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import gradio as gr |
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import paperqa |
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import pickle |
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import pandas as pd |
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from pathlib import Path |
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import requests |
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import zipfile |
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import io |
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import tempfile |
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import os |
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css_style = """ |
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.gradio-container { |
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font-family: "IBM Plex Mono"; |
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} |
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""" |
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def request_pathname(files, data, openai_api_key): |
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if files is None: |
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return [[]] |
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for file in files: |
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if file.name in [x[0] for x in data]: |
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continue |
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data.append([file.name, None, None]) |
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return [[len(data), 0]], data, data, validate_dataset(pd.DataFrame(data), openai_api_key) |
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def validate_dataset(dataset, openapi): |
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docs_ready = dataset.iloc[-1, 0] != "" |
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if docs_ready and type(openapi) is str and len(openapi) > 0: |
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return "✨Ready✨" |
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elif docs_ready: |
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return "⚠️Waiting for key⚠️" |
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elif type(openapi) is str and len(openapi) > 0: |
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return "⚠️Waiting for documents⚠️" |
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else: |
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return "⚠️Waiting for documents and key⚠️" |
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def make_stats(docs): |
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return [[len(docs.doc_previews), sum([x[0] for x in docs.doc_previews])]] |
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def do_ask(question, button, openapi, dataset, length, do_marg, k, max_sources, docs): |
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passages = "" |
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docs_ready = dataset.iloc[-1, 0] != "" |
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if button == "✨Ready✨" and type(openapi) is str and len(openapi) > 0 and docs_ready: |
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os.environ['OPENAI_API_KEY'] = openapi.strip() |
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if docs is None: |
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docs = paperqa.Docs() |
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for _, row in dataset.iterrows(): |
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try: |
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docs.add(row['filepath'], row['citation string'], |
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key=row['key'], disable_check=True) |
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yield "", "", "", docs, make_stats(docs) |
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except Exception as e: |
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pass |
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else: |
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yield "", "", "", docs, [[0, 0]] |
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docs._build_faiss_index() |
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for i, result in enumerate(docs.query_gen(question, |
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length_prompt=f'use {length:d} words', |
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marginal_relevance=do_marg, |
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k=k, max_sources=max_sources)): |
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yield result.formatted_answer, result.context, passages, docs, make_stats(docs) |
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for i, (key, passage) in enumerate(result.passages.items()): |
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passages += f'Disabled for now' |
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yield result.formatted_answer, result.context, passages, docs, make_stats(docs) |
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def download_repo(gh_repo, data, openai_api_key, pbar=gr.Progress()): |
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r = requests.get(f'https://api.github.com/repos/{gh_repo}/zipball') |
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if r.status_code == 200: |
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pbar(1, 'Downloaded') |
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with zipfile.ZipFile(io.BytesIO(r.content)) as z: |
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for i, f in enumerate(z.namelist()): |
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if f.endswith('/'): |
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continue |
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try: |
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text = z.read(f).decode('utf-8') |
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except UnicodeDecodeError: |
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continue |
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if len(text) > 1e5 or len(text) < 10: |
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continue |
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with tempfile.NamedTemporaryFile(delete=False) as tmp: |
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tmp.write(text.encode('utf-8')) |
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tmp.flush() |
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path = tmp.name |
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rel_path = '/'.join(f.split('/')[1:]) |
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key = os.path.basename(f) |
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citation = f'[{rel_path}](https://github.com/{gh_repo}/tree/main/{rel_path})' |
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if path in [x[0] for x in data]: |
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continue |
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data.append([path, citation, key]) |
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yield [[len(data), 0]], data, data, validate_dataset(pd.DataFrame(data), openai_api_key) |
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pbar(int((i+1)/len(z.namelist()) * 99), |
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f'Added {f}') |
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pbar(100, 'Done') |
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else: |
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raise ValueError('Unknown Github Repo') |
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return data |
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with gr.Blocks(css=css_style) as demo: |
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docs = gr.State(None) |
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data = gr.State([]) |
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openai_api_key = gr.State('') |
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gr.Markdown(f""" |
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# Document Question and Answer (v{paperqa.__version__}) |
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*By Andrew White ([@andrewwhite01](https://twitter.com/andrewwhite01))* |
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This tool will enable asking questions of your uploaded text, PDF documents, |
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or scrape github repos. |
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It uses OpenAI's GPT models and thus you must enter your API key below. This |
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tool is under active development and currently uses many tokens - up to 10,000 |
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for a single query. That is $0.10-0.20 per query, so please be careful! |
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* [PaperQA](https://github.com/whitead/paper-qa) is the code used to build this tool. |
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* [langchain](https://github.com/hwchase17/langchain) is the main library this tool utilizes. |
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1. Enter API Key ([What is that?](https://platform.openai.com/account/api-keys)) |
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2. Upload your documents |
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3. Ask a questions |
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""") |
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openai_api_key = gr.Textbox( |
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label="OpenAI API Key", placeholder="sk-...", type="password") |
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with gr.Tab('File Upload'): |
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uploaded_files = gr.File( |
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label="Your Documents Upload (PDF or txt)", file_count="multiple", ) |
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with gr.Tab('Github Repo'): |
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gh_repo = gr.Textbox( |
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label="Github Repo", placeholder="whitead/paper-qa") |
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download = gr.Button("Download Repo") |
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with gr.Accordion("See Docs:", open=False): |
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dataset = gr.Dataframe( |
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headers=["filepath", "citation string", "key"], |
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datatype=["str", "str", "str"], |
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col_count=(3, "fixed"), |
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interactive=False, |
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label="Documents and Citations", |
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overflow_row_behaviour='paginate', |
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max_rows=5 |
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) |
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buildb = gr.Textbox("⚠️Waiting for documents and key...", |
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label="Status", interactive=False, show_label=True, |
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max_lines=1) |
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stats = gr.Dataframe(headers=['Docs', 'Chunks'], |
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datatype=['number', 'number'], |
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col_count=(2, "fixed"), |
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interactive=False, |
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label="Doc Stats") |
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openai_api_key.change(validate_dataset, inputs=[ |
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dataset, openai_api_key], outputs=[buildb]) |
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dataset.change(validate_dataset, inputs=[ |
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dataset, openai_api_key], outputs=[buildb]) |
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uploaded_files.change(request_pathname, inputs=[ |
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uploaded_files, data, openai_api_key], outputs=[stats, data, dataset, buildb]) |
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download.click(fn=download_repo, inputs=[ |
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gh_repo, data, openai_api_key], outputs=[stats, data, dataset, buildb]) |
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query = gr.Textbox( |
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placeholder="Enter your question here...", label="Question") |
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with gr.Row(): |
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length = gr.Slider(25, 200, value=100, step=5, |
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label='Words in answer') |
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marg = gr.Checkbox(True, label='Max marginal relevance') |
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k = gr.Slider(1, 20, value=10, step=1, |
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label='Chunks to examine') |
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sources = gr.Slider(1, 10, value=5, step=1, |
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label='Contexts to include') |
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ask = gr.Button("Ask Question") |
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answer = gr.Markdown(label="Answer") |
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with gr.Accordion("Context", open=True): |
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context = gr.Markdown(label="Context") |
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with gr.Accordion("Raw Text", open=False): |
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passages = gr.Markdown(label="Passages") |
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ask.click(fn=do_ask, inputs=[query, buildb, |
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openai_api_key, dataset, |
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length, marg, k, sources, |
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docs], outputs=[answer, context, passages, docs, stats]) |
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demo.queue(concurrency_count=20) |
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demo.launch(show_error=True) |
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