Delete LVEBotG2.0.py

LVEBotG2.0.py

@@ -1,82 +0,0 @@
-import gradio as gr
-import os
-
-os.environ["OPENAI_API_KEY"] = "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"
-
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores import Chroma
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.llms import OpenAI
-from langchain.chains import ConversationalRetrievalChain
-from langchain.document_loaders import DirectoryLoader
-
-
-
-
-txt_loader = DirectoryLoader('d:\coding\data\lve', glob="**/*.txt")
-pdf_loader = DirectoryLoader('d:\coding\data\LVE', glob="**/*.pdf")
-doc_loader = DirectoryLoader('d:\coding\data\LVE', glob="**/*.docx")
-loaders = [pdf_loader, txt_loader, doc_loader]
-documents = []
-
-for loader in loaders:
-    documents.extend(loader.load())
-    
-print(f"Total # of documents: {len(documents)}")
-
-text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-documents = text_splitter.split_documents(documents)
-
-embeddings = OpenAIEmbeddings()
-vectorstore = Chroma.from_documents(documents, embeddings)
-
-from langchain.memory import ConversationBufferMemory
-memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-
-qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), memory=memory)
-
-chat_history = []
-
-def submit_callback(user_message):
-    default_prompt = " Please format your response in the following way: Each statement should be in a newline . "
-    prompt = default_prompt + user_message
-    
-    # Process user input and generate chatbot response
-    response = qa({"question": prompt, "chat_history": chat_history})
-    chat_history.append((prompt, response["answer"]))
-    return response["answer"]
-
-iface = gr.Interface(
-    fn=submit_callback,
-    inputs=gr.inputs.Textbox(lines=2, label="Enter your query"),
-    outputs=gr.outputs.Textbox(label="Chatbot Response"),
-    #outputs=gr.outputs.HTML(label="Chatbot Response"),
-    title="LVE Torpedoes Chatbot",
-    layout="vertical",
-    description="Enter your query to chat with the LVET chatbot",
-    examples=[
-        ["What are the practice times for each age group ?"],
-        ["What are the eligibility criteria for the Mini Torpedoes program?"],
-        ["What is the eligibility to participate in the LVET Swim Team?"],
-        ["How many volunteer hours are required per family during the swim season?"],
-        ["What strokes can swimmers participate in at swim meets?"],
-        ["How are swimmers grouped for practice?"],
-        ["When do evaluations take place for new swimmers?"],
-        ["Who are LVET's Board Members"],
-        ["How can I read swim meet results ?"],
-        ["How can I contact LVET's Board Members?"],
-        ["What is the penalty for not meeting the required volunteer hours?"],
-        ["Volunteer Hours?"],
-        ["Registration info?"],
-        ["How do I sign up for volunteer jobs to fulfill my volunteer hours?"],
-        ["Volunteer jobs that do not require certification or prior experience"],
-        ["What are the responsibilities of an Age Group Coordinator?"],
-        ["How do I commit my swimmer for meets/events?"],
-        ["What age groups and races does the LVET Swim Team participate in?"]
-    ],
-    theme="default"
-    
-)
-iface.launch(share=True)
-while True:
-    pass