Update app.py

app.py

@@ -1,19 +1,16 @@
 import gradio as gr
-import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-
+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
+
 
-# Step 1: Generate synthetic dataset
 def train_and_evaluate_model():
+    # Step 1: Generate synthetic dataset
     np.random.seed(42)
     n_records = 10000
     data = {
@@ -27,45 +24,43 @@ def train_and_evaluate_model():
 
     df = pd.DataFrame(data)
 
-    # Prepare data
+    # Step 2: Prepare data
     X = df.drop('stock_rating', axis=1)
     y = df['stock_rating']
 
-    # Encode target
+    # Step 3: Encode target
     le = LabelEncoder()
     y_encoded = le.fit_transform(y)
 
-    # Train/test split
+    # Step 4: Train/test split
     X_train, X_test, y_train, y_test = train_test_split(
         X, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded
     )
 
-    # Feature scaling
+    # Step 5: Feature scaling
     scaler = StandardScaler()
     X_train_scaled = scaler.fit_transform(X_train)
     X_test_scaled = scaler.transform(X_test)
 
-    # Train model
+    # Step 6: Train model
     model = RandomForestClassifier(random_state=42)
     model.fit(X_train_scaled, y_train)
 
-    # Predict
+    # Step 7: Predict
     y_pred = model.predict(X_test_scaled)
 
-    # Decode labels
+    # Step 8: Decode labels
     y_test_labels = le.inverse_transform(y_test)
     y_pred_labels = le.inverse_transform(y_pred)
 
-    # Metrics
+    # Step 9: Metrics
     acc = accuracy_score(y_test_labels, y_pred_labels)
     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')
-
-    # Classification report
     clf_report = classification_report(y_test_labels, y_pred_labels)
 
-    # Confusion matrix plot
+    # Step 10: 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",
@@ -74,12 +69,13 @@ def train_and_evaluate_model():
     plt.ylabel("Actual")
     plt.title("Confusion Matrix")
 
-    # Save plot
-    cm_plot_path = "confusion_matrix.png"
-    plt.savefig(cm_plot_path)
+    # Save the confusion matrix plot
+    cm_path = "confusion_matrix.png"
+    plt.savefig(cm_path)
     plt.close()
 
-    result = f"""
+    # Combine output (correct Markdown formatting)
+    output = f"""
 ### ✅ Evaluation Metrics:
 - **Accuracy:** {acc:.2f}
 - **Precision:** {prec:.2f}
@@ -89,18 +85,19 @@ def train_and_evaluate_model():
 ---
 
 ### 📊 Classification Report:
-
 """
-    return result, cm_plot_path
+    return output, cm_path
 
 # Gradio Interface
-iface = gr.Interface(
-    fn=train_and_evaluate_model,
-    inputs=[],
-    outputs=[gr.Markdown(), gr.Image(type="filepath")],
-    title="📈 Stock Model Evaluation Dashboard",
-    description="Train a Random Forest classifier on stock data and evaluate model performance with detailed metrics and confusion matrix."
-)
-
-if __name__ == "__main__":
-    iface.launch()
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 Stock Rating Prediction Model Evaluation")
+    gr.Markdown("Click the button below to train the model on synthetic stock data and evaluate its performance.")
+
+    eval_btn = gr.Button("Run Model Evaluation")
+    output_md = gr.Markdown()
+    output_img = gr.Image(type="filepath")
+
+    eval_btn.click(fn=train_and_evaluate_model, outputs=[output_md, output_img])
+
+# Launch app
+demo.launch()