Spaces:
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amartyasaran
commited on
App Deployed
Browse files- app.py +175 -0
- requirements.txt +8 -0
app.py
ADDED
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import streamlit as st
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import pandas as pd
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import plotly.express as px
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from fpdf import FPDF
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import base64
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from transformers import pipeline
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from openai import OpenAI
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pipe = pipeline("sentiment-analysis",
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model="finiteautomata/bertweet-base-sentiment-analysis")
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def col_labels(df, column_name):
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label_count = df[column_name].value_counts()
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return label_count
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def plots(labels):
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fig = px.pie(names=labels.index, values=labels.values)
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fig.update_layout(
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showlegend=True,
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autosize=False,
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width=500,
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height=500
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)
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st.plotly_chart(fig, use_container_width=True)
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def generate_pdf_report(df, questions):
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pdf = FPDF()
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pdf.add_page()
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pdf.set_font("Arial", size=12, style='B')
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pdf.cell(200, 10, txt="Bennett University NAAC Survey Report",
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ln=True, align="C")
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pdf.ln(10)
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for question in questions:
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pdf.multi_cell(200, 10, txt=question)
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labels = col_labels(df, question)
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for label, count in labels.items():
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pdf.cell(200, 10, txt=f"{label}: {count}", ln=True)
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pdf.ln(5)
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pdf_file = "survey_report.pdf"
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pdf.output(pdf_file)
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return pdf_file
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def chunking(arr):
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max_chunk_size = 1024
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chunks = []
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current_chunk = ""
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for recommendation in arr:
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if len(current_chunk) + len(arr) + len('. ') <= max_chunk_size:
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current_chunk += recommendation + '. '
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else:
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chunks.append(current_chunk[:-2])
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current_chunk = recommendation + '. '
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if current_chunk:
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chunks.append(current_chunk[:-2])
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return chunks
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def generate_summary(text):
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input_chunks = chunking(text)
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output_chunks = []
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client = OpenAI()
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for chunk in input_chunks:
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response = client.completions.create(
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model="gpt-3.5-turbo-instruct",
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prompt=(
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f"Please give summary of:\n{chunk}. The summary given should be in bullet points.\n\nSummary:"),
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temperature=0.7,
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max_tokens=1024,
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n=1,
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stop=None
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)
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summary = response.choices[0].text.strip()
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output_chunks.append(summary)
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return " ".join(output_chunks)
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st.set_page_config(
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page_title="Bennett University NAAC Survey Report",
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page_icon="📊",
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layout="wide"
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)
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hide_streamlit_style = """
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<style>
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#MainMenu {visibility: hidden;}
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footer {visibility: hidden;}
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</style>
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"""
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st.markdown(hide_streamlit_style, unsafe_allow_html=True)
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st.markdown(
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"<h1 style='text-align: center; color: #008080;'>Bennett University NAAC Survey Report</h1>",
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unsafe_allow_html=True
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)
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file = st.file_uploader("Upload Response File", type=['csv'])
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if file is not None:
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st.sidebar.info("File uploaded successfully! Proceed with the analysis.")
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df = pd.read_csv(file)
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questions = [
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'How much of the syllabus was covered in the class?',
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'How well did the teachers prepare for the classes?',
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'How well were the teachers able to communicate?',
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'The teacher\'s approach to teaching can best be described as',
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'Fairness of the internal evaluation process by the teachers.',
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'Was your performance in assignments discussed with you?',
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'The institute takes active interest in promoting internship, student exchange, field visit opportunities for students.',
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'The teaching and mentoring process in your institution facilitates you in cognitive, social and emotional growth.',
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'The institution provides multiple opportunities to learn and grow.',
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'Teachers inform you about your expected competencies, course outcomes and programme outcomes.',
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'Your mentor does a necessary follow-up with an assigned task to you.',
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'The teachers illustrate the concepts through examples and applications.',
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'The teachers identify your strengths and encourage you with providing right level of challenges.',
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'Teachers are able to identify your weaknesses and help you to overcome them.',
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'The institution makes effort to engage students in the monitoring, review and continuous quality improvement of the teaching learning process.',
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'The institute/ teachers use student centric methods, such as experiential learning, participative learning and problem solving methodologies for enhancing learning experiences.',
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'Teachers encourage you to participate in extracurricular activities.',
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'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.',
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'What percentage of teachers use ICT tools such as LCD projector, Multimedia, etc. while teaching.',
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'The overall quality of teaching-learning process in your institute is very good.',
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]
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st.write("---")
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st.write("### Survey Questions Analysis")
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st.write("Below are the analysis results for each survey question:")
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pos_comments = []
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neg_comments = []
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neu_comments = []
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for data in df['Give three observation / suggestions to improve the overall teaching - learning experience in your institution.']:
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sentiment = pipe(data)[0]['label']
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if sentiment == "POS":
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pos_comments.append(data)
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if sentiment == "NEG":
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neg_comments.append(data)
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if sentiment == "NEU":
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neu_comments.append(data)
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else:
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neu_comments.append(data)
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st.subheader("Positive Comments")
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st.write(generate_summary(pos_comments))
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st.write("---")
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st.subheader("Negative Comments")
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st.write(generate_summary(neg_comments))
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st.sidebar.markdown("---")
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st.sidebar.write("#### Analysis Settings")
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selected_questions = [
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question for question in questions if st.sidebar.checkbox(question, value=True)]
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for question in selected_questions:
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st.write("")
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st.subheader(question)
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labels = col_labels(df, question)
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plots(labels)
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st.sidebar.markdown("---")
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st.sidebar.write("#### Download Report")
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if st.sidebar.button("Generate PDF Report"):
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pdf_file = generate_pdf_report(df, selected_questions)
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with open(pdf_file, "rb") as f:
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base64_pdf = base64.b64encode(f.read()).decode("utf-8")
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href = f"<a href='data:application/octet-stream;base64,{base64_pdf}' download='survey_report.pdf'><button>Download PDF Report</button></a>"
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st.sidebar.markdown(href, unsafe_allow_html=True)
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requirements.txt
ADDED
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
1 |
+
openai
|
2 |
+
transformers
|
3 |
+
plotly
|
4 |
+
fpdf
|
5 |
+
emoji
|
6 |
+
pandas
|
7 |
+
streamlit
|
8 |
+
torch
|