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import streamlit as st
import openai
import os
import re
import ast
import pandas as pd
st.title(":rocket: Named Entity Recognition (NER) with GPT-3")
# st.header("Guidelines")
st.markdown(
"You can edit the guidelines here. Press `Delete` to remove a row after selecting it."
)
df = pd.DataFrame(
[
{
"entity": "PERSON",
"definition": "Short name or full name of a person from any geographic regions.",
"color": "red",
},
{
"entity": "DATE",
"definition": "Any format of dates. Dates can also be in natural language.",
"color": "green",
},
{
"entity": "LOC",
"definition": "Name of any geographic location, like cities, countries, continents, districts etc.",
"color": "blue",
},
]
)
edited_df = st.experimental_data_editor(df, num_rows="dynamic")
examples = [
{
"sentence": "Mr. Jacob lives in Madrid since 12th January 2015.",
"output": "{{'PERSON': ['Mr. Jacob'], 'DATE': ['12th January 2015'], 'LOC': ['Madrid']}}",
},
{
"sentence": "Mr. Rajeev Mishra and Sunita Roy are friends and they meet each other on 24/03/1998.",
"output": "{{'PERSON': ['Mr. Rajeev Mishra', 'Sunita Roy'], 'DATE': ['24/03/1998'], 'LOC': ['None']}}",
},
]
def generate_guidelines_prompt(guidelines):
guidelines_prompt = "Entity Definition:\n"
for guideline in guidelines.values():
guidelines_prompt += f"{guideline['entity']}: {guideline['definition']}\n"
guidelines_prompt += "\nOutput Format:\n"
guidelines_prompt += "{{'PERSON': [list of entities present], 'DATE': [list of entities present], 'LOC': [list of entities present]}}\n"
guidelines_prompt += "If no entities are presented in any categories keep it None\n"
guidelines_prompt += "\nExamples:\n\n"
for i, example in enumerate(examples):
guidelines_prompt += f"{i+1}. Sentence: {example['sentence']}\n"
guidelines_prompt += f"Output: {example['output']}\n\n"
guidelines_prompt += str(len(examples) + 1) + ". Sentence: {}\n"
guidelines_prompt += "Output: "
return guidelines_prompt
openai.api_key = os.getenv("OPENAI_API_KEY")
SYSTEM_PROMPT = "You are a smart and intelligent Named Entity Recognition (NER) system. I will provide you the definition of the entities you need to extract, the sentence from where your extract the entities and the output format with examples."
USER_PROMPT_1 = "Are you clear about your role?"
ASSISTANT_PROMPT_1 = "Sure, I'm ready to help you with your NER task. Please provide me with the necessary information to get started."
def openai_chat_completion_response(final_prompt):
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": USER_PROMPT_1},
{"role": "assistant", "content": ASSISTANT_PROMPT_1},
{"role": "user", "content": final_prompt},
],
)
return response["choices"][0]["message"]["content"].strip(" \n")
my_sentence = st.text_input("Your Sentence")
if st.button("Submit"):
colors = {}
for guideline in edited_df.to_dict(orient="index").values():
colors[guideline["entity"]] = guideline["color"]
GUIDELINES_PROMPT = generate_guidelines_prompt(edited_df.to_dict(orient="index"))
GUIDELINES_PROMPT = GUIDELINES_PROMPT.format(my_sentence)
ners = openai_chat_completion_response(GUIDELINES_PROMPT)
ners_dictionary = ast.literal_eval(ners)
st.json(ners_dictionary)
for entity_type, entity_list in ners_dictionary.items():
entity_list = list(set(entity_list))
color = colors[entity_type]
for ent in entity_list:
if ent != "None":
my_sentence = re.sub(
ent,
":" + color + "[" + ent + "\[" + entity_type + "\]" + "]",
my_sentence,
)
st.markdown(my_sentence)
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