Update app.py

app.py

@@ -13,6 +13,7 @@ linear_model = joblib.load(MODEL_PATH)
 df = pd.read_excel(EXCEL_PATH)
 df.columns = df.columns.str.strip()
 
+# Strains and rows
 strain_names = df['pea plant strain'].unique().tolist()
 row_options = ["None, Enter Manually"] + [str(i) for i in range(len(df))]
 
@@ -21,7 +22,7 @@ row_options = ["None, Enter Manually"] + [str(i) for i in range(len(df))]
 # -----------------------------
 def autofill_fields(row_index):
     if row_index == "None, Enter Manually":
-        return [None]*11  # Dose, Soil N, P, K, pH + 6 optional yield metrics
+        return [None]*11  # Dose, Soil N, P, K, pH + 6 actual outputs
     row = df.iloc[int(row_index)]
     return (
         row['Dose (g/pot)'], row['Soil N (ppm)'], row['Soil P (ppm)'],
@@ -34,18 +35,17 @@ def autofill_fields(row_index):
 # 3️⃣ Prediction function
 # -----------------------------
 def predict_linear(strain, dose, soil_n, soil_p, soil_k, ph,
-                   chlorophyll, shoot_len, root_len, shoot_wt, root_wt, yield_gp,
-                   actual_yield=None):
+                   chlorophyll, shoot_len, root_len, shoot_wt, root_wt, yield_gp):
     logs = []
 
     # Validate required inputs
     required = [dose, soil_n, soil_p, soil_k, ph]
     if any(v is None for v in required):
         logs.append("[DEBUG] Missing numeric inputs!")
-        return "⚠️ Fill all inputs", "", "", "\n".join(logs)
+        return "⚠️ Fill all required inputs", "", "\n".join(logs)
     logs.append("[DEBUG] Inputs received.")
 
-    # Prepare DataFrame
+    # Prepare input DataFrame for prediction
     X_input = pd.DataFrame([{
         'pea plant strain': strain,
         'Dose (g/pot)': dose,
@@ -56,28 +56,27 @@ def predict_linear(strain, dose, soil_n, soil_p, soil_k, ph,
     }])
     logs.append(f"[DEBUG] Input DataFrame:\n{X_input}")
 
-    # Predict all 7 target metrics
+    # Predict all 7 targets
     y_pred = linear_model.predict(X_input)[0]
     logs.append(f"[DEBUG] Predicted values:\n{y_pred}")
 
-    # Optional actual_yield comparison
-    if actual_yield is not None:
-        try:
-            actual_val = float(actual_yield)
-            abs_error = np.abs(actual_val - y_pred[5])  # assuming Yield (g/pot) at index 5
-            logs.append(f"[DEBUG] Actual Yield: {actual_val}")
-            logs.append(f"[DEBUG] Absolute Error: {abs_error}")
-            return (f"**Predicted Metrics:** {y_pred.round(2)}",
-                    f"**Actual Yield:** {actual_val}",
-                    f"**Absolute Error:** {abs_error:.2f}",
-                    "\n".join(logs))
-        except:
-            logs.append("[DEBUG] Actual yield invalid.")
-
-    return (f"**Predicted Metrics:** {y_pred.round(2)}",
-            "Actual Yield: Not provided",
-            "Absolute Error: N/A",
-            "\n".join(logs))
+    # Compute absolute errors using autofilled actuals
+    actuals = [chlorophyll, shoot_len, root_len, shoot_wt, root_wt, yield_gp]
+    abs_errors = []
+    for pred_val, act_val in zip(y_pred[:6], actuals):
+        if act_val is not None:
+            abs_errors.append(round(abs(pred_val - act_val), 2))
+        else:
+            abs_errors.append("N/A")
+
+    # Format outputs
+    target_cols = ['Chlorophyll (SPAD)', 'Shoot Length (cm)', 'Root Length (cm)',
+                   'Shoot Wt (g)', 'Root Wt (g)', 'Yield (g/pot)', 'Relative Yield (%)']
+
+    pred_str = "\n".join([f"{c}: {round(v,2)}" for c,v in zip(target_cols, y_pred)])
+    abs_str = "\n".join([f"{c}: {e}" for c,e in zip(target_cols[:6], abs_errors)])
+
+    return pred_str, abs_str, "\n".join(logs)
 
 # -----------------------------
 # 4️⃣ Gradio Interface
@@ -98,7 +97,7 @@ with gr.Blocks(title="Linear Regression Plant Predictor") as demo:
             soil_k = gr.Number(label="Soil K (ppm)")
             ph = gr.Number(label="pH")
 
-            gr.Markdown("### Optional Yield Metrics")
+            gr.Markdown("### Autofilled Actual Metrics (from CSV)")
             chlorophyll = gr.Number(label="Chlorophyll (SPAD)")
             shoot_len = gr.Number(label="Shoot Length (cm)")
             root_len = gr.Number(label="Root Length (cm)")
@@ -106,13 +105,11 @@ with gr.Blocks(title="Linear Regression Plant Predictor") as demo:
             root_wt = gr.Number(label="Root Wt (g)")
             yield_gp = gr.Number(label="Yield (g/pot)")
 
-            actual_yield = gr.Number(label="Actual Yield (g/pot)")
             predict_btn = gr.Button("Predict", variant="primary")
 
         with gr.Column(scale=1):
             gr.Markdown("### Inference Result")
             pred_box = gr.Markdown("Awaiting prediction...")
-            actual_box = gr.Markdown("")
             abs_box = gr.Markdown("")
             log_box = gr.Textbox(label="Debug Logs", lines=15)
 
@@ -125,9 +122,8 @@ with gr.Blocks(title="Linear Regression Plant Predictor") as demo:
     # Prediction callback
     predict_btn.click(fn=predict_linear,
                       inputs=[strain_input, dose, soil_n, soil_p, soil_k, ph,
-                              chlorophyll, shoot_len, root_len, shoot_wt, root_wt, yield_gp,
-                              actual_yield],
-                      outputs=[pred_box, actual_box, abs_box, log_box])
+                              chlorophyll, shoot_len, root_len, shoot_wt, root_wt, yield_gp],
+                      outputs=[pred_box, abs_box, log_box])
 
 # -----------------------------
 # 5️⃣ Launch