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import gradio as gr
docs = None
def request_pathname(files):
return [[file.name, file.name.split('/')[-1]] for file in files]
def validate_dataset(dataset, openapi):
docs_ready = dataset.iloc[-1, 0] != ""
if docs_ready and type(openapi) is str and len(openapi) > 0:
return "✨Ready✨"
elif docs_ready:
return "Waiting for key..."
elif type(openapi) is str and len(openapi) > 0:
return "Waiting for documents..."
else:
return "Waiting for documents and key..."
def do_ask(question, button, openapi, dataset, progress=gr.Progress()):
global docs
docs_ready = dataset.iloc[-1, 0] != ""
if button == "✨Ready✨" and openapi != "" and docs_ready:
import os
os.environ['OPENAI_API_KEY'] = openapi.strip()
import paperqa
docs = paperqa.Docs()
# dataset is pandas dataframe
for _, row in dataset.iterrows():
key = None
if ',' not in row['citation string']:
key = row['citation string']
docs.add(row['filepath'], row['citation string'], key=key)
else:
return ""
if docs is None:
return """**Error**: You must build the index first!"""
progress(0, "Building Index...")
docs._build_faiss_index()
progress(0.25, "Querying...")
result = docs.query(question)
progress(1.0, "Done!")
return result.formatted_answer, result.context
with gr.Blocks() as demo:
gr.Markdown("""
# Document Question and Answer
This tool will enable asking questions of your uploaded text or PDF documents.
It uses OpenAI's GPT models and thus you must enter your API key below. This
tool is under active development and currently uses many tokens - up to 10,000
for a single query. That is $0.10-0.20 per query, so please be careful!
* [PaperQA](https://github.com/whitead/paper-qa) is the code used to build this tool.
* [langchain](https://github.com/hwchase17/langchain) is the main library this tool utilizes.
## Instructions
1. Enter API Key
2. Upload your documents and modify citation strings if you want (to look prettier)
""")
openai_api_key = gr.Textbox(
label="OpenAI API Key", placeholder="sk-...", type="password")
uploaded_files = gr.File(
label="Your Documents Upload (PDF or txt)", file_count="multiple")
dataset = gr.Dataframe(
headers=["filepath", "citation string"],
datatype=["str", "str"],
col_count=(2, "fixed"),
interactive=True,
label="Documents and Citations"
)
buildb = gr.Textbox("Waiting for documents and key...",
label="Status", interactive=False, show_label=True)
openai_api_key.change(validate_dataset, inputs=[
dataset, openai_api_key], outputs=[buildb])
dataset.change(validate_dataset, inputs=[
dataset, openai_api_key], outputs=[buildb])
uploaded_files.change(request_pathname, inputs=[
uploaded_files], outputs=[dataset])
query = gr.Textbox(
placeholder="Enter your question here...", label="Question")
ask = gr.Button("Ask Question")
gr.Markdown("## Answer")
answer = gr.Markdown(label="Answer")
with gr.Accordion("Context", open=False):
gr.Markdown(
"### Context\n\nThe following context was used to generate the answer:")
context = gr.Markdown(label="Context")
ask.click(fn=do_ask, inputs=[query, buildb,
openai_api_key, dataset], outputs=[answer, context])
demo.queue(concurrency_count=3)
demo.launch()
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