Spaces:
Sleeping
Sleeping
def demo(): | |
with gr.Blocks(theme="base") as demo: | |
vector_db = gr.State() | |
qa_chain = gr.State() | |
collection_name = gr.State() | |
gr.Markdown( | |
"""<center><h2>PDF-based chatbot (powered by LangChain and open-source LLMs)</center></h2> | |
<h3>Ask any questions about your PDF documents, along with follow-ups</h3> | |
<b>Note:</b> This AI assistant performs retrieval-augmented generation from your PDF documents. \ | |
When generating answers, it takes past questions into account (via conversational memory), and includes document references for clarity purposes.</i> | |
<br><b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate an output.<br> | |
""") | |
with gr.Tab("Step 1 - Document pre-processing"): | |
with gr.Row(): | |
document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)") | |
# upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1) | |
with gr.Row(): | |
db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value = "ChromaDB", type="index", info="Choose your vector database") | |
with gr.Accordion("Advanced options - Document text splitter", open=False): | |
with gr.Row(): | |
slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True) | |
with gr.Row(): | |
slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True) | |
with gr.Row(): | |
db_progress = gr.Textbox(label="Vector database initialization", value="None") | |
with gr.Row(): | |
db_btn = gr.Button("Generate vector database...") | |
with gr.Tab("Step 2 - QA chain initialization"): | |
with gr.Row(): | |
llm_btn = gr.Radio(list_llm_simple, \ | |
label="LLM models", value = list_llm_simple[0], type="index", info="Choose your LLM model") | |
with gr.Accordion("Advanced options - LLM model", open=False): | |
with gr.Row(): | |
slider_temperature = gr.Slider(minimum = 0.0, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True) | |
with gr.Row(): | |
slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True) | |
with gr.Row(): | |
slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True) | |
with gr.Row(): | |
llm_progress = gr.Textbox(value="None",label="QA chain initialization") | |
with gr.Row(): | |
qachain_btn = gr.Button("Initialize question-answering chain...") | |
with gr.Tab("Step 3 - Conversation with chatbot"): | |
chatbot = gr.Chatbot(height=300) | |
with gr.Accordion("Advanced - Document references", open=False): | |
with gr.Row(): | |
doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20) | |
source1_page = gr.Number(label="Page", scale=1) | |
with gr.Row(): | |
doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20) | |
source2_page = gr.Number(label="Page", scale=1) | |
with gr.Row(): | |
doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20) | |
source3_page = gr.Number(label="Page", scale=1) | |
with gr.Row(): | |
msg = gr.Textbox(placeholder="Type message", container=True) | |
with gr.Row(): | |
submit_btn = gr.Button("Submit") | |
clear_btn = gr.ClearButton([msg, chatbot]) | |
# Preprocessing events | |
#upload_btn.upload(upload_file, inputs=[upload_btn], outputs=[document]) | |
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]).then(lambda:[None,"",0,"",0,"",0], \ | |
inputs=None, \ | |
outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \ | |
queue=False) | |
# Chatbot events | |
msg.submit(conversation, \ | |
inputs=[qa_chain, msg, chatbot], \ | |
outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \ | |
queue=False) | |
submit_btn.click(conversation, \ | |
inputs=[qa_chain, msg, chatbot], \ | |
outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \ | |
queue=False) | |
clear_btn.click(lambda:[None,"",0,"",0,"",0], \ | |
inputs=None, \ | |
outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \ | |
queue=False) | |
demo.queue().launch(debug=True) | |
if __name__ == "__main__": | |
demo() |