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| import streamlit as st | |
| import joblib | |
| import pickle | |
| import pandas as pd | |
| import numpy as np | |
| from model_interface.hf_model_store import get_artifact_path | |
| def expense_forecasting_farmerp(): | |
| st.title("Expense Forecasting") | |
| model = joblib.load( | |
| get_artifact_path("13_Expense_forecasting(FarmERP)/Expense_model2.pkl" | |
| )) | |
| encoders = joblib.load( | |
| get_artifact_path("13_Expense_forecasting(FarmERP)/labels_expense.pkl" | |
| )) | |
| reference_df = joblib.load( | |
| get_artifact_path("13_Expense_forecasting(FarmERP)/expense_reference.pkl" | |
| )) | |
| # PAGE CONFIG | |
| # st.set_page_config( | |
| # page_title="Expense Forecasting App", | |
| # layout="wide" | |
| # ) | |
| # st.title("🌾 Expense & Harvest Forecasting - VegPro") | |
| # SIDEBAR INPUTS (WITH 'All' OPTION) | |
| st.sidebar.header("Enter Crop & Field Details") | |
| # 1. Site Name | |
| site_options = ["All"] + sorted(reference_df["Site_Name"].unique()) | |
| site_name = st.sidebar.selectbox("Site Name", site_options) | |
| if site_name != "All": | |
| site_df = reference_df[reference_df["Site_Name"] == site_name] | |
| else: | |
| site_df = reference_df.copy() | |
| # 2. Plot Name | |
| plot_options = ["All"] + sorted(site_df["Plot_Name"].unique()) | |
| plot_name = st.sidebar.selectbox("Plot Name", plot_options) | |
| if plot_name != "All": | |
| plot_df = site_df[site_df["Plot_Name"] == plot_name] | |
| else: | |
| plot_df = site_df.copy() | |
| # 3. Crop Name | |
| crop_options = ["All"] + sorted(plot_df["Crop_Name"].unique()) | |
| crop_name = st.sidebar.selectbox("Crop Name", crop_options) | |
| if crop_name != "All": | |
| filtered_df = plot_df[plot_df["Crop_Name"] == crop_name] | |
| else: | |
| filtered_df = plot_df.copy() | |
| # PREDICTION | |
| if st.sidebar.button("🔮 Predict"): | |
| if filtered_df.empty: | |
| st.warning("No records found for selected inputs.") | |
| st.stop() | |
| results = [] | |
| # Detect area column automatically | |
| area_col = [c for c in filtered_df.columns if "area" in c.lower()][0] | |
| for _, row in filtered_df.iterrows(): | |
| area_acres = float(row[area_col]) | |
| input_df = pd.DataFrame({ | |
| "Site_Name": [row["Site_Name"]], | |
| "Plot_Name": [row["Plot_Name"]], | |
| "SubPlot_Name": [row["SubPlot_Name"]], | |
| "Crop_Name": [row["Crop_Name"]], | |
| "Crop_Type": [row["Crop_Type"]], | |
| "Variety_Name": [row["Variety_Name"]], | |
| "Area_acres": [area_acres] | |
| }) | |
| # Encode categorical columns | |
| for col, encoder in encoders.items(): | |
| if input_df[col].iloc[0] not in encoder.classes_: | |
| st.error(f"❌ Unknown value in '{col}': {input_df[col].iloc[0]}") | |
| st.stop() | |
| input_df[col] = encoder.transform(input_df[col]) | |
| # Model prediction | |
| predictions = model.predict(input_df) | |
| total_expense = float(predictions[0][0]) | |
| total_harvested_qty = float(predictions[0][1]) | |
| results.append({ | |
| "Site Name": row["Site_Name"], | |
| "Plot Name": row["Plot_Name"], | |
| "Sub Plot Name": row["SubPlot_Name"], | |
| "Crop Name": row["Crop_Name"], | |
| "Crop Type": row["Crop_Type"], | |
| "Variety Name": row["Variety_Name"], | |
| "Area (Acres)": area_acres, | |
| "Total Estimated Production Qty(Kg)": 0, | |
| "Total Harvested Qty (Kg)": round(total_harvested_qty, 2), | |
| "Total Expenses (KES)": round(total_expense, 2), | |
| #"Cost Per Unit": 0 | |
| }) | |
| result_df = pd.DataFrame(results) | |
| # DISPLAY RESULTS | |
| st.subheader("📊 Forecast Results") | |
| st.dataframe(result_df, use_container_width=True) | |
| # SUMMARY METRICS | |
| st.subheader("📈 Overall Summary") | |
| col1, col2, col3 = st.columns(3) | |
| total_expenses = result_df["Total Expenses (KES)"].sum() | |
| total_qty = result_df["Total Harvested Qty (Kg)"].sum() | |
| col1.metric("Total Expenses (KES)", round(total_expenses, 2)) | |
| col2.metric("Total Harvested Qty (Kg)", round(total_qty, 2)) | |
| #col3.metric("Avg Cost Per Unit", 0) |