v1.3

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.pyc
+
+# Distribution / packaging
+.Python
+build/
+dist/
+*.egg-info/
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.pytest_cache

LICENSE.md

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+MIT License
+
+Copyright (c) 2024 Aniket Kamble
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# 📊 Psychometric Template Generator
+
+Psychometric Template Generator is a data visualization tool built using Python, Streamlit, Pandas, Matplotlib, and Plotly. It allows users to upload a CSV file and create intutive data visualizations just by selecting the features from the data.
+
+## ✨ Features
+
+- Upload CSV data: Allows users to upload CSV files containing the data for psychometric testing.
+- Data preview: Displays a glimpse of the uploaded data using a Streamlit expander.
+- Feature selection: Enables users to choose specific features (columns) for comparison in the charts.
+- Feature validation: Ensures the selected features are numerical for visualization.
+- Chart selection: Provides a dropdown menu for users to select the desired chart type (Line, Bar, Scatter, Pie, Histogram, Heatmap).
+- Chart generation: Creates charts based on the chosen chart type and displays them on the Streamlit app.
+- Line chart: Plots lines for each selected feature over the data index.
+- Bar chart: Creates a bar chart to compare the values of selected features across the data index.
+- Scatter plot: Generates a scatter plot to visualize the relationship between two selected features.
+- Pie chart: Creates a pie chart to represent the distribution of data across the selected features.
+- Histogram: Generates a histogram to show the frequency distribution of a single selected feature.
+- Heatmap: Creates a heatmap to visualize the correlation between all selected features.
+
+## 🛠️ Technologies Used
+
+- Python
+- Streamlit
+- Pands
+- Matplotlib
+- Plotly
+
+## 🚀 Installation
+
+1. Clone this repository to your local machine using:
+   - `git clone`
+2. Install the required Python packages using pip:
+   - `pip install -r requirements.txt`
+
+## 💡 Usage
+
+1. Run the app using:
+   - `streamlit run app.py`
+2. Upload the dataset.
+3. Select features from the data.
+4. Select the Visualization from the given visualization charts and plots.
+5. You're Done, You'll get the visualization from selected features.

app.py

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+# Copyright (c) 2024 Aniket Kamble
+# SPDX-License-Identifier: MIT
+
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import plotly.express as px
+
+# Config
+# st.set_option("deprecation.showPyplotGlobalUse", False)
+
+def main():
+    st.set_page_config(page_title="Psychometric Template Generator", layout="wide")
+    st.title("📊 Psychometric Template Generator")
+
+    # Sidebar for file upload and chart selection
+    with st.sidebar:
+        st.header("Upload Data")
+        uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])
+
+    if uploaded_file:
+        df = pd.read_csv(uploaded_file)
+        display_data_preview(df)
+        selected_features = select_features(df)
+
+        if selected_features:
+            if validate_features(df, selected_features):
+                chart_type = select_chart_type()
+                display_chart(df, selected_features, chart_type)
+
+def display_data_preview(df):
+    """Displays a preview of the uploaded data."""
+    with st.expander("Preview of the uploaded data", expanded=False):
+        st.write(df.head())
+
+def select_features(df):
+    """Allows the user to select features for comparison."""
+    selected_features = st.multiselect("Select features for comparison", df.columns)
+    if not selected_features:
+        st.warning("Please select at least one feature for comparison.")
+    return selected_features
+
+def validate_features(df, selected_features):
+    """Validates that selected features are numeric."""
+    if not all(df[feature].dtype in (int, float) for feature in selected_features):
+        st.error("Selected features must be numeric for visualization.")
+        return False
+    return True
+
+def select_chart_type():
+    """Allows the user to select the type of chart."""
+    with st.sidebar:
+        st.header("Chart Selection")
+        return st.selectbox(
+            "Select the type of chart",
+            ["Line Chart", "Bar Chart", "Scatter Plot", "Pie Chart", "Histogram", "Heatmap"],
+        )
+
+def display_chart(df, selected_features, chart_type):
+    """Displays the selected chart."""
+    st.subheader(f"{chart_type}")
+    if chart_type == "Line Chart":
+        generate_line_chart(df, selected_features)
+    elif chart_type == "Bar Chart":
+        generate_bar_chart(df, selected_features)
+    elif chart_type == "Scatter Plot":
+        generate_scatter_plot(df, selected_features)
+    elif chart_type == "Pie Chart":
+        generate_pie_chart(df, selected_features)
+    elif chart_type == "Histogram":
+        generate_histogram(df, selected_features[0])
+    elif chart_type == "Heatmap":
+        generate_heatmap(df, selected_features)
+
+def generate_line_chart(df, selected_features):
+    fig, ax = plt.subplots(figsize=(15, 8))
+    for feature in selected_features:
+        ax.plot(df.index, df[feature], label=feature)
+
+    ax.set_xlabel("Index")
+    ax.set_ylabel("Values")
+    ax.set_title("Line Chart")
+    ax.legend()
+    st.pyplot(fig)
+
+def generate_bar_chart(df, selected_features):
+    fig = px.bar(df, x=df.index, y=selected_features, barmode="group")
+    fig.update_layout(title="Bar Chart", xaxis_title="Index", yaxis_title="Values", height=600, width=1000)
+    st.plotly_chart(fig)
+
+def generate_scatter_plot(df, selected_features):
+    fig = px.scatter(
+        df,
+        x=selected_features[0],
+        y=selected_features[1],
+        title="Scatter Plot",
+        labels={
+            selected_features[0]: selected_features[0],
+            selected_features[1]: selected_features[1],
+        },
+        height=600,
+        width=1000
+    )
+    st.plotly_chart(fig)
+
+def generate_pie_chart(df, selected_features):
+    fig = px.pie(df, names=selected_features, title="Pie Chart", hole=0.3, height=600, width=1000)
+    st.plotly_chart(fig)
+
+def generate_histogram(df, selected_feature):
+    fig, ax = plt.subplots(figsize=(15, 8))
+    ax.hist(df[selected_feature], bins=20, edgecolor="black")
+    ax.set_xlabel(selected_feature)
+    ax.set_ylabel("Frequency")
+    ax.set_title("Histogram")
+    st.pyplot(fig)
+
+def generate_heatmap(df, selected_features):
+    fig = px.imshow(df[selected_features].corr(), title="Heatmap", height=600, width=1000)
+    st.plotly_chart(fig)
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit==1.35.0
+pandas==2.2.2
+matplotlib==3.8.4
+plotly==5.20.0