create app.py file

app.py

@@ -0,0 +1,321 @@
+
+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, CTransformers
+from langchain.embeddings import LlamaCppEmbeddings, HuggingFaceEmbeddings
+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
+# import llamapy
+# import langchain.llms.
+
+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 init_page() -> None:
+    st.set_page_config(
+        page_title="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 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 = []
+
+
+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
+
+
+# texts: str, embeddings: Union[OpenAIEmbeddings, HuggingFaceEmbeddings]) \
+
+
+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
+
+
+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)
+    print('Returing:--->', model_name)
+    return model_name, temperature
+
+
+# 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"C:Users/SravanthK/Downloads/{model_name}.bin",
+#             input={"temperature": temperature,
+#                    "max_length": 2048,
+#                    "top_p": 1
+#                    },
+#             n_ctx=2048,
+#             callback_manager=callback_manager,
+#             verbose=False,  # True
+#         )
+
+def load_llm(model_name: str, temperature: float):
+    """
+    Load LLM.
+    """
+
+    if model_name.startswith("gpt-"):
+        return ChatOpenAI(temperature=temperature, model_name=model_name)
+    elif model_name.startswith("llama-2-"):
+        print('At else---->', model_name)
+        callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+        return LlamaCpp(
+            model_path=r"C:\Users\SravanthK\Desktop\ISK\ggl_project\models\llama-2-7b-chat.ggmlv3.q2_K.bin",
+            input={"temperature": temperature,
+                   "max_length": 2048,
+                   "top_p": 1
+                   },
+            n_ctx=2048,
+            callback_manager=callback_manager,
+            verbose=False,  # True
+        )
+    #     return CTransformers(
+    #     model=r"C:\Users\SravanthK\Downloads\llama-2-7b-chat.ggmlv3.q2_K.bin",
+    #     model_type="llama",
+    #     max_new_tokens=256,
+    #     temperature=0.5,
+    #     context_length=512,
+    #     verbose=False,# Set to the model's maximum context length
+    #     callback_manager=callback_manager
+    # )
+
+
+
+# def load_embeddings(model_name: str) -> Union[OpenAIEmbeddings, HuggingFaceEmbeddings]:
+
+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")
+        return LlamaCppEmbeddings(model_path=r'C:\Users\SravanthK\Downloads\llama-2-7b-chat.ggmlv3.q2_K.bin')
+        # print(f'---> Selected model: {model_name}')
+        # # HuggingFaceEmbeddings(model_path=f"C:/Users/SravanthK/Downloads/{model_name}.bin")
+        # print('YES')
+        # return HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
+    
+
+
+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, CTransformers):
+    #     return llm(llama_v2_prompt(convert_langchainschema_to_dict(messages))), 0.0
+    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 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 ..."):
+            print(type(llm), type(st.session_state.messages))
+            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(extract_userquesion_part_only(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()