Update app.py

app.py

@@ -3,10 +3,13 @@
 import pandas as pd
 import numpy as np
 import streamlit as st
+import seaborn as sns
+import matplotlib.pyplot as plt
 from xgboost import XGBRegressor
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import r2_score
 from io import BytesIO
+import json
 
 # -------------------------
 # 🔃 Load Dataset
@@ -59,35 +62,74 @@ st.metric(label="Predicted GDP", value=f"${gdp_prediction:,.2f} Billion USD")
 st.caption(f"Model Accuracy (R²): {score:.4f}")
 
 # -------------------------
-# 📈 GDP Trend Line Chart
+# 📈 GDP Trend Chart
 # -------------------------
 st.subheader("📉 Historical GDP Trend (Nigeria)")
-gdp_df = df[['Year', 'Real GDP']].dropna()
-gdp_df = gdp_df.sort_values("Year")
+gdp_df = df[['Year', 'Real GDP']].dropna().sort_values("Year")
 st.line_chart(gdp_df.set_index("Year"))
 
 # -------------------------
-# 📥 Download Prediction
+# 🧾 Extra Visualizations
 # -------------------------
-def convert_df_to_csv(data):
-    return data.to_csv(index=False).encode('utf-8')
+st.subheader("📊 GDP vs Economic Indicators")
+
+cols = st.columns(len(features))
+for i, feat in enumerate(features):
+    with cols[i]:
+        fig, ax = plt.subplots()
+        sns.scatterplot(x=df[feat], y=df['Real GDP'], ax=ax)
+        ax.set_title(f"GDP vs {feat}")
+        st.pyplot(fig)
+
+# -------------------------
+# 🔥 Correlation Heatmap
+# -------------------------
+st.subheader("🔗 Correlation Heatmap")
+corr = df[features + ['Real GDP']].corr()
+fig, ax = plt.subplots()
+sns.heatmap(corr, annot=True, cmap="coolwarm", fmt=".2f", ax=ax)
+st.pyplot(fig)
+
+# -------------------------
+# 📥 Export Prediction
+# -------------------------
+st.subheader("⬇️ Export Predicted Data")
 
-st.subheader("⬇️ Export Prediction")
 output_df = input_data.copy()
 output_df['Predicted_GDP'] = gdp_prediction
-csv_data = convert_df_to_csv(output_df)
 
-st.download_button(
-    label="Download Prediction as CSV",
-    data=csv_data,
-    file_name="gdp_prediction.csv",
-    mime='text/csv'
-)
+# CSV
+csv_data = output_df.to_csv(index=False).encode('utf-8')
+st.download_button("Download as CSV", csv_data, "gdp_prediction.csv", "text/csv")
+
+# Excel
+excel_buffer = BytesIO()
+with pd.ExcelWriter(excel_buffer, engine='xlsxwriter') as writer:
+    output_df.to_excel(writer, index=False, sheet_name='Prediction')
+st.download_button("Download as Excel", excel_buffer.getvalue(), "gdp_prediction.xlsx", "application/vnd.ms-excel")
+
+# JSON
+json_data = output_df.to_json(orient='records')
+st.download_button("Download as JSON", json_data, "gdp_prediction.json", "application/json")
 
 # -------------------------
-# 🌐 Gradio Integration (optional)
+# 🌐 Gradio Integration
 # -------------------------
-st.subheader("🤖 Gradio AI Simulator (Embed)")
+st.subheader("🤖 Gradio Integration (Hugging Face)")
+st.markdown(
+    """
+    You can also create a separate Gradio interface and host it on [Hugging Face Spaces](https://huggingface.co/spaces).
+
+    Once deployed, embed your app here:
+    
+    ```python
+    <iframe src="https://your-huggingface-username.hf.space" width="100%" height="600" frameborder="0"></iframe>
+    ```
+
+    Replace the `src` with your actual Hugging Face space link.
+    """
+)
+
 st.markdown(
     """
     <iframe src="https://your-huggingface-username.hf.space" width="100%" height="600" frameborder="0"></iframe>