Update app.py

app.py

@@ -17,19 +17,19 @@ df.columns = df.columns.str.strip()
 strain_names = df['pea plant strain'].unique().tolist()
 row_options = ["None, Enter Manually"] + [str(i) for i in range(len(df))]
 
-# -----------------------------
-# 2️⃣ Autofill function
-# -----------------------------
-def autofill_fields(row_index):
-    if row_index == "None, Enter Manually":
-        return [None]*11
-    row = df.iloc[int(row_index)]
-    return (
-        row['Dose (g/pot)'], row['Soil N (ppm)'], row['Soil P (ppm)'],
-        row['Soil K (ppm)'], row['pH'], row['Chlorophyll (SPAD)'],
-        row['Shoot Length (cm)'], row['Root Length (cm)'], row['Shoot Wt (g)'],
-        row['Root Wt (g)'], row['Yield (g/pot)']
-    )
+# # -----------------------------
+# # 2️⃣ Autofill function
+# # -----------------------------
+# def autofill_fields(row_index):
+#     if row_index == "None, Enter Manually":
+#         return [None]*11
+#     row = df.iloc[int(row_index)]
+#     return (
+#         row['Dose (g/pot)'], row['Soil N (ppm)'], row['Soil P (ppm)'],
+#         row['Soil K (ppm)'], row['pH'], row['Chlorophyll (SPAD)'],
+#         row['Shoot Length (cm)'], row['Root Length (cm)'], row['Shoot Wt (g)'],
+#         row['Root Wt (g)'], row['Yield (g/pot)']
+#     )
 
 # -----------------------------
 # 3️⃣ Prediction function
@@ -71,9 +71,9 @@ def predict_linear(strain, dose, soil_n, soil_p, soil_k, ph,
     # Build table DataFrame
     data = {
         "Output Metric": target_cols,
-        "Actual Value": actuals + ["N/A"],
+        # "Actual Value": actuals + ["N/A"],
         "Predicted Value": [round(v, 2) for v in y_pred],
-        "Absolute Error": abs_errors + ["N/A"]
+        # "Absolute Error": abs_errors + ["N/A"]
     }
     result_df = pd.DataFrame(data)
 
@@ -123,13 +123,13 @@ with gr.Blocks(
             soil_k = gr.Number(label="Soil K (ppm)")
             ph = gr.Number(label="pH")
 
-            gr.Markdown("### Autofilled Actual Metrics (from Excel)")
-            chlorophyll = gr.Number(label="Chlorophyll (SPAD)")
-            shoot_len = gr.Number(label="Shoot Length (cm)")
-            root_len = gr.Number(label="Root Length (cm)")
-            shoot_wt = gr.Number(label="Shoot Wt (g)")
-            root_wt = gr.Number(label="Root Wt (g)")
-            yield_gp = gr.Number(label="Yield (g/pot)")
+            # gr.Markdown("### Autofilled Actual Metrics (from Excel)")
+            # chlorophyll = gr.Number(label="Chlorophyll (SPAD)")
+            # shoot_len = gr.Number(label="Shoot Length (cm)")
+            # root_len = gr.Number(label="Root Length (cm)")
+            # shoot_wt = gr.Number(label="Shoot Wt (g)")
+            # root_wt = gr.Number(label="Root Wt (g)")
+            # yield_gp = gr.Number(label="Yield (g/pot)")
 
             with gr.Row():
                 predict_btn = gr.Button(" Predict", variant="primary")
@@ -148,7 +148,6 @@ with gr.Blocks(
                 """
                 ### Input Tips:
                 - Select the **pea plant strain** you want to analyze.
-                - You can either **select a row** from the Excel sheet to autofill values, or **enter values manually**.
                 - Provide all essential input parameters: **Dose (g/pot), Soil N, Soil P, Soil K, and pH**.
                 - (Optional) Enter actual observed values for:
                     - **Chlorophyll (SPAD)**
@@ -170,7 +169,6 @@ with gr.Blocks(
                 """
             )
 
-
     # Autofill callback
     row_selector.change(
         fn=autofill_fields,