Update app.py

app.py

@@ -11,7 +11,7 @@ EXCEL_PATH = "microalgae_pot_experiment_corrected_doses.xlsx"
 MODEL_PATH = "EcoGrowAI_yield_model.json"
 df = pd.read_excel(EXCEL_PATH)
 
-# Apply encoding used during training
+# Encode categorical columns
 for col in ["Crop", "Microalgae_Strain"]:
     le = LabelEncoder()
     df[col] = le.fit_transform(df[col])
@@ -19,7 +19,7 @@ for col in ["Crop", "Microalgae_Strain"]:
 drop_cols = ["Yield_g_per_pot", "Relative_Yield_%"]
 feature_cols = [c for c in df.columns if c not in drop_cols]
 
-# Load model
+# Load XGBoost model
 model = XGBRegressor()
 model.load_model(MODEL_PATH)
 
@@ -33,9 +33,8 @@ def parse_index(label):
     return int(label.split("index=")[1].strip(")"))
 
 def on_row_select(row_label):
-    idx = parse_index(row_label)
-    row_df = df.iloc[[idx]][feature_cols]
-    return "### Selected Row (Input Features)\n\n" + row_df.T.to_markdown()
+    # Only return the selected row label (to show left)
+    return f"### Selected Row:\n{row_label}"
 
 def on_predict(row_label):
     idx = parse_index(row_label)
@@ -53,7 +52,8 @@ def on_predict(row_label):
     error = abs(pred_rounded - actual)
     error_rounded = float(np.round(error, 3))
 
-    # Markdown display
+    # Markdown display (Right column)
+    features_md = "### Input Features\n\n" + row.to_frame().to_markdown()
     result_md = (
         f"### Prediction Result\n\n"
         f"**Predicted Yield (g per pot):** {pred_rounded}\n\n"
@@ -61,7 +61,7 @@ def on_predict(row_label):
         f"**Absolute Error:** {error_rounded}"
     )
 
-    return "✅ Prediction complete", result_md
+    return features_md, result_md
 
 # -------------------------------
 # Dropdown options
@@ -69,7 +69,7 @@ def on_predict(row_label):
 row_choices = [make_row_label(i) for i in range(len(df))]
 
 # -------------------------------
-# Gradio App Layout
+# Gradio Layout
 # -------------------------------
 with gr.Blocks(css="""
 body {background:#f5f7fa;color:#0b1220;font-family:Inter,sans-serif;}
@@ -78,28 +78,26 @@ body {background:#f5f7fa;color:#0b1220;font-family:Inter,sans-serif;}
 """) as demo:
     
     with gr.Row():
-        # LEFT COLUMN: controls + selected row info
+        # LEFT COLUMN: Dropdown + Predict + Selected row
         with gr.Column(elem_classes="card", scale=1):
-            gr.Markdown("# 🌿 EcoGrowAI — Yield Prediction")
-            gr.Markdown("Select any experimental row and predict **Yield (g per pot)** using the trained XGBoost model.")
-            
+            gr.Markdown("# EcoGrowAI — Yield Prediction")
+            gr.Markdown("Select a row to predict its Yield (g per pot).")
             row_dropdown = gr.Dropdown(
                 label="Select Row", choices=row_choices, value=row_choices[0], interactive=True
             )
-            
             predict_button = gr.Button("Predict", variant="primary")
-            status = gr.Markdown("")
-            row_info = gr.Markdown("No row selected yet.")  # Selected row info on left
+            selected_row_label = gr.Markdown("No row selected yet.")  # only row label
 
-        # RIGHT COLUMN: prediction result
-        with gr.Column(elem_classes="card", scale=1):
-            result_md = gr.Markdown("Prediction result will appear here.")
+        # RIGHT COLUMN: input features + prediction result
+        with gr.Column(elem_classes="card", scale=2):
+            features_md = gr.Markdown("Input features will appear here.")
+            result_md = gr.Markdown("Prediction results will appear here.")
 
     # -------------------------------
-    # Event bindings
+    # Bind events
     # -------------------------------
-    row_dropdown.change(on_row_select, row_dropdown, row_info)
-    predict_button.click(on_predict, row_dropdown, [status, result_md])
+    row_dropdown.change(on_row_select, row_dropdown, selected_row_label)
+    predict_button.click(on_predict, row_dropdown, [features_md, result_md])
 
 # -------------------------------
 # Launch