Update app.py

app.py

@@ -1,90 +1,273 @@
-import os
-import tempfile
+# app.py
+from typing import List, Union, Optional
+
+from dotenv import load_dotenv, find_dotenv
+from langchain.callbacks import get_openai_callback
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.schema import (SystemMessage, HumanMessage, AIMessage)
+from langchain.llms import LlamaCpp
+from langchain.embeddings import LlamaCppEmbeddings
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.text_splitter import TokenTextSplitter
+from langchain.prompts import PromptTemplate
+from langchain.vectorstores import Qdrant
+from PyPDF2 import PdfReader
 import streamlit as st
-from streamlit_chat import message
-from agent import Agent
 
-st.set_page_config(page_title="ChatPDF")
+PROMPT_TEMPLATE = """
+Use the following pieces of context enclosed by triple backquotes to answer the question at the end.
+\n\n
+Context:
+```
+{context}
+```
+\n\n
+Question: [][][][]{question}[][][][]
+\n
+Answer:"""
 
 
-def display_messages():
-    st.subheader("Chat")
-    for i, (msg, is_user) in enumerate(st.session_state["messages"]):
-        message(msg, is_user=is_user, key=str(i))
-    st.session_state["thinking_spinner"] = st.empty()
+def init_page() -> None:
+    st.set_page_config(
+        page_title="Personal ChatGPT"
+    )
+    st.sidebar.title("Options")
 
 
-def process_input():
-    if st.session_state["user_input"] and len(st.session_state["user_input"].strip()) > 0:
-        user_text = st.session_state["user_input"].strip()
-        with st.session_state["thinking_spinner"], st.spinner(f"Thinking"):
-            agent_text = st.session_state["agent"].ask(user_text)
+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 QA assistant. "
+                    "When answering questions, use the context enclosed by triple backquotes if it is relevant. "
+                    "If you don't know the answer, just say that you don't know, "
+                    "don't try to make up an answer. "
+                    "Reply your answer in mardkown format.")
+            )
+        ]
+        st.session_state.costs = []
 
-        st.session_state["messages"].append((user_text, True))
-        st.session_state["messages"].append((agent_text, False))
 
+def get_pdf_text() -> Optional[str]:
+    """
+    Function to load PDF text and split it into chunks.
+    """
+    st.header("Document Upload")
+    uploaded_file = st.file_uploader(
+        label="Here, upload your PDF file you want ChatGPT to use to answer",
+        type="pdf"
+    )
+    if uploaded_file:
+        pdf_reader = PdfReader(uploaded_file)
+        text = "\n\n".join([page.extract_text() for page in pdf_reader.pages])
+        text_splitter = TokenTextSplitter(chunk_size=100, chunk_overlap=0)
+        return text_splitter.split_text(text)
+    else:
+        return None
 
-def read_and_save_file():
-    st.session_state["agent"].forget()  # to reset the knowledge base
-    st.session_state["messages"] = []
-    st.session_state["user_input"] = ""
 
-    for file in st.session_state["file_uploader"]:
-        with tempfile.NamedTemporaryFile(delete=False) as tf:
-            tf.write(file.getbuffer())
-            file_path = tf.name
+def build_vectore_store(
+    texts: str, embeddings: Union[OpenAIEmbeddings, LlamaCppEmbeddings]) \
+        -> Optional[Qdrant]:
+    """
+    Store the embedding vectors of text chunks into vector store (Qdrant).
+    """
+    if texts:
+        with st.spinner("Loading PDF ..."):
+            qdrant = Qdrant.from_texts(
+                texts,
+                embeddings,
+                path=":memory:",
+                collection_name="my_collection",
+                force_recreate=True
+            )
+        st.success("File Loaded Successfully!!")
+    else:
+        qdrant = None
+    return qdrant
 
-        with st.session_state["ingestion_spinner"], st.spinner(f"Ingesting {file.name}"):
-            st.session_state["agent"].ingest(file_path)
-        os.remove(file_path)
 
+def select_llm() -> Union[ChatOpenAI, LlamaCpp]:
+    """
+    Read user selection of parameters in Streamlit sidebar.
+    """
+    model_name = st.sidebar.radio("Choose LLM:",
+                                  ("gpt-3.5-turbo-0613",
+                                   "gpt-3.5-turbo-16k-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)
+    return model_name, temperature
 
