Update app.py

app.py

@@ -9,6 +9,8 @@ from sklearn.neighbors import KNeighborsClassifier
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.naive_bayes import GaussianNB
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+import matplotlib.pyplot as plt
+import seaborn as sns
 
 # File uploader
 st.title("Model Training with Metrics")
@@ -84,12 +86,98 @@ if uploaded_file is not None:
             st.subheader("Model Performance Metrics")
             st.dataframe(metrics_df)
 
-            # Option to download the model performance metrics (Results Table)
+            # Download options
+            st.subheader("Download Model Performance Report in Different Formats")
+
+            # CSV
             st.download_button(
-                label="Download Model Report",
+                label="Download as CSV",
                 data=metrics_df.to_csv(index=False),
                 file_name="model_report.csv",
                 mime="text/csv"
             )
 
-           
+            # Excel
+            st.download_button(
+                label="Download as Excel",
+                data=metrics_df.to_excel(index=False, engine='openpyxl'),
+                file_name="model_report.xlsx",
+                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+            )
+
+            # JSON
+            st.download_button(
+                label="Download as JSON",
+                data=metrics_df.to_json(orient='records'),
+                file_name="model_report.json",
+                mime="application/json"
+            )
+
+            # PDF (using `fpdf` library)
+            from fpdf import FPDF
+
+            def generate_pdf(df):
+                pdf = FPDF()
+                pdf.add_page()
+                pdf.set_font("Arial", size=12)
+                pdf.cell(200, 10, txt="Model Performance Report", ln=True, align="C")
+                pdf.ln(10)
+
+                # Add table header
+                pdf.set_font("Arial", style='B', size=10)
+                for header in df.columns:
+                    pdf.cell(40, 10, header, border=1)
+                pdf.ln()
+
+                # Add table rows
+                pdf.set_font("Arial", size=10)
+                for row in df.values:
+                    for value in row:
+                        pdf.cell(40, 10, str(value), border=1)
+                    pdf.ln()
+
+                return pdf.output(dest='S').encode('latin1')
+
+            # PDF download
+            st.download_button(
+                label="Download as PDF",
+                data=generate_pdf(metrics_df),
+                file_name="model_report.pdf",
+                mime="application/pdf"
+            )
+
+            # Option to download the dataset
+            st.download_button(
+                label="Download Dataset",
+                data=df.to_csv(index=False),
+                file_name="dataset.csv",
+                mime="text/csv"
+            )
+
+            # Generate and download PNG report
+            st.subheader("Download Report as PNG")
+
+            # Create table plot using matplotlib
+            fig, ax = plt.subplots(figsize=(12, 4))  # Adjust the figure size to match the table's layout
+            ax.axis('tight')
+            ax.axis('off')
+            table_data = metrics_df.values
+            table_columns = metrics_df.columns.tolist()
+            
+            table = ax.table(cellText=table_data, colLabels=table_columns, loc='center', cellLoc='center', colLoc='center')
+            table.auto_set_font_size(False)
+            table.set_fontsize(10)
+            table.scale(1.2, 1.2)  # Adjust the scale for better appearance
+
+            # Save the table as a PNG file
+            png_file = "model_report.png"
+            fig.savefig(png_file, bbox_inches='tight', dpi=300)
+
+            # Provide a download button for the PNG file
+            with open(png_file, "rb") as file:
+                st.download_button(
+                    label="Download as PNG",
+                    data=file,
+                    file_name="model_report.png",
+                    mime="image/png"
+                )