Update app.py

app.py

@@ -32,10 +32,13 @@ single_strain_value = 0  # training used LabelEncoder -> 0
 
 # ==================== New Integration ====================
 # Create a list of row indices for dropdown
-row_options = [str(i) for i in range(len(df))]
+row_options = ["None, Enter manually"] + [str(i) for i in range(len(df))]
 
 # Function to autofill input fields from selected row
 def autofill_fields(row_index):
+    if row_index == "None":
+        # Reset all fields if 'None' selected UPDATE
+        return (None, None, None, None, None, None, None, None, None, None, None)
     row = df.iloc[int(row_index)]
     return (
         row['Soil_N_ppm'], row['Soil_P_ppm'], row['Soil_K_ppm'],
@@ -44,6 +47,7 @@ def autofill_fields(row_index):
         row['Relative_Yield_%'], row['Dose_g_per_pot'] if 'Dose_g_per_pot' in row else None
     )
 
+
 # -----------------------------
 # 3️⃣ Prediction function
 # -----------------------------
@@ -189,6 +193,7 @@ with gr.Blocks(
                          chlorophyll, shoot_length, root_length, yield_g,
                          relative_yield, actual_dose]
                        )
+
             predict_btn.click(
                 fn=predict_dose,
                 inputs=[crop, strain, soil_n, soil_p, soil_k, soil_ec, soil_moisture,