Update app.py

app.py

@@ -3,99 +3,90 @@ import numpy as np
 import gradio as gr
 from xgboost import XGBRegressor
 
-# --- Configuration ---
+# === Configuration ===
 EXCEL_PATH = "microalgae_pot_experiment_corrected_doses.xlsx"
 MODEL_PATH = "EcoGrowAI_yield_model.json"
 
-# --- Load data & model ---
-try:
-    df = pd.read_excel(EXCEL_PATH)
-except Exception as e:
-    raise RuntimeError(f"Failed to load Excel file: {e}")
-
-# Drop targets for prediction
-drop_cols = ['Yield_g_per_pot', 'Relative_Yield_%']
+# === 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]
 
-# Load model
 model = XGBRegressor()
 model.load_model(MODEL_PATH)
 
-# --- Helpers ---
+# === Helpers ===
 def make_row_label(i):
     return f"Row {i+1} (index={i})"
 
 def parse_index(label):
-    """Extract numeric index from dropdown label."""
     return int(label.split("index=")[1].strip(")"))
 
-# --- Callbacks ---
-def on_row_select(row_label):
+# === 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."
 
-    row_df = df.iloc[idx][feature_cols].to_frame().T
-    return "### Selected Row (Input Features)\n\n" + row_df.T.to_markdown()
-
-def on_predict(row_label):
+def predict_yield(row_label):
     try:
         idx = parse_index(row_label)
-    except Exception:
-        return "Invalid 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
-
-# --- UI ---
+        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 ===
 row_choices = [make_row_label(i) for i in range(len(df))]
 
 with gr.Blocks(css="""
-/* Clean professional styling */
+/* 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: 20px; border-radius: 10px; box-shadow: 0 4px 16px rgba(0,0,0,0.05); }
-h1 { margin-bottom: 10px; }
+.card { background: white; padding: 22px; border-radius: 10px; 
+        box-shadow: 0 4px 14px rgba(0,0,0,0.05); }
+h1 { margin-bottom: 12px; }
 """) 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("")
-
-    # When row changes
+    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=on_row_select,
+        fn=show_row,
         inputs=[row_dropdown],
-        outputs=[row_info],
+        outputs=[row_display]
     )
 
-    # When Predict is clicked
     predict_button.click(
-        fn=on_predict,
+        fn=predict_yield,
         inputs=[row_dropdown],
-        outputs=[status, features_md, result_md]
+        outputs=[status, features_display, result_display]
     )
 
+# === Launch ===
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)