Update app.py

app.py

@@ -1,4 +1,3 @@
-# app.py
 import pandas as pd
 import numpy as np
 import gradio as gr
@@ -14,31 +13,32 @@ try:
 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_%']
 feature_cols = [c for c in df.columns if c not in drop_cols]
 
-# Load model
 model = XGBRegressor()
 model.load_model(MODEL_PATH)
 
-# --- Functions ---
+# --- Helpers ---
 def make_row_label(i):
     return f"Row {i+1} (index={i})"
 
+def parse_index(label):
+    return int(label.split("index=")[1].strip("    return int(label.split("index=")[1].strip(")"))
+
+# --- Callbacks ---
 def on_row_select(row_label):
     try:
-        idx = int(row_label.split("index=")[1].strip(")"))
+        idx = parse_index(row_label)
     except Exception:
-        return "Invalid row label.", gr.update(visible=False), None
+        return "Invalid row label."
 
     row_df = df.iloc[idx][feature_cols].to_frame().T
-    md = "### Selected Row (Input Features)\n\n" + row_df.T.to_markdown()
-    return md, gr.update(visible=True), idx
+    return "### Selected Row (Input Features)\n\n" + row_df.T.to_markdown()
 
 def on_predict(row_label):
     try:
-        idx = int(row_label.split("index=")[1].strip(")"))
+        idx = parse_index(row_label)
     except Exception:
         return "Invalid row label.", "", ""
 
@@ -55,19 +55,19 @@ def on_predict(row_label):
 row_choices = [make_row_label(i) for i in range(len(df))]
 
 with gr.Blocks(css="""
-/* Minimal, professional design */
+/* Minimal professional 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; }
 """) as demo:
-    
+
     with gr.Column(elem_classes="card"):
         gr.Markdown("# EcoGrowAI — Yield Prediction")
-        gr.Markdown("Select any experimental row below and get the predicted **Yield (g per pot)** based on the trained XGBoost model.")
+        gr.Markdown("Select any experimental row and predict **Yield (g per pot)** using the trained XGBoost model.")
         
         row_dropdown = gr.Dropdown(
-            label="Select row",
+            label="Select Row",
             choices=row_choices,
             value=row_choices[0],
             interactive=True
@@ -75,23 +75,24 @@ h1 { margin-bottom: 10px; }
 
         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("")
 
-    # Events
+    # When row changes
     row_dropdown.change(
         fn=on_row_select,
         inputs=[row_dropdown],
-        outputs=[row_info, predict_button, gr.State()]
+        outputs=[row_info],
     )
 
+    # When Predict is clicked
     predict_button.click(
         fn=on_predict,
         inputs=[row_dropdown],
         outputs=[status, features_md, result_md]
     )
 
-# --- Run ---
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)