Create app.py

app.py

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+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!
+""")