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https://github.com/deepset-ai/haystack.git
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d81897535e
* Queries now run only when pressing RUN. File upload hidden. Question is not sent if the textbox is empty. * Add latest docstring and tutorial changes * Tidy up: remove needless state, add comments, fix minor bugs * Had to add results to the status to avoid some bugs in eval mode * Added 'credits' * Add footers, update requirements, some random questions for the evaluation * Add requested changes * Temporary rollback the UI to the old GoT dataset Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
212 lines
8.7 KiB
Python
212 lines
8.7 KiB
Python
import os
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import sys
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import logging
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import pandas as pd
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from pathlib import Path
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import streamlit as st
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from annotated_text import annotated_text
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# streamlit does not support any states out of the box. On every button click, streamlit reload the whole page
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# and every value gets lost. To keep track of our feedback state we use the official streamlit gist mentioned
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# here https://gist.github.com/tvst/036da038ab3e999a64497f42de966a92
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import SessionState
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from utils import HS_VERSION, feedback_doc, haystack_is_ready, retrieve_doc, upload_doc, haystack_version
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# Adjust to a question that you would like users to see in the search bar when they load the UI:
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DEFAULT_QUESTION_AT_STARTUP = "Who's the father of Arya Stark?"
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# Labels for the evaluation
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EVAL_LABELS = os.getenv("EVAL_FILE", Path(__file__).parent / "eval_labels_example.csv")
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# Whether the file upload should be enabled or not
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DISABLE_FILE_UPLOAD = os.getenv("HAYSTACK_UI_DISABLE_FILE_UPLOAD")
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# Retrieve Haystack version from the REST API
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HS_VERSION = haystack_version()
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def main():
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# Persistent state
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state = SessionState.get(
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random_question=DEFAULT_QUESTION_AT_STARTUP,
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random_answer="",
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results=None,
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raw_json=None,
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get_next_question=True
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)
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# Small callback to reset the interface in case the text of the question changes
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def reset_results(*args):
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state.results = None
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state.raw_json = None
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# Title
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st.write("# Haystack Demo")
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# Sidebar
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st.sidebar.header("Options")
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top_k_reader = st.sidebar.slider("Max. number of answers", min_value=1, max_value=10, value=3, step=1)
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top_k_retriever = st.sidebar.slider("Max. number of documents from retriever", min_value=1, max_value=10, value=3, step=1)
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eval_mode = st.sidebar.checkbox("Evaluation mode")
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debug = st.sidebar.checkbox("Show debug info")
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# File upload block
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if not DISABLE_FILE_UPLOAD:
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st.sidebar.write("## File Upload:")
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data_files = st.sidebar.file_uploader("", type=["pdf", "txt", "docx"], accept_multiple_files=True)
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for data_file in data_files:
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# Upload file
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if data_file:
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raw_json = upload_doc(data_file)
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st.sidebar.write(str(data_file.name) + " ✅ ")
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if debug:
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st.subheader("REST API JSON response")
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st.sidebar.write(raw_json)
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st.sidebar.markdown(f"""
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<style>
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a {{
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text-decoration: none;
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}}
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.haystack-footer {{
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text-align: center;
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}}
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.haystack-footer h4 {{
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margin: 0.1rem;
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padding:0;
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}}
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footer {{
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opacity: 0;
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}}
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</style>
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<div class="haystack-footer">
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<hr />
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<h4>Built with <a href="https://www.deepset.ai/haystack">Haystack</a> <small>(v{HS_VERSION})</small></h4>
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<p>Get it on <a href="https://github.com/deepset-ai/haystack/">GitHub</a> - Read the <a href="https://haystack.deepset.ai/overview/intro">Docs</a></p>
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<small>Data crawled from <a href="https://en.wikipedia.org/wiki/Category:Lists_of_countries_by_continent">Wikipedia</a> in November 2021.<br />See the <a href="https://creativecommons.org/licenses/by-sa/3.0/">License</a> (CC BY-SA 3.0).</small>
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</div>
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""", unsafe_allow_html=True)
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# Load csv into pandas dataframe
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if eval_mode:
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try:
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df = pd.read_csv(EVAL_LABELS, sep=";")
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except Exception:
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st.error(f"The eval file was not found. Please check the demo's [README](https://github.com/deepset-ai/haystack/tree/master/ui/README.md) for more information.")
