Update app.py

app.py

@@ -1,10 +1,97 @@
+# app.py
 
-import gradio as gr
+import pandas as pd
+import numpy as np
+import streamlit as st
+from xgboost import XGBRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import r2_score
+from io import BytesIO
 
-def greet(name):
-    return f"Hello, {name}!"
+# -------------------------
+# 🔃 Load Dataset
+# -------------------------
+@st.cache_data
+def load_data():
+    url = "https://raw.githubusercontent.com/yourusername/nigeria-economy-ai/main/Nigeria_Economy_Data.csv"
+    df = pd.read_csv(url)
+    return df
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+df = load_data()
 
-if __name__ == "__main__":
-    demo.launch()
+# -------------------------
+# ⚙️ Model Training
+# -------------------------
+features = ['Agriculture_Contribution', 'Industry_Contribution', 'Services_Contribution', 'Inflation_Rate', 'Govt_Debt']
+target = 'GDP_Billion_USD'
+
+X = df[features]
+y = df[target]
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = XGBRegressor()
+model.fit(X_train, y_train)
+y_pred = model.predict(X_test)
+score = r2_score(y_test, y_pred)
+
+# -------------------------
+# 🎨 Streamlit UI
+# -------------------------
+st.set_page_config(page_title="Nigeria GDP Predictor", layout="wide")
+st.title("🇳🇬 Nigeria GDP Forecasting App")
+st.markdown("Use the controls to simulate policy changes and forecast **GDP** using AI.")
+
+st.sidebar.header("📊 Economic Inputs")
+agri = st.sidebar.slider("Agriculture Contribution (%)", 0.0, 100.0, 25.0)
+indus = st.sidebar.slider("Industry Contribution (%)", 0.0, 100.0, 30.0)
+service = st.sidebar.slider("Services Contribution (%)", 0.0, 100.0, 40.0)
+inflation = st.sidebar.slider("Inflation Rate (%)", 0.0, 100.0, 15.0)
+debt = st.sidebar.slider("Govt Debt (Billion USD)", 0.0, 200.0, 60.0)
+
+# -------------------------
+# 🧮 Make Prediction
+# -------------------------
+input_data = pd.DataFrame([[agri, indus, service, inflation, debt]], columns=features)
+gdp_prediction = model.predict(input_data)[0]
+
+st.subheader("🧠 Predicted GDP Result")
+st.metric(label="Predicted GDP", value=f"${gdp_prediction:,.2f} Billion USD")
+st.caption(f"Model Accuracy (R²): {score:.4f}")
+
+# -------------------------
+# 📈 GDP Trend Line Chart
+# -------------------------
+st.subheader("📉 Historical GDP Trend (Nigeria)")
+gdp_df = df[['Year', 'GDP_Billion_USD']].dropna()
+gdp_df = gdp_df.sort_values("Year")
+st.line_chart(gdp_df.set_index("Year"))
+
+# -------------------------
+# 📥 Download Prediction
+# -------------------------
+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)
+
+st.download_button(
+    label="Download Prediction as CSV",
+    data=csv_data,
+    file_name="gdp_prediction.csv",
+    mime='text/csv'
+)
+
+# -------------------------
+# 🌐 Gradio Integration (optional)
+# -------------------------
+st.subheader("🤖 Gradio AI Simulator (Embed)")
+st.markdown(
+    """
+    <iframe src="https://your-huggingface-username.hf.space" width="100%" height="600" frameborder="0"></iframe>
+    """,
+    unsafe_allow_html=True
+)