purge_chat_and_render_first(file):
-    print("purge_chat_and_render_first")
-    # Purges the previous chat session so that the bot has no concept of previous documents
-    chat_history = []
-    history = []
-    #count = 0
-
-    #vincent added 20250211
-    #count = count + 1
-    app.count = 0
-
-    # Use PyMuPDF to render the first page of the uploaded document
-    doc = pymupdf.open(file.name)
-    page = doc[0]
-    # Render the page as a PNG image with a resolution of 150 DPI
-    pix = page.get_pixmap(dpi=150)
-    image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
-    return image, []
-
-app = my_app()
-
-with gr.Blocks() as demo:
-    
-    vector_db = gr.State()
-    qa_chain = gr.State()
-    collection_name = gr.State()
-    #N = gr.Number()
-    #count = gr.Number()
-
-    N = 0
-    count = 0
-    
-    #chat_history = gr.State()
-    #chat_history: list = []
-    #chat_history = []
-    
-    with gr.Column():
-        """
-        with gr.Row():
-
-            with gr.Column(scale=1):
-                api_key = gr.Textbox(
-                    placeholder="Enter OpenAI API key and hit <RETURN>",
-                    show_label=False,
-                    interactive=True
-                )
-        """
-        with gr.Row():
-            with gr.Column(scale=2):
-                with gr.Row():
-                    chatbot = gr.Chatbot(value=[], elem_id="chatbot")
-                with gr.Row():
-                    txt = gr.Textbox(
-                        show_label=False,
-                        placeholder="Enter text and press submit",
-                        scale=2
-                    )
-                    submit_btn = gr.Button("submit", scale=1)
-
-            with gr.Column(scale=1):
-                with gr.Row():
-                    show_img = gr.Image(label="Upload PDF")
-                with gr.Row():
-                    btn = gr.UploadButton("📁 upload a PDF", file_types=[".pdf"])
-
-    """
-    api_key.submit(
-        fn=set_apikey,
-        inputs=[api_key],
-        outputs=[
-            api_key,
-        ],
-    )
-    """
-
-    btn.upload(
-        fn=purge_chat_and_render_first,
-        inputs=[btn],
-        outputs=[show_img, chatbot],
-    )
-
-    submit_btn.click(
-        fn=add_text,
-        inputs=[chatbot, txt],
-        outputs=[
-            chatbot,
-        ],
-        queue=False,
-    ).success(
-        fn=get_response, inputs=[collection_name,vector_db, qa_chain, chatbot, txt, btn], outputs=[collection_name,vector_db, qa_chain, chatbot, txt]
-    ).success(
-        fn=render_file, inputs=[btn], outputs=[show_img]
-    )
-
-#demo.queue()
-#demo.launch(share=True, ssr_mode=False)
-#demo.launch()
-demo.queue().launch(share=True)
-#demo.launch(share=True)