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app.py

@@ -76,3 +76,25 @@ if st.button("🚀 Predict Yield"):
 
     prediction_bags_per_acre = (prediction * 2.47 * 1000) / bag_weight
     st.info(f"Equivalent: {prediction_bags_per_acre:.2f} bags per acre")
+
+        # --- Visualizations ---
+    st.subheader("📊 Yield Insights from Your Inputs")
+
+    fig, axs = plt.subplots(1, 3, figsize=(18, 5))
+
+    # 1. Fertilizer vs Yield
+    sns.barplot(x=["Fertilizer Used"], y=[fertilizer], ax=axs[0], palette="Greens")
+    axs[0].set_ylabel("Kg")
+    axs[0].set_title("Fertilizer Usage")
+
+    # 2. Rainfall
+    sns.barplot(x=["Annual Rainfall"], y=[rainfall], ax=axs[1], palette="Blues")
+    axs[1].set_ylabel("mm")
+    axs[1].set_title("Annual Rainfall")
+
+    # 3. Yield
+    sns.barplot(x=["Predicted Yield"], y=[prediction], ax=axs[2], palette="Oranges")
+    axs[2].set_ylabel("t/ha")
+    axs[2].set_title("Estimated Yield")
+
+    st.pyplot(fig)

requirements.txt

@@ -4,3 +4,5 @@ scikit-learn==1.6.1
 xgboost==2.1.4
 joblib==1.4.2
 streamlit==1.43.2
+matplotlib
+seaborn