Add Space configuration with metadata

README.md

@@ -1,65 +1,40 @@
-# 📊 Advance Data Visualization Tool
+---
+title: Advance Data Visualization Tool
+emoji: 📊
+colorFrom: blue
+colorTo: purple
+sdk: docker
+pinned: false
+license: mit
+---
 
-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.
+# 📊 Advance Data Visualization Tool
 
-🚀 **Live Demo**: [View on Hugging Face Spaces](https://huggingface.co/spaces/aniket47/Advance-Data-Visualization-Tool)
+An interactive data visualization tool built with Streamlit that allows users to upload CSV files and create various types of charts and plots with just a few clicks.
 
 ## ✨ 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.
+- **Multiple Chart Types**: Line charts, bar charts, scatter plots, pie charts, histograms, and heatmaps
+- **Easy Data Upload**: Simple CSV file upload interface
+- **Feature Selection**: Choose which columns to visualize
+- **Interactive Plots**: Powered by Plotly and Matplotlib
+- **Correlation Analysis**: Heatmap visualization for feature correlations
+
+## 🚀 How to Use
 
-## 🛠️ Technologies Used
+1. Upload your CSV file using the sidebar
+2. Select the features (columns) you want to visualize
+3. Choose your preferred chart type
+4. View your interactive visualization!
+
+## 🛠️ Built With
 
 - Python
 - Streamlit
-- Pands
+- Pandas
 - Matplotlib
 - Plotly
 
-## 🚀 Installation
-
-1. Clone this repository to your local machine using:
-   - `git clone https://github.com/Aniket-404/Advance-Data-Visualization-Tool.git`
-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.
-
-## 🐳 Docker Deployment
-
-### Run with Docker locally:
-```bash
-docker build -t data-viz-tool .
-docker run -p 7860:7860 data-viz-tool
-```
-
-Then open your browser to `http://localhost:7860`
+## 📝 License
 
-### Deploy to Hugging Face Spaces:
-1. Create a new Space at [huggingface.co/new-space](https://huggingface.co/new-space)
-2. Select **Docker** as the SDK
-3. Push your code to the Space repository:
-```bash
-git remote add space https://huggingface.co/spaces/aniket47/Advance-Data-Visualization-Tool
-git add .
-git commit -m "Add Docker support for HF Spaces"
-git push space main
-```
+MIT License - See LICENSE.md for details

README_HF.md

@@ -0,0 +1,40 @@
+---
+title: Advance Data Visualization Tool
+emoji: 📊
+colorFrom: blue
+colorTo: purple
+sdk: docker
+pinned: false
+license: mit
+---
+
+# 📊 Advance Data Visualization Tool
+
+An interactive data visualization tool built with Streamlit that allows users to upload CSV files and create various types of charts and plots with just a few clicks.
+
+## ✨ Features
+
+- **Multiple Chart Types**: Line charts, bar charts, scatter plots, pie charts, histograms, and heatmaps
+- **Easy Data Upload**: Simple CSV file upload interface
+- **Feature Selection**: Choose which columns to visualize
+- **Interactive Plots**: Powered by Plotly and Matplotlib
+- **Correlation Analysis**: Heatmap visualization for feature correlations
+
+## 🚀 How to Use
+
+1. Upload your CSV file using the sidebar
+2. Select the features (columns) you want to visualize
+3. Choose your preferred chart type
+4. View your interactive visualization!
+
+## 🛠️ Built With
+
+- Python
+- Streamlit
+- Pandas
+- Matplotlib
+- Plotly
+
+## 📝 License
+
+MIT License - See LICENSE.md for details