Update app.py

app.py

@@ -19,19 +19,19 @@ row_options = ["None, Enter Manually"] + [str(i) for i in range(len(df))]
 
 #-------------- To be commented --------------------
 
-# # -----------------------------
-# # 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
@@ -58,9 +58,6 @@ def predict_linear(strain, dose, soil_n, soil_p, soil_k, ph,
     if any(v is None for v in required):
         logs.append("[DEBUG] Missing numeric inputs!")
         return pd.DataFrame(), "\n⚠️ Fill all required inputs", "\n".join(logs)
-    # if any(v is 0 for v in required):
-    #     logs.append("[DEBUG] Missing numeric inputs!")
-    #     return pd.DataFrame(), "\n⚠️ Fill all required inputs", "\n".join(logs)
     logs.append("[DEBUG] Inputs received.")
 
     # Prepare DataFrame for model
@@ -133,7 +130,7 @@ with gr.Blocks(
                 label="Select Strain",
                 value=strain_names[0]  # default: Tetradesmus nigardi
             )
-            # row_selector = gr.Dropdown(row_options, label="Select Row", value="None, Enter Manually")
+            row_selector = gr.Dropdown(row_options, label="Select Row", value="None, Enter Manually")
 
             gr.Markdown("### Input Parameters")
             dose = gr.Number(label="Dose (g/pot)",placeholder="Enter value")
@@ -142,13 +139,13 @@ with gr.Blocks(
             soil_k = gr.Number(label="Soil K (ppm)",placeholder="Enter value")
             ph = gr.Number(label="pH",placeholder="Enter value")
 
-            # 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)")
 
             # Hidden placeholders for metrics
             chlorophyll = gr.State(None)
@@ -196,14 +193,14 @@ with gr.Blocks(
                 """
             )
 
-    # Autofill callback
-    # row_selector.change(
-    #     fn=autofill_fields,
-    #     inputs=[row_selector],
-    #     outputs=[dose, soil_n, soil_p, soil_k, ph,
-    #              chlorophyll, shoot_len, root_len,
-    #              shoot_wt, root_wt, yield_gp]
-    # )
+    Autofill callback
+    row_selector.change(
+        fn=autofill_fields,
+        inputs=[row_selector],
+        outputs=[dose, soil_n, soil_p, soil_k, ph,
+                 chlorophyll, shoot_len, root_len,
+                 shoot_wt, root_wt, yield_gp]
+    )
 
     # Prediction callback
     predict_btn.click(