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import gradio as gr
import os
import re
from pathlib import Path
from langchain_community.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFaceEndpoint
from pathlib import Path
import chromadb
from unidecode import unidecode
# List of allowed models
allowed_llms = [
"mistralai/Mistral-7B-Instruct-v0.2",
"mistralai/Mixtral-8x7B-Instruct-v0.1",
"mistralai/Mistral-7B-Instruct-v0.1",
"google/gemma-7b-it",
"google/gemma-2b-it",
"HuggingFaceH4/zephyr-7b-beta",
"HuggingFaceH4/zephyr-7b-gemma-v0.1",
"meta-llama/Llama-2-7b-chat-hf"
]
list_llm_simple = [os.path.basename(llm) for llm in allowed_llms]
# Load PDF document and create doc splits
def load_doc(list_file_path, chunk_size, chunk_overlap):
loaders = [PyPDFLoader(x) for x in list_file_path]
pages = []
for loader in loaders:
pages.extend(loader.load())
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap
)
doc_splits = text_splitter.split_documents(pages)
return doc_splits
# Create vector database
def create_db(splits, collection_name):
embedding = HuggingFaceEmbeddings()
embedding=embedding,
client=new_client,
collection_name=collection_name,
)
return vectordb
# Initialize langchain LLM chain
def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
llm = HuggingFaceEndpoint(
repo_id=llm_model,
temperature=temperature,
max_new_tokens=max_tokens,
top_k=top_k,
load_in_8bit=True,
)
memory = ConversationBufferMemory(
memory_key="chat_history",
output_key='answer',
return_messages=True
)
retriever = vector_db.as_retriever()
qa_chain = ConversationalRetrievalChain.from_llm(
llm,
retriever=retriever,
chain_type="stuff",
memory=memory,
return_source_documents=True,
verbose=False,
)
return qa_chain
# Generate collection name for vector database
def create_collection_name(filepath):
collection_name = Path(filepath).stem
collection_name = unidecode(collection_name).replace(" ", "-")
collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)[:50]
if len(collection_name) < 3:
collection_name = collection_name + 'xyz'
if not collection_name[0].isalnum():
collection_name = 'A' + collection_name[1:]
if not collection_name[-1].isalnum():
collection_name = collection_name[:-1] + 'Z'
return collection_name
# Initialize database
def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
list_file_path = [x.name for x in list_file_obj if x is not None]
collection_name = create_collection_name(list_file_path[0])
doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
vector_db = create_db(doc_splits, collection_name)
return vector_db, collection_name, "Complete!"
# Initialize LLM
def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
llm_name = allowed_llms[llm_option]
qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
return qa_chain, "Complete!"
# Format chat history
def format_chat_history(message, chat_history):
formatted_chat_history = []
for user_message, bot_message in chat_history:
formatted_chat_history.append(f"User: {user_message}")
formatted_chat_history.append(f"Assistant: {bot_message}")
return formatted_chat_history
# Conversation handling
def conversation(qa_chain, message, history):
formatted_chat_history = format_chat_history(message, history)
response = qa_chain({"question": message, "chat_history": formatted_chat_history})
response_answer = response["answer"].split("Helpful Answer:")[-1]
response_sources = response["source_documents"]
new_history = history + [(message, response_answer)]
response_details = [(src.page_content.strip(), src.metadata["page"] + 1) for src in response_sources[:3]]
return qa_chain, gr.update(value=""), new_history, *sum(response_details, ())
# Gradio Interface
def demo():
with gr.Blocks(theme="default") as demo:
vector_db = gr.State()
qa_chain = gr.State()
collection_name = gr.State()
gr.Markdown(
"""<center><h2>PDF-based Chatbot</h2></center>
<h3>Ask any questions about your PDF documents</h3>""")
with gr.Tab("Upload PDF"):
document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF Documents")
with gr.Tab("Process Document"):
db_btn = gr.Radio(["ChromaDB"], label="Vector Database", value="ChromaDB", type="index")
with gr.Accordion("Advanced Options", open=False):
slider_chunk_size = gr.Slider(100, 1000, 600, 20, label="Chunk Size", interactive=True)
slider_chunk_overlap = gr.Slider(10, 200, 40, 10, label="Chunk Overlap", interactive=True)
db_progress = gr.Textbox(label="Database Initialization Status", value="None")
db_btn = gr.Button("Generate Database")
with gr.Tab("Initialize QA Chain"):
llm_btn = gr.Radio(list_llm_simple, label="LLM Models", value=list_llm_simple[0], type="index")
with gr.Accordion("Advanced Options", open=False):
slider_temperature = gr.Slider(0.01, 1.0, 0.7, 0.1, label="Temperature", interactive=True)
slider_maxtokens = gr.Slider(224, 4096, 1024, 32, label="Max Tokens", interactive=True)
slider_topk = gr.Slider(1, 10, 3, 1, label="Top-k Samples", interactive=True)
llm_progress = gr.Textbox(value="None", label="QA Chain Initialization Status")
qachain_btn = gr.Button("Initialize QA Chain")
with gr.Tab("Chatbot"):
chatbot = gr.Chatbot(height=300)
with gr.Accordion("Document References", open=False):
for i in range(1, 4):
gr.Row([gr.Textbox(label=f"Reference {i}", lines=2, container=True, scale=20), gr.Number(label="Page", scale=1)])
msg = gr.Textbox(placeholder="Type message here...", container=True)
gr.Row([gr.Button("Submit"), gr.Button("Clear Conversation")])
# Define Interactions
db_btn.click(initialize_database, inputs=[document, slider_chunk_size, slider_chunk_overlap], outputs=[vector_db, collection_name, db_progress])
qachain_btn.click(initialize_LLM, inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], outputs=[qa_chain, llm_progress])
msg.submit(conversation, inputs=[qa_chain, msg, chatbot], outputs=[qa_chain, msg, chatbot] + [None] * 6)
demo.launch(debug=True)
if __name__ == "__main__":
demo() |