-def is_openai_api_key_set() -> bool:
-    return len(st.session_state["OPENAI_API_KEY"]) > 0
 
+def load_llm(model_name: str, temperature: float) -> Union[ChatOpenAI, LlamaCpp]:
+    """
+    Load LLM.
+    """
+    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"./models/{model_name}.bin",
+            input={"temperature": temperature,
+                   "max_length": 2048,
+                   "top_p": 1
+                   },
+            n_ctx=2048,
+            callback_manager=callback_manager,
+            verbose=False,  # True
+        )
+
+
+def load_embeddings(model_name: str) -> Union[OpenAIEmbeddings, LlamaCppEmbeddings]:
+    """
+    Load embedding model.
+    """
+    if model_name.startswith("gpt-"):
+        return OpenAIEmbeddings()
+    elif model_name.startswith("llama-2-"):
+        return LlamaCppEmbeddings(model_path=f"./models/{model_name}.bin")
+
+
+def get_answer(llm, messages) -> tuple[str, float]:
+    """
+    Get the AI answer to user questions.
+    """
+    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 main():
-    if len(st.session_state) == 0:
-        st.session_state["messages"] = []
-        st.session_state["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "")
-        if is_openai_api_key_set():
-            st.session_state["agent"] = Agent(st.session_state["OPENAI_API_KEY"])
-        else:
-            st.session_state["agent"] = None
-
-    st.header("ChatPDF")
-
-    if st.text_input("OpenAI API Key", value=st.session_state["OPENAI_API_KEY"], key="input_OPENAI_API_KEY", type="password"):
-        if (
-            len(st.session_state["input_OPENAI_API_KEY"]) > 0
-            and st.session_state["input_OPENAI_API_KEY"] != st.session_state["OPENAI_API_KEY"]
-        ):
-            st.session_state["OPENAI_API_KEY"] = st.session_state["input_OPENAI_API_KEY"]
-            if st.session_state["agent"] is not None:
-                st.warning("Please, upload the files again.")
-            st.session_state["messages"] = []
-            st.session_state["user_input"] = ""
-            st.session_state["agent"] = Agent(st.session_state["OPENAI_API_KEY"])
-
-    st.subheader("Upload a document")
-    st.file_uploader(
-        "Upload document",
-        type=["pdf"],
-        key="file_uploader",
-        on_change=read_and_save_file,
-        label_visibility="collapsed",
-        accept_multiple_files=True,
-        disabled=not is_openai_api_key_set(),
-    )
 
-    st.session_state["ingestion_spinner"] = st.empty()
+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 extract_userquesion_part_only(content):
+    """
+    Function to extract only the user question part from the entire question
+    content combining user question and pdf context.
+    """
+    content_split = content.split("[][][][]")
+    if len(content_split) == 3:
+        return content_split[1]
+    return content
+
+
+def main() -> None:
+    _ = load_dotenv(find_dotenv())
+
+    init_page()
+
+    model_name, temperature = select_llm()
+    llm = load_llm(model_name, temperature)
+    embeddings = load_embeddings(model_name)
+
+    texts = get_pdf_text()
+    qdrant = build_vectore_store(texts, embeddings)
+
+    init_messages()
+
+    st.header("Personal ChatGPT")
+    # Supervise user input
+    if user_input := st.chat_input("Input your question!"):
+        if qdrant:
+            context = [c.page_content for c in qdrant.similarity_search(
+                user_input, k=10)]
+            user_input_w_context = PromptTemplate(
+                template=PROMPT_TEMPLATE,
+                input_variables=["context", "question"]) \
+                .format(
+                    context=context, question=user_input)
+        else:
+            user_input_w_context = user_input
+        st.session_state.messages.append(
+            HumanMessage(content=user_input_w_context))
+        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_messages()
-    st.text_input("Message", key="user_input", disabled=not is_openai_api_key_set(), on_change=process_input)
+    # 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(extract_userquesion_part_only(message.content))
 
-    st.divider()
-    st.markdown("Source code: [Github](https://github.com/viniciusarruda/chatpdf)")
+    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()