Update app.py

app.py

@@ -22,7 +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 actual outputs
+        return [None]*11
     row = df.iloc[int(row_index)]
     return (
         row['Dose (g/pot)'], row['Soil N (ppm)'], row['Soil P (ppm)'],
@@ -38,14 +38,13 @@ def predict_linear(strain, dose, soil_n, soil_p, soil_k, ph,
                    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 required inputs", "", "\n".join(logs)
+        return pd.DataFrame(), "\n⚠️ Fill all required inputs", "\n".join(logs)
     logs.append("[DEBUG] Inputs received.")
 
-    # Prepare input DataFrame for prediction
+    # Prepare DataFrame for model
     X_input = pd.DataFrame([{
         'pea plant strain': strain,
         'Dose (g/pot)': dose,
@@ -56,58 +55,67 @@ def predict_linear(strain, dose, soil_n, soil_p, soil_k, ph,
     }])
     logs.append(f"[DEBUG] Input DataFrame:\n{X_input}")
 
-    # Predict all 7 targets
     y_pred = linear_model.predict(X_input)[0]
     logs.append(f"[DEBUG] Predicted values:\n{y_pred}")
 
-    # Compute absolute errors using autofilled actuals
+    # Actuals and errors
     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
+    abs_errors = [
+        round(abs(p - a), 2) if a is not None else "N/A"
+        for p, a in zip(y_pred[:6], actuals)
+    ]
+
     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)])
+    # Build table DataFrame
+    data = {
+        "Output Metric": target_cols,
+        "Actual Value": actuals + ["N/A"],
+        "Predicted Value": [round(v, 2) for v in y_pred],
+        "Absolute Error": abs_errors + ["N/A"]
+    }
+    result_df = pd.DataFrame(data)
 
-    return pred_str, abs_str, "\n".join(logs)
+    return result_df, "✅ Prediction complete!", "\n".join(logs)
 
 # -----------------------------
-# 4️⃣ Gradio Interface
+# 4️⃣ Gradio Interface (Green Theme)
 # -----------------------------
 invalid_strains = ["Strains", "strain1", "strain2", ""]
 valid_strain = next(
-    (s for s in strain_names if pd.notna(s) and s not in invalid_strains), 
-    strain_names[0]  # fallback if none found
+    (s for s in strain_names if pd.notna(s) and s not in invalid_strains),
+    strain_names[0]
 )
 
-with gr.Blocks(title="Linear Regression Plant Predictor") as demo:
-    gr.Markdown("<h1 style='text-align:center'>Linear Regression — Plant Yield Predictor</h1>")
+with gr.Blocks(
+    title="Linear Regression Plant Predictor",
+    theme=gr.themes.Soft(
+        primary_hue="green",
+        secondary_hue="green",
+        neutral_hue="green"
+    )
+) as demo:
+    gr.Markdown("<h1 style='text-align:center; color:#2E8B57;'>🌿 Linear Regression — Plant Yield Predictor 🌿</h1>")
 
     with gr.Row():
         with gr.Column(scale=1):
-            gr.Markdown("### Plant & Strain")
+            gr.Markdown("### 🌱 Plant & Strain")
             strain_input = gr.Dropdown(
                 strain_names,
                 label="Select Strain",
-                value=valid_strain  # first valid, non-NaN, non-invalid strain
+                value=valid_strain
             )
             row_selector = gr.Dropdown(row_options, label="Select Row", value="None, Enter Manually")
 
-            gr.Markdown("### Input Parameters")
+            gr.Markdown("### 🌾 Input Parameters")
             dose = gr.Number(label="Dose (g/pot)")
             soil_n = gr.Number(label="Soil N (ppm)")
             soil_p = gr.Number(label="Soil P (ppm)")
             soil_k = gr.Number(label="Soil K (ppm)")
             ph = gr.Number(label="pH")
 
-            gr.Markdown("### Autofilled Actual Metrics (from CSV)")
+            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)")
@@ -115,25 +123,34 @@ 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)")
 
-            predict_btn = gr.Button("Predict", variant="primary")
+            predict_btn = gr.Button("🌿 Predict", variant="primary")
 
         with gr.Column(scale=1):
-            gr.Markdown("### Inference Result")
-            pred_box = gr.Markdown("Awaiting prediction...")
-            abs_box = gr.Markdown("")
+            gr.Markdown("### 🌼 Prediction Results Table")
+            result_table = gr.DataFrame(
+                headers=["Output Metric", "Actual Value", "Predicted Value", "Absolute Error"],
+                label="Results Comparison",
+                interactive=False
+            )
+            status_box = gr.Markdown("")
             log_box = gr.Textbox(label="Debug Logs", lines=15)
 
     # 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])
+    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(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],
-                      outputs=[pred_box, abs_box, log_box])
+    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],
+        outputs=[result_table, status_box, log_box]
+    )
 
 # -----------------------------
 # 5️⃣ Launch