Delete app.py

app.py

@@ -1,153 +0,0 @@
-from typing import Optional
-import chainlit as cl
-from langchain_chroma import Chroma
-from langchain_core.prompts import PromptTemplate
-from langchain_core.callbacks import CallbackManager, AsyncCallbackManagerForLLMRun
-from langchain_community.llms import LlamaCpp
-from chainlit.types import ThreadDict
-from langchain.chains import RetrievalQA, ConversationChain
-from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
-from langchain.chains.conversation.memory import ConversationBufferMemory
-
-
-
-# ctransformers is no longer used
-""" from langchain_community.llms import CTransformers
-
-# Initialize the language model
-llm = CTransformers(model='Model/llama-2-7b-chat.ggmlv3.q2_K.bin',  # 2 bit quantized model
-                    model_type='llama',
-                    config={'max_new_tokens': 256,  # max tokens in reply
-                            'temperature': 0.01, }  # randomness of the reply
-                    )
- """
-
-# Initialize the language model with LlamaCpp
-llm = LlamaCpp(model_path="Model/llama-2-7b-chat.Q4_K_M.gguf",  #  token streaming to terminal
-               device="cpu",verbose = True, max_tokens = 4096,  #offloads ALL layers to GPU, uses around 6 GB of Vram
-               config={  # max tokens in reply
-                       'temperature': 0.75}  # randomness of the reply
-               )
-
-DATA_PATH = 'Data/'
-
-DB_CHROMA_PATH = 'vectorstore/db_chroma'
-
-embedding_function = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device': 'cpu'})
-
-db = Chroma(persist_directory=DB_CHROMA_PATH, embedding_function=embedding_function)
-
-
-rag_pipeline = RetrievalQA.from_chain_type(
-    llm=llm, chain_type='stuff',
-    retriever=db.as_retriever(),
-    return_source_documents=True
-)
-
-
-template = """
-You are an AI specialized in the medical domain. 
-Your purpose is to provide accurate, clear, and helpful responses to medical-related inquiries. 
-You must avoid misinformation at all costs. Do not respond to questions outside of the medical domain. 
-If you are unsure or lack information about a query, you must clearly state that you do not know the answer.
-
-Question: {query}
-
-Answer:
-
-"""
-
-prompt_template = PromptTemplate(input_variables=["query"],template=template)
-
-
-
-conversation_buf = ConversationChain(
-    llm=llm,
-    memory=ConversationBufferMemory(), 
-)
-
-
-
-
-@cl.on_chat_start
-async def on_chat_start():
-    pass
-
-
-
-
-@cl.step(type="llm")
-def get_response(query):
-    """
-    Generates a response from the language model based on the user's input. If the input includes
-    '-rag', it uses a retrieval-augmented generation pipeline, otherwise, it directly invokes
-    the language model.
-
-    Args:
-        question (str): The user's input text.
-
-    Returns:
-        str: The language model's response, potentially including source documents if '-rag' was used.
-    """
-    
-   
-    if "-rag" in query.lower():
-        response = rag_pipeline(prompt_template.format(query=query))
-        result = response["result"]
-        source = response["source_documents"]
-        if source:
-            source_details = "\n\nSources:"
-            for source in source:
-                page_content = source.page_content 
-                page_number = source.metadata['page']
-                source_book = source.metadata['source']
-                source_details += f"\n- Page {page_number} from {source_book}: \"{page_content}\""
-        
-            result += source_details
-        return result
-        
-     
-    return llm.invoke(prompt_template.format(query=query))
-
-
-
-@cl.on_message
-async def on_message(message: cl.Message):
-    """
-    Fetches the response from the language model and shows it in the web ui.
-    """
-    try:
-        response = get_response(message.content)
-        msg = cl.Message(content=response)
-    except Exception as e:
-        msg = cl.Message(content=str(e))
-
-    await msg.send()
-
-
-
-
-@cl.on_chat_resume
-async def on_chat_resume(thread: ThreadDict):
-    pass # TODO user history gets fed to LLM 
-
-
-
-@cl.on_chat_end
-def on_chat_end():
-    pass 
-
-
-
-@cl.password_auth_callback
-def auth_callback(username: str, password: str):
-    # Fetch the user matching username from your database
-    # and compare the hashed password with the value stored in the database
-    if (username, password) == ("karcan", "karcan123"):
-        return cl.User(
-            identifier="admin", metadata={"role": "admin", "provider": "credentials"}
-        )
-    else:
-        return None
-
-