Update app.py

app.py

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+#####################################
+##  BitsAndBytes
+#####################################
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+ 
+model_name = "bn22/Mistral-7B-Instruct-v0.1-sharded"
+
+###### other models:
+# "Trelis/Llama-2-7b-chat-hf-sharded-bf16"
+# "bn22/Mistral-7B-Instruct-v0.1-sharded"
+# "HuggingFaceH4/zephyr-7b-beta"
+
+# function for loading 4-bit quantized model
+def load_quantized_model(model_name: str):
+    """
+    :param model_name: Name or path of the model to be loaded.
+    :return: Loaded quantized model.
+    """
+    bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_use_double_quant=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.bfloat16
+    )
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        load_in_4bit=True,
+        torch_dtype=torch.bfloat16,
+        quantization_config=bnb_config
+    )
+    return model
+
+##################################################
+## vs chat
+##################################################
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
+
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_community.document_loaders import WebBaseLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+
+#from langchain_openai import OpenAIEmbeddings, ChatOpenAI
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.vectorstores.faiss import FAISS
+
+
+from dotenv import load_dotenv
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.chains import create_history_aware_retriever, create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+
+
+load_dotenv()
+
+def get_vectorstore_from_url(url):
+    # get the text in document form
+    loader = WebBaseLoader(url)
+    document = loader.load()
+    
+    # split the document into chunks
+    text_splitter = RecursiveCharacterTextSplitter()
+    document_chunks = text_splitter.split_documents(document)
+    ####### 
+    ''' 
+        FAISS
+        A FAISS vector store containing the embeddings of the text chunks.
+   '''
+    model = "BAAI/bge-base-en-v1.5"
+    encode_kwargs = {
+        "normalize_embeddings": True
+    }  # set True to compute cosine similarity
+    embeddings = HuggingFaceBgeEmbeddings(
+        model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
+    )
+    # load from disk
+    vector_store = Chroma(persist_directory="./chroma_db", embedding_function=embeddings)
+ 
+    #vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    vector_store = Chroma.from_documents(document_chunks, embeddings, persist_directory="./chroma_db")
+ 
+
+
+
+    print("-----")
+    print(vector_store.similarity_search("What is ALiBi?"))
+    print("-----") 
+
+    #######
+    # create a vectorstore from the chunks
+
+    return vector_store
+
+
+
+
+
+def get_context_retriever_chain(vector_store):
+
+    # specify model huggingface mode name
+    model_name = "anakin87/zephyr-7b-alpha-sharded"
+   # model_name = "bn22/Mistral-7B-Instruct-v0.1-sharded"
+
+    ###### other models:
+    # "Trelis/Llama-2-7b-chat-hf-sharded-bf16"
+    # "bn22/Mistral-7B-Instruct-v0.1-sharded"
+    # "HuggingFaceH4/zephyr-7b-beta"
+
+    # function for loading 4-bit quantized model
+     
+
+    llm = load_quantized_model(model_name)
+    
+    retriever = vector_store.as_retriever()
+    
+    prompt = ChatPromptTemplate.from_messages([
+      MessagesPlaceholder(variable_name="chat_history"),
+      ("user", "{input}"),
+      ("user", "Given the above conversation, generate a search query to look up in order to get information relevant to the conversation")
+    ])
+    
+    retriever_chain = create_history_aware_retriever(llm, retriever, prompt)
+    
+    return retriever_chain
+    
+def get_conversational_rag_chain(retriever_chain): 
+    
+    llm = load_quantized_model(model_name)
+    
+    prompt = ChatPromptTemplate.from_messages([
+      ("system", "Answer the user's questions based on the below context:\n\n{context}"),
+      MessagesPlaceholder(variable_name="chat_history"),
+      ("user", "{input}"),
+    ])
+    
+    stuff_documents_chain = create_stuff_documents_chain(llm,prompt)
+    
+    return create_retrieval_chain(retriever_chain, stuff_documents_chain)
+
+def get_response(user_input):
+    retriever_chain = get_context_retriever_chain(st.session_state.vector_store)
+    conversation_rag_chain = get_conversational_rag_chain(retriever_chain)
+    
+    response = conversation_rag_chain.invoke({
+        "chat_history": st.session_state.chat_history,
+        "input": user_query
+    })
+    
+    return response['answer']
+
+
+
+###################
+
+###################
+import gradio as gr
+
+##from langchain_core.runnables.base import ChatPromptValue
+#from torch import tensor
+
+# Create Gradio interface
+#vector_store = None  # Set your vector store here
+chat_history = []     # Set your chat history here
+
+# Define your function here
+def get_response(user_input):
+
+      # Define the prompt as a ChatPromptValue object
+    #user_input = ChatPromptValue(user_input)
+    
+    # Convert the prompt to a tensor
+    #input_ids = user_input.tensor
+    
+
+    #vs = get_vectorstore_from_url(user_url, all_domain)
+    vs = get_vectorstore_from_url("https://www.bofrost.de/shop/laenderkueche_5573/italienische-kueche_5576/linguine-mit-feinen-pilzen.html?position=1&clicked=")
+    print("------ here 22 " )
+    chat_history =[]
+    retriever_chain = get_context_retriever_chain(vs)
+    conversation_rag_chain = get_conversational_rag_chain(retriever_chain)
+    
+    response = conversation_rag_chain.invoke({
+        "chat_history": chat_history,
+        "input": user_input
+    })
+    
+    return response['answer']
+
+def simple(text:str):
+  return text +" hhhmmm "
+
+app = gr.Interface(
+    fn=get_response,
+    #fn=simple,
+    inputs=["text"],
+    outputs="text",
+    title="Chat with Websites",
+    description="Type your message and chat with websites.",
+    #allow_flagging=False
+)
+
+app.launch(debug=True, share=True)#wie registriere ich mich bei bofrost? Was kosten Linguine