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studfaceval / HF_pipeline.py

b09a689 over 2 years ago

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from transformers import pipeline
import streamlit as st

MODEL_URLS = {
    "DISTILIBERT MODEL": "MENG21/stud-fac-eval-distilbert-base-uncased",
    "BERT-LARGE MODEL": "MENG21/stud-fac-eval-bert-large-uncased",
    "BERT-BASE MODEL": "MENG21/stud-fac-eval-bert-base-uncased"
}

@st.cache_resource(experimental_allow_widgets=True, show_spinner=False)
def analyze_sintement(text, selected_model):
    # st.write(selected_model)
    
    # API_URL = MODEL_URLS.get(selected_model, MODEL_URLS[selected_model])  # Get API URL based on selected model
    # Create a text classification pipeline
    classifier = pipeline("text-classification", model=MODEL_URLS[selected_model])

    result = classifier(text)
    # st.text(result)
    return result[0]['label'], result[0]['score']

	from transformers import pipeline
	import streamlit as st

	MODEL_URLS = {
	"DISTILIBERT MODEL": "MENG21/stud-fac-eval-distilbert-base-uncased",
	"BERT-LARGE MODEL": "MENG21/stud-fac-eval-bert-large-uncased",
	"BERT-BASE MODEL": "MENG21/stud-fac-eval-bert-base-uncased"
	}

	@st.cache_resource(experimental_allow_widgets=True, show_spinner=False)
	def analyze_sintement(text, selected_model):
	# st.write(selected_model)

	# API_URL = MODEL_URLS.get(selected_model, MODEL_URLS[selected_model]) # Get API URL based on selected model
	# Create a text classification pipeline
	classifier = pipeline("text-classification", model=MODEL_URLS[selected_model])

	result = classifier(text)
	# st.text(result)
	return result[0]['label'], result[0]['score']