Feature extraction and model training

app.py

@@ -66,8 +66,46 @@ dataset_mapping = {
 dataset_path = dataset_mapping.get(problem, "datasets/spam_detection.csv")
 df = pd.read_csv(dataset_path)
 
+# Generated AI Code
+generated_code = f"""
+# AI Model Code
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from {model.__module__} import {model.__class__.__name__}
+
+# Load Data
+df = pd.read_csv('{dataset_path}')
+X = df.iloc[:, :-1]
+y = df.iloc[:, -1]
+
+# Train/Test Split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Scaling
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+# Train Model
+model = {model.__class__.__name__}()
+model.fit(X_train, y_train)
+
+# Predict
+y_pred = model.predict(X_test)
+print(y_pred)
+"""
+
+# Display AI Code
+st.subheader("📜 Generated AI Model Code")
+st.code(generated_code, language="python")
+
+# Download Buttons (Top of UI)
+st.download_button("📥 Download Python Script (.py)", generated_code, file_name="ai_model.py", mime="text/x-python")
+st.download_button("📥 Download Jupyter Notebook (.ipynb)", json.dumps({"cells": [{"cell_type": "code", "source": generated_code.split("\n"), "metadata": {}}], "metadata": {}, "nbformat": 4, "nbformat_minor": 2}), file_name="ai_model.ipynb", mime="application/json")
+
 # Display dataset
-st.subheader("Sample Dataset")
+st.subheader("📊 Sample Dataset")
 st.write(df.head())
 
 # Preprocessing Steps
@@ -163,41 +201,4 @@ if model in ["Random Forest", "Decision Tree"]:
     sns.barplot(x=importance_df["Importance"], y=importance_df["Feature"], ax=ax)
     st.pyplot(fig)
 
-# Show and Download Generated Code
-generated_code = f"""
-# AI Model Code
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
-from {model_instance.__module__} import {model_instance.__class__.__name__}
-
-# Load Data
-df = pd.read_csv('{dataset_path}')
-X = df.iloc[:, :-1]
-y = df.iloc[:, -1]
-
-# Train/Test Split
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-# Scaling
-scaler = StandardScaler()
-X_train = scaler.fit_transform(X_train)
-X_test = scaler.transform(X_test)
-
-# Train Model
-model = {model_instance.__class__.__name__}()
-model.fit(X_train, y_train)
-
-# Predict
-y_pred = model.predict(X_test)
-print(y_pred)
-"""
-
-st.subheader("📜 Generated Code")
-st.code(generated_code, language="python")
-
-# Download buttons
-st.download_button("📥 Download Python Script (.py)", generated_code, file_name="ai_model.py", mime="text/x-python")
-st.download_button("📥 Download Jupyter Notebook (.ipynb)", json.dumps({"cells": [{"cell_type": "code", "source": generated_code.split("\n"), "metadata": {}}], "metadata": {}, "nbformat": 4, "nbformat_minor": 2}), file_name="ai_model.ipynb", mime="application/json")
-
 st.success("Code generated! Download and start using it! 🚀")