Update app.py

app.py

@@ -1,207 +1,133 @@
-import gradio as gr 
-import pandas as pd 
-import numpy as np 
-from xgboost import XGBRegressor 
-import joblib 
-import os 
-import logging 
+import gradio as gr
+import pandas as pd
+import numpy as np
+import joblib
 
 # -----------------------------
-# 1️⃣ File paths
+# 1️⃣ Load model and data
 # -----------------------------
-MODEL_PATH = "xgb_model_corn_final.json"
-SCALER_PATH = "minmax_scaler.pkl"
-EXCEL_PATH = "microalgae_pot_experiment_corrected_doses.xlsx"
+MODEL_PATH = "linear_model.pkl"
+EXCEL_PATH = "excel sheet of plant 2.xlsx"
 
-# Check files exist
-for path in [MODEL_PATH, SCALER_PATH, EXCEL_PATH]:
-    if not os.path.exists(path):
-        raise FileNotFoundError(f"File not found: {path}")
-
-# -----------------------------
-# 2️⃣ Load model, scaler, and data
-# -----------------------------
-xgb_model = XGBRegressor()
-xgb_model.load_model(MODEL_PATH)
-scaler = joblib.load(SCALER_PATH)
+linear_model = joblib.load(MODEL_PATH)
 df = pd.read_excel(EXCEL_PATH)
+df.columns = df.columns.str.strip()
 
-# Only one strain exists
-strain_names = df['Microalgae_Strain'].unique().tolist()
-single_strain_value = 0  # training used LabelEncoder -> 0
-
-# ==================== New Integration ====================
-# Create a list of row indices for dropdown
+strain_names = df['pea plant strain'].unique().tolist()
 row_options = ["None, Enter Manually"] + [str(i) for i in range(len(df))]
 
-# Function to autofill input fields from selected row
+# -----------------------------
+# 2️⃣ Autofill function
+# -----------------------------
 def autofill_fields(row_index):
     if row_index == "None, Enter Manually":
-        # Reset all fields if 'None' selected UPDATE
-        return (None, None, None, None, None, None, None, None, None, None, None)
+        return [None]*11  # Dose, Soil N, P, K, pH + 6 optional yield metrics
     row = df.iloc[int(row_index)]
     return (
-        row['Soil_N_ppm'], row['Soil_P_ppm'], row['Soil_K_ppm'],
-        row['Soil_EC_dS_m'], row['Soil_Moisture_%'], row['Chlorophyll_SPAD'],
-        row['Shoot_Length_cm'], row['Root_Length_cm'], row['Yield_g_per_pot'],
-        row['Relative_Yield_%'], row['Dose_g_per_pot'] if 'Dose_g_per_pot' in row else None
+        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
 # -----------------------------
-def predict_dose(crop, microalgae_strain, soil_n, soil_p, soil_k, soil_ec, soil_moisture,
-                 chlorophyll, shoot_length, root_length, yield_g, relative_yield,
-                 actual_dose):
-    logs = []  # Capture all debug info
-
-    # 1️⃣ Validate inputs
-    required = [soil_n, soil_p, soil_k, soil_ec, soil_moisture,
-                chlorophyll, shoot_length, root_length, yield_g, relative_yield]
+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):
+    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 "⚠️ Please fill all numeric input fields.", None, None, "\n".join(logs)
-    logs.append("[DEBUG] All inputs received.")
-
-    # 2️⃣ Encode strain
-    strain_encoded = single_strain_value
-    logs.append(f"[DEBUG] Using Microalgae_Strain encoded value: {strain_encoded}")
-
-    # 3️⃣ Create DataFrame
-    feature_cols = ['Soil_N_ppm','Soil_P_ppm','Soil_K_ppm','Soil_EC_dS_m',
-                    'Soil_Moisture_%','Chlorophyll_SPAD','Shoot_Length_cm',
-                    'Root_Length_cm','Yield_g_per_pot','Relative_Yield_%','Microalgae_Strain']
-    X_input_df = pd.DataFrame([[soil_n, soil_p, soil_k, soil_ec, soil_moisture,
-                                chlorophyll, shoot_length, root_length, yield_g,
-                                relative_yield, strain_encoded]],
-                              columns=feature_cols).astype(float)
-    logs.append(f"[DEBUG] Input DataFrame:\n{X_input_df}")
-
-    # 4️⃣ Scale
-    X_input_scaled = scaler.transform(X_input_df)
-    logs.append(f"[DEBUG] Scaled Input:\n{X_input_scaled}")
-
-    # 5️⃣ Predict
-    predicted_dose = xgb_model.predict(X_input_scaled)[0]
-    logs.append(f"[DEBUG] Predicted dose (raw): {predicted_dose}")
-
-    # # 6️⃣ Compute error
-    # if actual_dose is not None:
-    #     abs_error = abs(predicted_dose - actual_dose)
-    #     logs.append(f"[DEBUG] Actual dose: {actual_dose}")
-    #     logs.append(f"[DEBUG] Absolute Error: {abs_error}")
-    #     return (f"**Predicted Dose:** {predicted_dose:.2f} g/pot",
-    #             f"**Actual Dose:** {actual_dose:.2f} g/pot",
-    #             f"**Absolute Error:** {abs_error:.2f} g/pot",
-    #             "\n".join(logs))
-    # else:
-    #     logs.append("[DEBUG] Actual dose not provided.")
-    #     return (f"**Predicted Dose:** {predicted_dose:.2f} g/pot",
-    #             " Actual Dose: Not provided",
-    #             " Absolute Error: N/A",
-    #             "\n".join(logs))
-
-    # 6️⃣ Compute error
-    if actual_dose is not None:
+        return "⚠️ Fill all inputs", "", "", "\n".join(logs)
+    logs.append("[DEBUG] Inputs received.")
+
+    # Prepare DataFrame
+    X_input = pd.DataFrame([{
+        'pea plant strain': strain,
+        'Dose (g/pot)': dose,
+        'Soil N (ppm)': soil_n,
+        'Soil P (ppm)': soil_p,
+        'Soil K (ppm)': soil_k,
+        'pH': ph
+    }])
+    logs.append(f"[DEBUG] Input DataFrame:\n{X_input}")
+
+    # Predict all 7 target metrics
+    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_dose_float = float(actual_dose)  # <-- convert string to float
-            abs_error = abs(predicted_dose - actual_dose_float)
-            logs.append(f"[DEBUG] Actual dose: {actual_dose_float}")
+            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 Dose:** {predicted_dose:.2f} g/pot",
-                f"**Actual Dose:** {actual_dose_float:.2f} g/pot",
-                f"**Absolute Error:** {abs_error:.2f} g/pot",
-                "\n".join(logs))
-        except ValueError:
-            logs.append("[DEBUG] Actual dose could not be converted to float")
-            return (f"**Predicted Dose:** {predicted_dose:.2f} g/pot",
-                    " Actual Dose: Invalid",
-                    " Absolute Error: N/A",
+            return (f"**Predicted Metrics:** {y_pred.round(2)}",
+                    f"**Actual Yield:** {actual_val}",
+                    f"**Absolute Error:** {abs_error:.2f}",
                     "\n".join(logs))
-    else:
-        logs.append("[DEBUG] Actual dose not provided.")
-        return (f"**Predicted Dose:** {predicted_dose:.2f} g/pot",
-                " Actual Dose: Not provided",
-                " Absolute Error: N/A",
-                "\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))
 
