App Deployed

app.py

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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+from fpdf import FPDF
+import base64
+from transformers import pipeline
+from openai import OpenAI
+
+pipe = pipeline("sentiment-analysis",
+                model="finiteautomata/bertweet-base-sentiment-analysis")
+
+
+def col_labels(df, column_name):
+    label_count = df[column_name].value_counts()
+    return label_count
+
+
+def plots(labels):
+    fig = px.pie(names=labels.index, values=labels.values)
+    fig.update_layout(
+        showlegend=True,
+        autosize=False,
+        width=500,
+        height=500
+    )
+    st.plotly_chart(fig, use_container_width=True)
+
+
+def generate_pdf_report(df, questions):
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.set_font("Arial", size=12, style='B')
+    pdf.cell(200, 10, txt="Bennett University NAAC Survey Report",
+             ln=True, align="C")
+    pdf.ln(10)
+    for question in questions:
+        pdf.multi_cell(200, 10, txt=question)
+        labels = col_labels(df, question)
+        for label, count in labels.items():
+            pdf.cell(200, 10, txt=f"{label}: {count}", ln=True)
+        pdf.ln(5)
+    pdf_file = "survey_report.pdf"
+    pdf.output(pdf_file)
+    return pdf_file
+
+
+def chunking(arr):
+    max_chunk_size = 1024
+    chunks = []
+    current_chunk = ""
+
+    for recommendation in arr:
+        if len(current_chunk) + len(arr) + len('. ') <= max_chunk_size:
+            current_chunk += recommendation + '. '
+        else:
+            chunks.append(current_chunk[:-2])
+            current_chunk = recommendation + '. '
+
+    if current_chunk:
+        chunks.append(current_chunk[:-2])
+
+    return chunks
+
+
+def generate_summary(text):
+    input_chunks = chunking(text)
+    output_chunks = []
+    client = OpenAI()
+    for chunk in input_chunks:
+        response = client.completions.create(
+            model="gpt-3.5-turbo-instruct",
+            prompt=(
+                f"Please give summary of:\n{chunk}. The summary given should be in bullet points.\n\nSummary:"),
+            temperature=0.7,
+            max_tokens=1024,
+            n=1,
+            stop=None
+        )
+        summary = response.choices[0].text.strip()
+        output_chunks.append(summary)
+    return " ".join(output_chunks)
+
+st.set_page_config(
+    page_title="Bennett University NAAC Survey Report",
+    page_icon="📊",
+    layout="wide"
+)
+
+hide_streamlit_style = """
+    <style>
+        #MainMenu {visibility: hidden;}
+        footer {visibility: hidden;}
+    </style>
+"""
+
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+
+st.markdown(
+    "<h1 style='text-align: center; color: #008080;'>Bennett University NAAC Survey Report</h1>",
+    unsafe_allow_html=True
+)
+
+file = st.file_uploader("Upload Response File", type=['csv'])
+
+if file is not None:
+    st.sidebar.info("File uploaded successfully! Proceed with the analysis.")
+
+    df = pd.read_csv(file)
+
+    questions = [
+        'How much of the syllabus was covered in the class?',
+        'How well did the teachers prepare for the classes?',
+        'How well were the teachers able to communicate?',
+        'The teacher\'s approach to teaching can best be described as',
+        'Fairness of the internal evaluation process by the teachers.',
+        'Was your performance in assignments discussed with you?',
+        'The institute takes active interest in promoting internship, student exchange, field visit opportunities for students.',
+        'The teaching and mentoring process in your institution facilitates you in cognitive, social and emotional growth.',
+        'The institution provides multiple opportunities to learn and grow.',
+        'Teachers inform you about your expected competencies, course outcomes and programme outcomes.',
+        'Your mentor does a necessary follow-up with an assigned task to you.',
+        'The teachers illustrate the concepts through examples and applications.',
+        'The teachers identify your strengths and encourage you with providing right level of challenges.',
+        'Teachers are able to identify your weaknesses and help you to overcome them.',
+        'The institution makes effort to engage students in the monitoring, review and continuous quality improvement of the teaching learning process.',
+        'The institute/ teachers use student centric methods, such as experiential learning, participative learning and problem solving methodologies for enhancing learning experiences.',
+        'Teachers encourage you to participate in extracurricular activities.',
+        'Efforts are made by the institute/ teachers to inculcate soft skills, life skills and employability skills to make you ready for the world of work.',
+        'What percentage of teachers use ICT tools such as LCD projector, Multimedia, etc. while teaching.',
+        'The overall quality of teaching-learning process in your institute is very good.',
+    ]
+
+    st.write("---")
+    st.write("### Survey Questions Analysis")
+    st.write("Below are the analysis results for each survey question:")
+
+    pos_comments = []
+    neg_comments = []
+    neu_comments = []
+    for data in df['Give three observation / suggestions to improve the overall teaching - learning experience in your institution.']:
+        sentiment = pipe(data)[0]['label']
+        if sentiment == "POS":
+            pos_comments.append(data)
+        if sentiment == "NEG":
+            neg_comments.append(data)
+        if sentiment == "NEU":
+            neu_comments.append(data)
+        else:
+            neu_comments.append(data)
+
+    st.subheader("Positive Comments")
+    st.write(generate_summary(pos_comments))
+    st.write("---")
+    st.subheader("Negative Comments")
+    st.write(generate_summary(neg_comments))
+
+    st.sidebar.markdown("---")
+    st.sidebar.write("#### Analysis Settings")
+    selected_questions = [
+        question for question in questions if st.sidebar.checkbox(question, value=True)]
+    for question in selected_questions:
+        st.write("")
+        st.subheader(question)
+        labels = col_labels(df, question)
+        plots(labels)
+
+    st.sidebar.markdown("---")
+    st.sidebar.write("#### Download Report")
+    if st.sidebar.button("Generate PDF Report"):
+        pdf_file = generate_pdf_report(df, selected_questions)
+        with open(pdf_file, "rb") as f:
+            base64_pdf = base64.b64encode(f.read()).decode("utf-8")
+            href = f"<a href='data:application/octet-stream;base64,{base64_pdf}' download='survey_report.pdf'><button>Download PDF Report</button></a>"
+            st.sidebar.markdown(href, unsafe_allow_html=True)

requirements.txt

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+openai
+transformers
+plotly
+fpdf
+emoji
+pandas
+streamlit
+torch