Spaces:
Sleeping
Sleeping
File size: 2,612 Bytes
232459a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
import streamlit as st
import pandas as pd
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v2.0")
def predict_teacher_course(feedback):
sequence_to_classify = feedback
candidate_labels = ["teacher", "course"]
output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
return str(output['labels'][0])
def predict_sentiment(feedback):
sequence_to_classify = feedback
candidate_labels = ["positive", "negative", "neutral"]
output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
return str(output['labels'][0])
def predict_teacher_aspect(feedback):
sequence_to_classify = feedback
candidate_labels = ['general', 'teaching skills', 'behaviour', 'knowledge', 'experience', 'assessment']
output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
return str(output['labels'][0])
def predict_course_aspect(feedback):
sequence_to_classify = feedback
candidate_labels = ['relevancy', 'general', 'content', 'learning material', 'pace']
output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
return str(output['labels'][0])
# Streamlit app layout
st.set_page_config(page_title="Aspect-based Sentiment Anlaysis of Student Feedback", layout="centered", initial_sidebar_state="auto")
st.markdown("""
#### This application analyzes the student feedback to determine whether it is about a teacher or a course, detects sentiment, and identifies important teacher or course aspects.
""")
# Get user input
user_input = st.text_area("Enter the feedback or comments for analysis:", height=200)
if st.button("Analyze Text"):
if user_input.strip():
# Predict whether it's about teacher or course
type_result = predict_teacher_course(user_input)
sentiment_result = predict_sentiment(user_input)
if type_result == 'teacher':
aspect_result = predict_teacher_aspect(user_input)
else:
aspect_result = predict_course_aspect(user_input)
# Display the results in a nice way
st.subheader("Analysis Results")
st.markdown(f"**Type:** `{type_result}`")
st.markdown(f"**Sentiment:** `{sentiment_result}`")
st.write(f"**Aspect:** `{aspect_result}`")
else:
st.error("Please enter some text for analysis.")
# Add a footer
st.markdown("---")
st.markdown("**Developed by Sarang Shaikh**")
st.markdown("""
Feel free to reach out for more information or suggestions!
""") |