Update app.py

app.py

@@ -4,7 +4,10 @@ import numpy as np
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler, LabelEncoder
 from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report
+from sklearn.metrics import (
+    accuracy_score, precision_score, recall_score, f1_score,
+    confusion_matrix, classification_report
+)
 import seaborn as sns
 import matplotlib.pyplot as plt
 
@@ -58,9 +61,31 @@ def train_and_evaluate_model():
     prec = precision_score(y_test_labels, y_pred_labels, average='weighted')
     rec = recall_score(y_test_labels, y_pred_labels, average='weighted')
     f1 = f1_score(y_test_labels, y_pred_labels, average='weighted')
-    clf_report = classification_report(y_test_labels, y_pred_labels)
 
-    # Step 10: Confusion matrix
+    # Step 10: Create Classification Report as DataFrame
+    report_dict = classification_report(y_test_labels, y_pred_labels, output_dict=True)
+    report_df = pd.DataFrame(report_dict).transpose().round(2)
+
+    # Step 11: Plot classification report as table with grid
+    fig, ax = plt.subplots(figsize=(8, 4))
+    ax.axis('off')
+    tbl = ax.table(
+        cellText=report_df.values,
+        colLabels=report_df.columns,
+        rowLabels=report_df.index,
+        cellLoc='center',
+        loc='center'
+    )
+    tbl.auto_set_font_size(False)
+    tbl.set_fontsize(10)
+    tbl.scale(1.2, 1.2)
+    for key, cell in tbl.get_celld().items():
+        cell.set_linewidth(0.8)
+    cr_path = "classification_report.png"
+    plt.savefig(cr_path, bbox_inches='tight')
+    plt.close()
+
+    # Step 12: Confusion matrix
     cm = confusion_matrix(y_test_labels, y_pred_labels, labels=le.classes_)
     plt.figure(figsize=(6, 5))
     sns.heatmap(cm, annot=True, fmt="d", cmap="Blues",
@@ -68,25 +93,19 @@ def train_and_evaluate_model():
     plt.xlabel("Predicted")
     plt.ylabel("Actual")
     plt.title("Confusion Matrix")
-
-    # Step 11: Save the confusion matrix plot
     cm_path = "confusion_matrix.png"
-    plt.savefig(cm_path)
+    plt.savefig(cm_path, bbox_inches='tight')
     plt.close()
 
-    # Step 12: Combine output (correct Markdown formatting)
+    # Step 13: Return outputs
     output = f"""
 ### ✅ Evaluation Metrics:
 - **Accuracy:** {acc:.2f}
 - **Precision:** {prec:.2f}
 - **Recall:** {rec:.2f}
 - **F1 Score:** {f1:.2f}
-
----
-
-### 📊 Classification Report:
 """
-    return output, cm_path
+    return output, cr_path, cm_path
 
 
 # Gradio Interface
@@ -96,9 +115,10 @@ with gr.Blocks() as demo:
 
     eval_btn = gr.Button("Click here... To Run Model Evaluation")
     output_md = gr.Markdown()
-    output_img = gr.Image(type="filepath")
+    report_img = gr.Image(type="filepath", label="📊 Classification Report")
+    cm_img = gr.Image(type="filepath", label="📉 Confusion Matrix")
 
-    eval_btn.click(fn=train_and_evaluate_model, outputs=[output_md, output_img])
+    eval_btn.click(fn=train_and_evaluate_model,
+                   outputs=[output_md, report_img, cm_img])
 
-# Launch app
 demo.launch()