Update app.py

app.py

@@ -1,72 +1,27 @@
-import streamlit as st
-import pandas as pd
-import plotly.express as px
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-# Load your dataset here
-df = pd.read_csv('your_data.csv')  # Replace with your actual dataset file
-
-# Streamlit Interface for Plotting Scatter Plot and Simple Chart
-def streamlit_interface():
-    st.title("Interactive Data Visualization App")
-
-    # Display dataframe
-    st.write(df.head())
-
-    # Plot interactive scatter plot with Plotly
-    scatter_fig = px.scatter(df, x='column_x', y='column_y', color='category_column', title="Interactive Scatter Plot")
-    st.plotly_chart(scatter_fig)
-
-    # Input fields for custom plotting
-    x_values = st.text_input("Enter X values (comma-separated)", "1,2,3,4,5")
-    y_values = st.text_input("Enter Y values (comma-separated)", "2,4,6,8,10")
-
-    # Button to plot custom chart
-    if st.button("Plot Custom Chart"):
-        plot_custom_chart(x_values, y_values)
-
-    # Correlation Heatmap using Seaborn
-    st.subheader("Correlation Heatmap")
-    plot_correlation_heatmap(df)
-
-# Function to plot custom chart
-def plot_custom_chart(x_values, y_values):
-    try:
-        # Convert the X and Y values from string input to lists of integers
-        x_vals = list(map(int, x_values.split(',')))
-        y_vals = list(map(int, y_values.split(',')))
-
-        # Ensure both X and Y values have the same length
-        if len(x_vals) != len(y_vals):
-            st.error("Error: X and Y values must have the same number of elements.")
-            return
-
-        # Plot using Matplotlib
-        plt.figure(figsize=(8, 5))
-        plt.plot(x_vals, y_vals, marker='o', color='b', label="Data Points")
-        plt.title("Custom Data Visualization")
-        plt.xlabel("X Values")
-        plt.ylabel("Y Values")
-        plt.grid(True)
-        plt.legend()
-
-        # Display the plot
-        st.pyplot(plt)
-
-    except ValueError:
-        st.error("Error: Please make sure the values are valid integers.")
-
-# Function to plot correlation heatmap
-def plot_correlation_heatmap(df):
-    corr = df.corr()  # Calculate correlation matrix
-    plt.figure(figsize=(10, 8))
-    sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f')
-    plt.title('Correlation Heatmap')
-    st.pyplot(plt)  # Display in Streamlit
-
-# Run the Streamlit interface
-if __name__ == "__main__":
-    streamlit_interface()
+def advanced_plot(df, chart_type):
+    plt.figure(figsize=(8, 6))
+    
+    if chart_type == "Bar":
+        sns.barplot(x='Category', y='Value', data=df)
+    elif chart_type == "Line":
+        sns.lineplot(x='Category', y='Value', data=df)
+    elif chart_type == "Pie":
+        df.set_index('Category')['Value'].plot.pie(autopct='%1.1f%%', figsize=(8, 6))
+    
+    plt.title(f'{chart_type} Chart')
+    plt.xlabel('Category')
+    plt.ylabel('Value')
+    return plt
+
+def gradio_interface_advanced(file, chart_type):
+    df = pd.read_csv(file.name)
+    return advanced_plot(df, chart_type)
+
+interface = gr.Interface(fn=gradio_interface_advanced, 
+                         inputs=[gr.File(label="Upload CSV"), 
+                                 gr.Dropdown(["Bar", "Line", "Pie"], label="Select Chart Type")], 
+                         outputs=gr.Plot())
+                         
+interface.launch()