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import os
import pandas as pd
import openai
import streamlit as st
from src.E_openAI_embeddings import calculate_openai_similarity, get_openai_embedding
from src.E_openAI_model import get_ballanced_intents, create_prompt, get_most_similar_intent, get_similarity_scores
# Streamlit App
st.title("Intent Detection using GPT-4 and Sentence Transformers")
# side radio button for temperature
# 0.5, 0.7, 0.9
temperature = st.sidebar.radio("Select temperature", [0.5, 0.7, 0.9])
# File uploader
uploaded_file = st.file_uploader("Upload a CSV file", type="csv")
if uploaded_file is not None:
data = pd.read_csv(uploaded_file)
# if data exeed 10000 rows, we will filter for 10000 rows
#filtered_data = get_ballanced_intents(data)
#st.write(filtered_data[:5])
#st.write(f"Filtered data shape: {filtered_data.intent.unique()}")
st.session_state['data'] = data # filtered_data # Store the uploaded file in session state
st.write("CSV file successfully uploaded!")
# Load the data from session state
if 'data' in st.session_state:
data = st.session_state['data']
# Extract utterances and intents
utterances = data['utterance'].tolist()
intents = data['intent'].tolist()
user_text = st.text_input("Enter user text:")
if st.button("Detect Intent"):
if user_text:
most_similar_intent, confidence = get_most_similar_intent(user_text, utterances, intents)
st.write(f"Most similar intent: {most_similar_intent}")
st.write(f"Confidence: {confidence}")
if user_text:
# Get embedding for the user input
user_embedding = get_openai_embedding(user_text)
# Search in FAISS index for top 5 most similar sentences
top_similar_sentences = calculate_openai_similarity(user_embedding, data, top_n=5)
# Display the results
st.write(f"Top 5 similar sentences:")
for i, (sentence, score) in enumerate(top_similar_sentences):
st.write(f"{i+1}. Sentence: {sentence}")
st.write(f"Similarity score: {score:.4f}")
else:
st.write("Please enter some text.")
else:
st.write("Please upload a CSV file.")
# User input
user_input = st.text_input("Enter a sentence:")
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