delete

streamlit_app.py

@@ -1,153 +0,0 @@
-# app.py
-from typing import List, Union
-from dotenv import load_dotenv, find_dotenv
-from langchain.callbacks import get_openai_callback
-from langchain.chat_models import ChatOpenAI
-from langchain.schema import (SystemMessage, HumanMessage, AIMessage)
-from langchain.llms import LlamaCpp
-from langchain.callbacks.manager import CallbackManager
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-import streamlit as st
-
-
-def init_page() -> None:
-    st.set_page_config(
-        page_title="Personal ChatGPT"
-    )
-    st.header("Personal ChatGPT")
-    st.sidebar.title("Options")
-
-
-def init_messages() -> None:
-    clear_button = st.sidebar.button("Clear Conversation", key="clear")
-    if clear_button or "messages" not in st.session_state:
-        st.session_state.messages = [
-            SystemMessage(
-                content="You are a helpful AI assistant. Reply your answer in mardkown format.")
-        ]
-        st.session_state.costs = []
-
-
-def select_llm() -> Union[ChatOpenAI, LlamaCpp]:
-    model_name = st.sidebar.radio("Choose LLM:",
-                                  ("gpt-3.5-turbo-0613", "gpt-4",
-                                   "llama-2-7b-chat.ggmlv3.q2_K"))
-    temperature = st.sidebar.slider("Temperature:", min_value=0.0,
-                                    max_value=1.0, value=0.0, step=0.01)
-    if model_name.startswith("gpt-"):
-        return ChatOpenAI(temperature=temperature, model_name=model_name)
-    elif model_name.startswith("llama-2-"):
-        callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
-        return LlamaCpp(
-            model_path=f"/app/models/{model_name}.bin",
-            input={"temperature": temperature,
-                   "max_length": 2000,
-                   "top_p": 1
-                   },
-            callback_manager=callback_manager,
-            verbose=False,  # True
-        )
-
-
-def get_answer(llm, messages) -> tuple[str, float]:
-    if isinstance(llm, ChatOpenAI):
-        with get_openai_callback() as cb:
-            answer = llm(messages)
-        return answer.content, cb.total_cost
-    if isinstance(llm, LlamaCpp):
-        return llm(llama_v2_prompt(convert_langchainschema_to_dict(messages))), 0.0
-
-
-def find_role(message: Union[SystemMessage, HumanMessage, AIMessage]) -> str:
-    """
-    Identify role name from langchain.schema object.
-    """
-    if isinstance(message, SystemMessage):
-        return "system"
-    if isinstance(message, HumanMessage):
-        return "user"
-    if isinstance(message, AIMessage):
-        return "assistant"
-    raise TypeError("Unknown message type.")
-
-
-def convert_langchainschema_to_dict(
-        messages: List[Union[SystemMessage, HumanMessage, AIMessage]]) \
-        -> List[dict]:
-    """
-    Convert the chain of chat messages in list of langchain.schema format to
-    list of dictionary format.
-    """
-    return [{"role": find_role(message),
-             "content": message.content
-             } for message in messages]
-
-
-def llama_v2_prompt(messages: List[dict]) -> str:
-    """
-    Convert the messages in list of dictionary format to Llama2 compliant format.
-    """
-    B_INST, E_INST = "[INST]", "[/INST]"
-    B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
-    BOS, EOS = "<s>", "</s>"
-    DEFAULT_SYSTEM_PROMPT = f"""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""
-
-    if messages[0]["role"] != "system":
-        messages = [
-            {
-                "role": "system",
-                "content": DEFAULT_SYSTEM_PROMPT,
-            }
-        ] + messages
-    messages = [
-        {
-            "role": messages[1]["role"],
-            "content": B_SYS + messages[0]["content"] + E_SYS + messages[1]["content"],
-        }
-    ] + messages[2:]
-
-    messages_list = [
-        f"{BOS}{B_INST} {(prompt['content']).strip()} {E_INST} {(answer['content']).strip()} {EOS}"
-        for prompt, answer in zip(messages[::2], messages[1::2])
-    ]
-    messages_list.append(
-        f"{BOS}{B_INST} {(messages[-1]['content']).strip()} {E_INST}")
-
-    return "".join(messages_list)
-
-
-def main() -> None:
-    _ = load_dotenv(find_dotenv())
-
-    init_page()
-    llm = select_llm()
-    init_messages()
-
-    # Supervise user input
-    if user_input := st.chat_input("Input your question!"):
-        st.session_state.messages.append(HumanMessage(content=user_input))
-        with st.spinner("ChatGPT is typing ..."):
-            answer, cost = get_answer(llm, st.session_state.messages)
-        st.session_state.messages.append(AIMessage(content=answer))
-        st.session_state.costs.append(cost)
-
-    # Display chat history
-    messages = st.session_state.get("messages", [])
-    for message in messages:
-        if isinstance(message, AIMessage):
-            with st.chat_message("assistant"):
-                st.markdown(message.content)
-        elif isinstance(message, HumanMessage):
-            with st.chat_message("user"):
-                st.markdown(message.content)
-
-    costs = st.session_state.get("costs", [])
-    st.sidebar.markdown("## Costs")
-    st.sidebar.markdown(f"**Total cost: ${sum(costs):.5f}**")
-    for cost in costs:
-        st.sidebar.markdown(f"- ${cost:.5f}")
-
-
-# streamlit run app.py
-if __name__ == "__main__":
-    main()