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import os
import pandas as pd
from openai import OpenAI
import streamlit as st
from dotenv import load_dotenv
import re
load_dotenv() # take environment variables from .env.
api_key = os.getenv('OPENAI_API_KEY')
#print(api_key)
# Create a filtered dataframe with the most 20 ballanced intents
def get_ballanced_intents(data):
intents = data['intent'].value_counts()
intents = intents[intents <= 40].index.tolist()
filtered_data = data[data['intent'].isin(intents)]
print(f"Filtered data shape: {filtered_data.shape}")
return filtered_data
# Create a prompt for the OpenAI Chat model
def create_prompt(user_text, utterances):
prompt = f"User text: {user_text}\n\nUtterance examples:\n"
for i, utterance in enumerate(utterances):
prompt += f"{i + 1}. {utterance}\n"
prompt += "\nPlease rate the similarity of the user text to each of the utterance examples on a scale from 0 to 1."
return prompt
# Get the similarity scores for the user text and the utterance examples
def get_similarity_scores(prompt, temperature):
load_dotenv() # take environment variables from .env.
api_key = os.getenv('OPENAI_API_KEY')
#print(api_key)
if not api_key:
st.write("Please set the OPENAI_API_KEY environment variable.")
return
#client = OpenAI(api_key=api_key)
client = OpenAI(api_key='sk-proj-SNnm3Z9t6BvJgQ3ztjWLT3BlbkFJ1037ZT68ltN47zcir44l')
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "You are a helpful assistant. Respond without details in ()"},
{"role": "user", "content": prompt}
],
max_tokens=150,
n=1,
stop='8.',
temperature=temperature,
)
similarities = response.choices[0].message.content.strip().split('\n')
#print(similarities)
for sim in similarities:
#print(sim)
print(sim.split('- ')[-1])
similarity_scores = [sim.split('- ')[-1] for sim in similarities]
similarity_scores = [sim if re.match(r'^0\.[1-9]$|^1\.0$', sim) else '0' for sim in similarity_scores] # Filter out the similarity scores
return similarity_scores
# Get the most similar intent and its confidence score
def get_most_similar_intent(user_text, utterances, intents, temperature=0.5):
prompt = create_prompt(user_text, utterances)
similarity_scores = get_similarity_scores(prompt, temperature)
print(similarity_scores)
max_index = similarity_scores.index(max(similarity_scores))
print(f'max_index: {max_index}')
most_similar_intent = intents[max_index]
return most_similar_intent, similarity_scores[max_index]
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