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sys.exit(f"The eval file was not found under `{EVAL_LABELS}`. Please check the README (https://github.com/deepset-ai/haystack/tree/master/ui/README.md) for more information.")
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# Get next random question from the CSV
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state.get_next_question = st.button("Load new question")
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if state.get_next_question:
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reset_results()
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new_row = df.sample(1)
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while new_row["Question Text"].values[0] == state.random_question: # Avoid picking the same question twice (the change is not visible on the UI)
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new_row = df.sample(1)
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state.random_question = new_row["Question Text"].values[0]
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state.random_answer = new_row["Answer"].values[0]
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# Search bar
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question = st.text_input(
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"Please provide your query:",
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value=state.random_question,
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max_chars=100,
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on_change=reset_results
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)
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run_query = st.button("Run")
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# Check the connection
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with st.spinner("⌛️ Haystack is starting..."):
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if not haystack_is_ready():
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st.error("🚫 Connection Error. Is Haystack running?")
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run_query = False
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reset_results()
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# Get results for query
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if run_query and question:
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reset_results()
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with st.spinner(
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"🧠 Performing neural search on documents... \n "
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"Do you want to optimize speed or accuracy? \n"
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"Check out the docs: https://haystack.deepset.ai/usage/optimization "
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):
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try:
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state.results, state.raw_json = retrieve_doc(question, top_k_reader=top_k_reader, top_k_retriever=top_k_retriever)
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except Exception as e:
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logging.exception(e)
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if "The server is busy processing requests" in str(e):
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st.error("🧑🌾 All our workers are busy! Try again later.")
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else:
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st.error("🐞 An error occurred during the request. Check the logs in the console to know more.")
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return
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if state.results:
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# Show the gold answer if we use a question of the given set
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if question == state.random_question and eval_mode:
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st.write("## Correct answers:")
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st.write(state.random_answer)
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st.write("## Results:")
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count = 0 # Make every button key unique
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for result in state.results:
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if result["answer"]:
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answer, context = result["answer"], result["context"]
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start_idx = context.find(answer)
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end_idx = start_idx + len(answer)
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annotated_text(context[:start_idx], (answer, "ANSWER", "#8ef"), context[end_idx:])
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else:
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st.markdown(result["context"])
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st.write("**Relevance:** ", result["relevance"], "**Source:** ", result["source"])
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if eval_mode:
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# Define columns for buttons
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button_col1, button_col2, button_col3, _ = st.columns([1, 1, 1, 6])
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if button_col1.button("👍", key=f"{result['context']}{count}1", help="Correct answer"):
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feedback_doc(
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question=question,
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is_correct_answer="true",
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document_id=result["document_id"],
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model_id=1,
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is_correct_document="true",
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answer=result["answer"],
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offset_start_in_doc=result["offset_start_in_doc"]
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)
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st.success("✨ Thanks for your feedback! ✨")
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if button_col2.button("👎", key=f"{result['context']}{count}2", help="Wrong answer and wrong passage"):
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feedback_doc(
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question=question,
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is_correct_answer="false",
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document_id=result["document_id"],
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model_id=1,
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is_correct_document="false",
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answer=result["answer"],
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offset_start_in_doc=result["offset_start_in_doc"]
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)
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st.success("✨ Thanks for your feedback! ✨")
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if button_col3.button("👎👍", key=f"{result['context']}{count}3", help="Wrong answer, but correct passage"):
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feedback_doc(
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question=question,
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is_correct_answer="false",
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document_id=result["document_id"],
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model_id=1,
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is_correct_document="true",
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answer=result["answer"],
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offset_start_in_doc=result["offset_start_in_doc"]
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)
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st.success("✨ Thanks for your feedback! ✨")
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count += 1
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st.write("___")
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if debug:
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st.subheader("REST API JSON response")
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st.write(state.raw_json)
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main()
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