Update app.py

app.py

@@ -3,13 +3,10 @@
 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
@@ -62,74 +59,35 @@ st.metric(label="Predicted GDP", value=f"${gdp_prediction:,.2f} Billion USD")
 st.caption(f"Model Accuracy (R²): {score:.4f}")
 
 # -------------------------
-# 📈 GDP Trend Chart
+# 📈 GDP Trend Line Chart
 # -------------------------
 st.subheader("📉 Historical GDP Trend (Nigeria)")
-gdp_df = df[['Year', 'Real GDP']].dropna().sort_values("Year")
+gdp_df = df[['Year', 'Real GDP']].dropna()
+gdp_df = gdp_df.sort_values("Year")
 st.line_chart(gdp_df.set_index("Year"))
 
 # -------------------------
-# 🧾 Extra Visualizations
+# 📥 Download Prediction
 # -------------------------
-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")
+def convert_df_to_csv(data):
+    return data.to_csv(index=False).encode('utf-8')
 
+st.subheader("⬇️ Export Prediction")
 output_df = input_data.copy()
 output_df['Predicted_GDP'] = gdp_prediction
+csv_data = convert_df_to_csv(output_df)
 
-# 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")
+st.download_button(
+    label="Download Prediction as CSV",
+    data=csv_data,
+    file_name="gdp_prediction.csv",
+    mime='text/csv'
+)
 
 # -------------------------
-# 🌐 Gradio Integration
+# 🌐 Gradio Integration (optional)
 # -------------------------
-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.subheader("🤖 Gradio AI Simulator (Embed)")
 st.markdown(
     """
     <iframe src="https://your-huggingface-username.hf.space" width="100%" height="600" frameborder="0"></iframe>