 # -----------------------------
 # 4️⃣ Gradio Interface
 # -----------------------------
-with gr.Blocks(
-    theme=gr.themes.Soft(primary_hue="green", secondary_hue="lime", neutral_hue="gray"),
-    title="Microalgae Dose Prediction"
-) as demo:
-    gr.Markdown(
-        """
-        <div style='text-align:center'>
-        <h1 style='color:#00b16a; margin-bottom:8px;'>Microalgae — Dose Prediction App</h1>
-        <p style='color:#555;'>Predict the optimal <b>microalgae dose (g/pot)</b> for Corn based on soil and growth parameters.</p>
-        </div>
-        <hr style='margin: 10px 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.Row():
         with gr.Column(scale=1):
-            gr.Markdown("### Crop & Microalgae Selection")
-            crop = gr.Dropdown(["Corn"], label="Select Crop", value="Corn")
-            strain = gr.Dropdown(strain_names, label="Microalgae Strain", value=strain_names[0])
-            row_selector = gr.Dropdown(row_options, label="Select Row (from XLSX)", value="0")
-            gr.Markdown("### Soil Parameters")
+            gr.Markdown("### Plant & Strain")
+            strain_input = gr.Dropdown(strain_names, label="Select Strain", value=strain_names[0])
+            row_selector = gr.Dropdown(row_options, label="Select Row", value="None, Enter Manually")
+
+            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)")
-            soil_ec = gr.Number(label="Soil EC (dS/m)")
-            soil_moisture = gr.Number(label="Soil Moisture (%)")
-            gr.Markdown("### Plant Growth Parameters")
+            ph = gr.Number(label="pH")
+
+            gr.Markdown("### Optional Yield Metrics")
             chlorophyll = gr.Number(label="Chlorophyll (SPAD)")
-            shoot_length = gr.Number(label="Shoot Length (cm)")
-            root_length = gr.Number(label="Root Length (cm)")
-            yield_g = gr.Number(label="Yield (g/pot)")
-            relative_yield = gr.Number(label="Relative Yield (%)")
-            gr.Markdown("### Actual Dose (optional)")
-            actual_dose = gr.Number(label="Actual Dose (g/pot)")
-            predict_btn = gr.Button("Predict Dose", variant="primary")
+            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)")
+
+            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)# <-- Add this
-            gr.Markdown(
-                """
-                ### Input Tips:
-                - Select Crop and the used microalgae strain.
-                - Select rows from XLSX or Enter manually
-                - Enter values for N, P, K, EC, and Moisture.
-                - Fill in Chlorophyll (SPAD), Shoot/Root length, Yield, and Relative Yield.
-                - (Optional) Add Actual Dose if you want to compare results.
-                - Click “Predict Dose” to get results.
-                ### Output Tips: 
-                <div>
-                <p style="margin:6px 0;"><strong>Predicted Dose:</strong> Model’s suggested microalgae dose (g/pot).</p>
-                <p style="margin:6px 0;"><strong>Absolute Error:</strong> Shows how close the prediction is to your actual dose.</p>
-                <p style="margin:6px 0;"><strong>Debug Logs:</strong> Displays detailed input and model processing info.</p>
-                </div>
-                """
-            )
-                # ✅ Autofill fields when row selected
-            row_selector.change(fn=autofill_fields,
-                inputs=[row_selector],
-                outputs=[soil_n, soil_p, soil_k, soil_ec, soil_moisture,
-                         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,
-                        chlorophyll, shoot_length, root_length, yield_g, relative_yield,
-                        actual_dose],
-                outputs=[pred_box, actual_box, abs_box, log_box]
-            )
+            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])
+
+    # 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])
 
 # -----------------------------
 # 5️⃣ Launch