Update app.py

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
 import pandas as pd
-import dask.dataframe as dd
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
 from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
@@ -16,18 +15,15 @@ import seaborn as sns
 from io import BytesIO
 
 # File uploader
-st.title("Model Training with Metrics and Correlation Heatmap (Optimized for Large Datasets)")
+st.title("Model Training with Metrics and Correlation Heatmap")
 uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"])
 
 if uploaded_file is not None:
-    # Use Dask for large datasets
-    st.write("Loading dataset...")
-    df = dd.read_csv(uploaded_file).compute()  # Convert Dask DataFrame to Pandas DataFrame for further processing
-    st.success("Dataset loaded successfully!")
-
-    # Show a preview of the dataset
-    st.write("Dataset Preview:")
-    st.dataframe(df.head(100))  # Display only the first 100 rows for better performance
+    df = pd.read_csv(uploaded_file)
+    
+    # Show the dataset
+    st.write("Dataset:")
+    st.dataframe(df)
 
     # Convert categorical (str) data to numerical
     st.write("Converting Categorical Columns to Numerical Values:")
@@ -36,12 +32,12 @@ if uploaded_file is not None:
     for col in df.columns:
         if df[col].dtype == 'object' or len(df[col].unique()) <= 10:
             st.write(f"Encoding Column: **{col}**")
-            df[col] = label_encoder.fit_transform(df[col].astype(str))
+            df[col] = label_encoder.fit_transform(df[col])
     
     # Display the dataset after conversion
-    st.write("Dataset After Conversion (Preview):")
-    st.dataframe(df.head(100))  # Display a preview of the converted dataset
-
+    st.write("Dataset After Conversion:")
+    st.dataframe(df)
+    
     # Handle Null Values (Missing Data)
     st.write("Handling Missing (Null) Values:")
     fill_method = st.selectbox("Choose how to handle missing values", ["Drop rows", "Fill with mean/median"])
@@ -76,8 +72,8 @@ if uploaded_file is not None:
     df = cap_extreme_values(df)
     
     # Show cleaned dataset
-    st.write("Cleaned Dataset (Preview):")
-    st.dataframe(df.head(100))  # Display a preview of the cleaned dataset
+    st.write("Cleaned Dataset:")
+    st.dataframe(df)
 
     # Add clean data download option
     st.subheader("Download Cleaned Dataset")
@@ -111,7 +107,7 @@ if uploaded_file is not None:
     high_corr = corr.abs().unstack().sort_values(ascending=False).drop_duplicates()
     high_corr = high_corr[high_corr.index.get_level_values(0) != high_corr.index.get_level_values(1)]
     high_corr_df = pd.DataFrame(high_corr)
-    st.write(high_corr_df.head(10))  # Show top 10 highly correlated pairs
+    st.write(high_corr_df)
 
     target = st.selectbox("Select Target Variable", df.columns)
     features = [col for col in df.columns if col != target]
@@ -148,6 +144,44 @@ if uploaded_file is not None:
         st.subheader("Classification Model Performance Metrics")
         st.dataframe(metrics_df)
 
+        # Save metrics as PNG (table form)
+        fig, ax = plt.subplots(figsize=(8, 4))
+        ax.axis('tight')
+        ax.axis('off')
+        table = plt.table(cellText=metrics_df.values, colLabels=metrics_df.columns, cellLoc='center', loc='center')
+        table.auto_set_font_size(False)
+        table.set_fontsize(10)
+        table.auto_set_column_width(col=list(range(len(metrics_df.columns))))
+        buf = BytesIO()
+        fig.savefig(buf, format="png")
+        buf.seek(0)
+        st.download_button(
+            label="Download Classification Metrics Table as PNG",
+            data=buf,
+            file_name="classification_metrics_table.png",
+            mime="image/png"
+        )
+        
+        # Visualization (Bar Graphs for Classification)
+        st.subheader("Classification Model Performance Metrics Graph")
+        metrics_df.set_index('Model', inplace=True)
+        ax = metrics_df.plot(kind='bar', figsize=(10, 6), colormap='coolwarm', rot=45)
+        plt.title("Classification Models - Performance Metrics")
+        plt.ylabel("Scores")
+        plt.xlabel("Models")
+        st.pyplot(plt)
+
+        # Download button for the bar graph
+        buf = BytesIO()
+        ax.figure.savefig(buf, format="png")
+        buf.seek(0)
+        st.download_button(
+            label="Download Classification Performance Graph as PNG",
+            data=buf,
+            file_name="classification_performance_graph.png",
+            mime="image/png"
+        )
+
     else:  # Continuous target (regression)
         st.subheader("Regression Model Training")
         regressors = {
@@ -175,3 +209,41 @@ if uploaded_file is not None:
         regression_metrics_df = pd.DataFrame(regression_metrics)
         st.subheader("Regression Model Performance Metrics")
         st.dataframe(regression_metrics_df)
+
+        # Save metrics as PNG (table form)
+        fig, ax = plt.subplots(figsize=(8, 4))
+        ax.axis('tight')
+        ax.axis('off')
+        table = plt.table(cellText=regression_metrics_df.values, colLabels=regression_metrics_df.columns, cellLoc='center', loc='center')
+        table.auto_set_font_size(False)
+        table.set_fontsize(10)
+        table.auto_set_column_width(col=list(range(len(regression_metrics_df.columns))))
+        buf = BytesIO()
+        fig.savefig(buf, format="png")
+        buf.seek(0)
+        st.download_button(
+            label="Download Regression Metrics Table as PNG",
+            data=buf,
+            file_name="regression_metrics_table.png",
+            mime="image/png"
+        )
+
+        # Visualization (Bar Graphs for Regression)
+        st.subheader("Regression Model Performance Metrics Graph")
+        regression_metrics_df.set_index('Model', inplace=True)
+        regression_metrics_df.plot(kind='bar', figsize=(10, 6), colormap='coolwarm', rot=45)
+        plt.title("Regression Models - Performance Metrics")
+        plt.ylabel("Scores")
+        plt.xlabel("Models")
+        st.pyplot(plt)
+
+        # Download button for the bar graph
+        buf = BytesIO()
+        plt.savefig(buf, format="png")
+        buf.seek(0)
+        st.download_button(
+            label="Download Regression Performance Graph as PNG",
+            data=buf,
+            file_name="regression_performance_graph.png",
+            mime="image/png"
+        )