This app simulates a full-fledged collaborative platform for data analysis, featuring file uploads, interactive visualizations, session handling, chat functionality, dynamic chart rendering, and more.
#!/usr/bin/env python3
import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import time
# Set App Title
st.title("DataMaster 360: Collaborative Analytics Platform")
# Sidebar: User Settings and File Upload
st.sidebar.header("Settings & File Upload")
uploaded_file = st.sidebar.file_uploader("Upload your dataset (CSV)", type=["csv"])
# User Authentication
st.sidebar.subheader("Login")
username = st.sidebar.text_input("Username")
password = st.sidebar.text_input("Password", type="password")
def authenticate(username, password):
# Simple username/password authentication (could be extended with a database)
if username == "user" and password == "pass":
return True
return False
# Sidebar: Visual Settings
chart_type = st.sidebar.radio("Select Chart Type", ["Line Chart", "Bar Chart", "Scatter Plot", "Histogram", "Pie Chart"])
filter_column = st.sidebar.text_input("Filter Column (Exact Name)")
filter_value = st.sidebar.slider("Filter Values Greater Than", 0, 100, 50)
selected_color = st.sidebar.color_picker("Pick a Chart Color", "#FF5733")
# Sidebar: Additional Collaboration Features
collaboration_mode = st.sidebar.checkbox("Enable Collaboration Mode")
# Session State Setup
if 'authenticated' not in st.session_state:
st.session_state['authenticated'] = False
if st.session_state['authenticated'] == False:
if st.sidebar.button("Login"):
if authenticate(username, password):
st.session_state['authenticated'] = True
st.success("Login successful!")
else:
st.error("Invalid username or password.")
st.stop()
# Main App Section
st.header("Data Overview & Visualization")
# If file uploaded and user authenticated
if uploaded_file:
data = pd.read_csv(uploaded_file)
st.write("### Raw Data Preview")
st.dataframe(data.head(10))
# Filter Column Validation
if filter_column and filter_column in data.columns:
filtered_data = data[data[filter_column] > filter_value]
st.write(f"### Filtered Data (by `{filter_column} > {filter_value}`)")
st.dataframe(filtered_data)
# Show Summary Statistics
with st.expander("Summary Statistics"):
st.write(filtered_data.describe())
# Chart Rendering
st.subheader(f"Visualization: {chart_type}")
if chart_type == "Line Chart":
st.line_chart(filtered_data[filter_column])
elif chart_type == "Bar Chart":
st.bar_chart(filtered_data[filter_column])
elif chart_type == "Scatter Plot":
fig, ax = plt.subplots()
ax.scatter(filtered_data.index, filtered_data[filter_column], color=selected_color)
st.pyplot(fig)
elif chart_type == "Histogram":
fig, ax = plt.subplots()
ax.hist(filtered_data[filter_column], bins=20, color=selected_color)
st.pyplot(fig)
elif chart_type == "Pie Chart":
fig, ax = plt.subplots()
ax.pie(filtered_data[filter_column].value_counts(), labels=data[filter_column].unique(), autopct='%1.1f%%', colors=[selected_color])
st.pyplot(fig)
# Data Download
csv = filtered_data.to_csv(index=False)
st.download_button("Download Filtered Data", data=csv, file_name="filtered_data.csv", mime="text/csv")
else:
st.warning("Please enter a valid column name for filtering.")
# Collaborative Features: Chat System
if collaboration_mode:
st.subheader("Collaboration & Live Chat")
feedback = st.text_area("Leave feedback or analysis notes")
if st.button("Submit Feedback"):
st.write(f"Feedback: {feedback}")
st.balloons()
chat_message = st.chat_input("Send a message to the team")
if chat_message:
st.write(f"You: {chat_message}")
with st.expander("Chat History"):
st.chat_message("user1").write("User 1: This data looks promising!")
st.chat_message("user2").write("User 2: Let's explore more filters.")
# Advanced Data Processing Simulation
st.header("Advanced Data Processing")
with st.spinner("Processing large datasets..."):
for i in range(100):
time.sleep(0.02)
st.success("Processing completed!")
# Metrics Display
st.subheader("Key Metrics")
if uploaded_file:
if filter_column:
st.metric("Max Value", filtered_data[filter_column].max())
st.metric("Min Value", filtered_data[filter_column].min())
st.metric("Mean Value", round(filtered_data[filter_column].mean(), 2))
# Feedback Form
st.subheader("Feedback Form")
with st.form("feedback_form"):
user_name = st.text_input("Your Name")
feedback_message = st.text_area("Your Feedback")
submitted = st.form_submit_button("Submit")
if submitted:
st.write(f"Thank you, {user_name}, for your feedback!")
st.write(f"Feedback Message: {feedback_message}")
# Progress Bar for Data Export
st.subheader("Data Export Progress")
progress = st.progress(0)
for i in range(100):
time.sleep(0.05)
progress.progress(i + 1)
# Simulated LaTeX Formula for Mathematical Analysis
st.latex(r"\int_a^b f(x)dx = F(b) - F(a)")