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import numpy as np
import pandas as pd
from pathlib import Path
from sentence_transformers import SentenceTransformer
from sklearn.linear_model import LogisticRegression
from joblib import dump, load
def pickle_model(model):
DATA_DIR = Path(__file__).parent.parent / "mathtext_fastapi" / "data" / "intent_classification_model.joblib"
dump(model, DATA_DIR)
def create_intent_classification_model():
encoder = SentenceTransformer('all-MiniLM-L6-v2')
# path = list(Path.cwd().glob('*.csv'))
DATA_DIR = Path(__file__).parent.parent / "mathtext_fastapi" / "data" / "labeled_data.csv"
print("DATA_DIR")
print(f"{DATA_DIR}")
with open(f"{DATA_DIR}",'r', newline='', encoding='utf-8') as f:
df = pd.read_csv(f)
df = df[df.columns[:2]]
df = df.dropna()
X_explore = np.array([list(encoder.encode(x)) for x in df['Utterance']])
X = np.array([list(encoder.encode(x)) for x in df['Utterance']])
y = df['Label']
model = LogisticRegression(class_weight='balanced')
model.fit(X, y, sample_weight=None)
print("MODEL")
print(model)
pickle_model(model)
def retrieve_intent_classification_model():
DATA_DIR = Path(__file__).parent.parent / "mathtext_fastapi" / "data" / "intent_classification_model.joblib"
model = load(DATA_DIR)
return model
encoder = SentenceTransformer('all-MiniLM-L6-v2')
# model = retrieve_intent_classification_model()
DATA_DIR = Path(__file__).parent.parent / "mathtext_fastapi" / "data" / "intent_classification_model.joblib"
model = load(DATA_DIR)
def predict_message_intent(message):
tokenized_utterance = np.array([list(encoder.encode(message))])
predicted_label = model.predict(tokenized_utterance)
predicted_probabilities = model.predict_proba(tokenized_utterance)
confidence_score = predicted_probabilities.max()
return {"type": "intent", "data": predicted_label[0], "confidence": confidence_score} |