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app.py

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+import streamlit as st
+import pandas as pd
+from nltk.tokenize import sent_tokenize
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import nltk
+nltk.download('punkt')
+
+def generate_summary(teacher_feedback):
+    # Tokenize the feedback into sentences
+    sentences = sent_tokenize(teacher_feedback)
+
+    if len(sentences) == 0:
+        st.text("No feedback available.")
+
+    # Encode sentences into BERT embeddings
+    sentence_embeddings = model.encode(sentences)
+
+    # Calculate the mean embedding of all sentences
+    mean_embedding = sentence_embeddings.mean(axis=0, keepdims=True)
+
+    # Calculate cosine similarity between each sentence embedding and the mean embedding
+    cos_similarities = cosine_similarity(sentence_embeddings, mean_embedding)
+
+    # Sort sentences by cosine similarity in descending order
+    sorted_indices = cos_similarities.flatten().argsort()[::-1]
+
+    # Select the top two sentences as representative
+    num_sentences = min(1,2) # Adjust the number of sentences as needed
+    representative_sentences = [sentences[idx] for idx in sorted_indices[:num_sentences]]
+
+    # Generate summary
+    summary = ' '.join(representative_sentences)
+    return summary
+
+st.header('RAMACHANDRA COLLEGE OF ENGINEERING')
+st.title('STUDENT FEEDBACK ANALYZER')
+model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
+
+csv=st.file_uploader('Enter CSV')
+if csv:
+    df = pd.read_csv(csv)
+# Load the dataset
+# Specify the range of teachers to consider
+    start_teacher = 1
+    end_teacher = 5  # Adjust as needed
+    # Generate summary for each teacher in the specified range
+    for i in range(start_teacher, end_teacher + 1):
+        if 'Teacher '+str(i) in df.columns and not df['Teacher '+str(i)].isnull().all():
+            teacher_feedback = df['Teacher '+str(i)].dropna().str.cat(sep=' ')
+            st.text("Summary of feedback for :"+'Teacher '+str(i))
+            st.text(generate_summary(teacher_feedback))
+
+        else:
+            st.text("No feedback available for Teacher"+str(i))

requirements.txt

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+pandas
+streamlit
+numpy
+seaborn
+matplotlib
+nltk
+sentence_transformers
+scikit-learn