app.py

import gradio as gr
import pandas as pd
import numpy as np
import joblib

# -----------------------------
# 1️⃣ Load model and data
# -----------------------------
MODEL_PATH = "linear_model.pkl"
EXCEL_PATH = "excel sheet of plant 2.xlsx"

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))]

#-------------- 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)']
#     )

# -----------------------------
# 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):
    logs = []
    # -----------------------------
    # 🧩 Convert textbox inputs to floats (or None if blank)
    # -----------------------------
    # def to_float(x):
    #     try:
    #         return float(x)
    #     except (TypeError, ValueError):
    #         return None

    # dose = to_float(dose)
    # soil_n = to_float(soil_n)
    # soil_p = to_float(soil_p)
    # soil_k = to_float(soil_k)
    # ph = to_float(ph)
    # # -----------------------------
    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 pd.DataFrame(), "\n⚠️ Fill all required inputs", "\n".join(logs)
    logs.append("[DEBUG] Inputs received.")

    # Prepare DataFrame for model
    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}")

    y_pred = linear_model.predict(X_input)[0]
    logs.append(f"[DEBUG] Predicted values:\n{y_pred}")

    # Actuals and errors
    actuals = [chlorophyll, shoot_len, root_len, shoot_wt, root_wt, yield_gp]
    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 (%)']

    # 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 result_df, "Prediction complete!", "\n".join(logs)
    
# -----------------------------
#  Clear Inputs Function
# -----------------------------
def clear_inputs():
    # Reset all input fields to None
    return [None] * 11  # same number of numeric input fields

# -----------------------------
# 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]
)

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>")

    strain_names = ["Tetradesmus nigardi", "Clostroprosis acicularis"]
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### Plant & Strain")
            strain_input = gr.Dropdown(
                strain_names,
                label="Select Strain",
                value=strain_names[0]  # default: Tetradesmus nigardi
            )
            # 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") # ,placeholder="Enter value"
            soil_n = gr.Number(label="Soil N (ppm)",placeholder="Enter value")
            soil_p = gr.Number(label="Soil P (ppm)",placeholder="Enter value")
            soil_k = gr.Number(label="Soil K (ppm)",placeholder="Enter value")
            ph = gr.Number(label="pH")

            # 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)
            shoot_len = gr.State(None)
            root_len = gr.State(None)
            shoot_wt = gr.State(None)
            root_wt = gr.State(None)
            yield_gp = gr.State(None)

            with gr.Row():
                predict_btn = gr.Button(" Predict", variant="primary")
                clear_btn = gr.Button("🧹 Clear Inputs", variant="secondary")

        with gr.Column(scale=1):
            gr.Markdown("### Prediction Results Table")
            result_table = gr.DataFrame(
                headers=["Output Metric", "Predicted Value"],      #"Actual Value", "Absolute Error"
                label="Results Comparison",
                interactive=False
            )
            status_box = gr.Markdown("")
            log_box = gr.Textbox(label="Debug Logs", lines=15)
            gr.Markdown(
                """
                ### Input Tips:
                - Select the **plant strain** you want to analyze.
                - Provide all essential input parameters: **Dose (g/pot), Soil N, Soil P, Soil K, and pH**.
                - Click **“Predict”** to generate the model’s predictions.

                ### Output Tips:
                <div>
                <p style="margin:6px 0;"><strong>Prediction Table:</strong> Displays model predictions for all plant growth metrics.</p>
                <p style="margin:6px 0;"><strong>Debug Logs:</strong> Provides detailed trace of model inputs and internal calculations.</p>
                </div>
                """
            )

    # #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],
        outputs=[result_table, status_box, log_box]
    )

    # Clear button callback
    clear_btn.click(
        fn=clear_inputs,
        inputs=[],
        outputs=[dose, soil_n, soil_p, soil_k, ph,
             chlorophyll, shoot_len, root_len,
             shoot_wt, root_wt, yield_gp]
    )


# -----------------------------
# 5️⃣ Launch
# -----------------------------
if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)