Update app.py

app.py

@@ -3,11 +3,9 @@ import numpy as np
 import gradio as gr
 from xgboost import XGBRegressor
 
-# === Configuration ===
 EXCEL_PATH = "microalgae_pot_experiment_corrected_doses.xlsx"
 MODEL_PATH = "EcoGrowAI_yield_model.json"
 
-# === Load data and model ===
 df = pd.read_excel(EXCEL_PATH)
 drop_cols = ["Yield_g_per_pot", "Relative_Yield_%"]
 feature_cols = [c for c in df.columns if c not in drop_cols]
@@ -15,78 +13,56 @@ feature_cols = [c for c in df.columns if c not in drop_cols]
 model = XGBRegressor()
 model.load_model(MODEL_PATH)
 
-# === Helpers ===
+
 def make_row_label(i):
     return f"Row {i+1} (index={i})"
 
+
 def parse_index(label):
     return int(label.split("index=")[1].strip(")"))
 
-# === Logic ===
-def show_row(row_label):
-    try:
-        idx = parse_index(row_label)
-        row_df = df.iloc[idx][feature_cols].to_frame().T
-        return "### Selected Row\n\n" + row_df.T.to_markdown()
-    except Exception:
-        return "Invalid row label."
-
-def predict_yield(row_label):
-    try:
-        idx = parse_index(row_label)
-        row = df.iloc[idx][feature_cols]
-        X_row = row.astype(float).values.reshape(1, -1)
-        pred = model.predict(X_row)[0]
-        pred_rounded = float(np.round(pred, 3))
-        features_md = "### Input Features\n\n" + row.to_frame().to_markdown()
-        result_md = f"### Predicted Yield (g per pot)\n\n**{pred_rounded}**"
-        return "✅ Prediction complete", features_md, result_md
-    except Exception as e:
-        return f"Error: {e}", "", ""
-
-# === UI ===
+
+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()
+
+
+def on_predict(row_label):
+    idx = parse_index(row_label)
+    row = df.iloc[idx][feature_cols]
+    X_row = row.astype(float).values.reshape(1, -1)
+    pred = model.predict(X_row)[0]
+    pred_rounded = float(np.round(pred, 3))
+    features_md = "### Input Features\n\n" + row.to_frame().to_markdown()
+    result_md = f"### Predicted Yield (g per pot)\n\n**{pred_rounded}**"
+    return "✅ Prediction complete", features_md, result_md
+
+
 row_choices = [make_row_label(i) for i in range(len(df))]
 
 with gr.Blocks(css="""
-/* Professional minimal styling */
-body { background: #f5f7fa; color: #0b1220; font-family: Inter, sans-serif; }
-.gradio-container { max-width: 900px; margin: 20px auto; }
-.card { background: white; padding: 22px; border-radius: 10px; 
-        box-shadow: 0 4px 14px rgba(0,0,0,0.05); }
-h1 { margin-bottom: 12px; }
+body {background:#f5f7fa;color:#0b1220;font-family:Inter,sans-serif;}
+.gradio-container{max-width:900px;margin:20px auto;}
+.card{background:white;padding:20px;border-radius:10px;
+box-shadow:0 4px 16px rgba(0,0,0,0.05);}
 """) as demo:
+    with gr.Column(elem_classes="card"):
+        gr.Markdown("# EcoGrowAI — Yield Prediction")
+        gr.Markdown("Select any experimental row and predict **Yield (g per pot)** using the trained XGBoost model.")
+
+        row_dropdown = gr.Dropdown(
+            label="Select Row", choices=row_choices, value=row_choices[0], interactive=True
+        )
+
+        row_info = gr.Markdown("No row selected yet.")
+        predict_button = gr.Button("Predict", variant="primary")
+        status = gr.Markdown("")
+        features_md = gr.Markdown("")
+        result_md = gr.Markdown("")
+
+    row_dropdown.change(on_row_select, row_dropdown, row_info)
+    predict_button.click(on_predict, row_dropdown, [status, features_md, result_md])
 
-    gr.Markdown("# EcoGrowAI — Yield Prediction")
-    gr.Markdown("Select a row from the dataset and predict **Yield (g per pot)** using the trained XGBoost model.")
-
-    with gr.Row(elem_classes="card"):
-        with gr.Column():
-            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("")
-        with gr.Column():
-            row_display = gr.Markdown("No row selected yet.")
-            features_display = gr.Markdown("")
-            result_display = gr.Markdown("")
-
-    # --- Bind events ---
-    row_dropdown.change(
-        fn=show_row,
-        inputs=[row_dropdown],
-        outputs=[row_display]
-    )
-
-    predict_button.click(
-        fn=predict_yield,
-        inputs=[row_dropdown],
-        outputs=[status, features_display, result_display]
-    )
-
-# === Launch ===
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)