Update app.py

app.py

@@ -2,14 +2,16 @@ import pandas as pd
 import numpy as np
 import gradio as gr
 from xgboost import XGBRegressor
-from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import LabelEncoder 
 
+# -------------------------------
+# Paths and Data
+# -------------------------------
 EXCEL_PATH = "microalgae_pot_experiment_corrected_doses.xlsx"
 MODEL_PATH = "EcoGrowAI_yield_model.json"
-
 df = pd.read_excel(EXCEL_PATH)
 
-# Encode categorical columns same as training
+# Apply encoding used during training
 for col in ["Crop", "Microalgae_Strain"]:
     le = LabelEncoder()
     df[col] = le.fit_transform(df[col])
@@ -17,37 +19,37 @@ 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
 model = XGBRegressor()
 model.load_model(MODEL_PATH)
 
-
+# -------------------------------
+# Helper functions
+# -------------------------------
 def make_row_label(i):
     return f"Row {i+1} (index={i})"
 
-
 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()
 
-
 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)
-
+    
     # Prediction
     pred = model.predict(X_row)[0]
     pred_rounded = float(np.round(pred, 3))
 
     # Ground truth
     actual = float(df.iloc[idx]["Yield_g_per_pot"])
-
-    # Error (always positive)
+    
+    # Error
     error = abs(pred_rounded - actual)
     error_rounded = float(np.round(error, 3))
 
@@ -62,34 +64,47 @@ def on_predict(row_label):
 
     return "✅ Prediction complete", features_md, result_md
 
-
+# -------------------------------
+# Dropdown options
+# -------------------------------
 row_choices = [make_row_label(i) for i in range(len(df))]
 
+# -------------------------------
+# Gradio App Layout
+# -------------------------------
 with gr.Blocks(css="""
 body {background:#f5f7fa;color:#0b1220;font-family:Inter,sans-serif;}
 .gradio-container{max-width:1200px;margin:20px auto;}
-.card{background:white;padding:20px;border-radius:10px;
-box-shadow:0 4px 16px rgba(0,0,0,0.05);}
+.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.")
-
-        with gr.Row():
-            with gr.Column(scale=1):
-                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")
-
-            with gr.Column(scale=1):
-                status = gr.Markdown("")
-                features_md = gr.Markdown("")
-                result_md = gr.Markdown("")
-
+    
+    with gr.Row():
+        # LEFT COLUMN: controls
+        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.")
+            
+            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("")
+
+        # RIGHT COLUMN: display features and results
+        with gr.Column(elem_classes="card", scale=2):
+            row_info = gr.Markdown("No row selected yet.")
+            features_md = gr.Markdown("")
+            result_md = gr.Markdown("")
+
+    # -------------------------------
+    # Event bindings
+    # -------------------------------
     row_dropdown.change(on_row_select, row_dropdown, row_info)
     predict_button.click(on_predict, row_dropdown, [status, features_md, result_md])
 
+# -------------------------------
+# Launch
+# -------------------------------
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)