import streamlit as st import pandas as pd import pickle # Load the trained models with open('best_model.pkl', 'rb') as model_file: input_weight_model = pickle.load(model_file) with open('inspection_model.pkl', 'rb') as model_file: machining_model = pickle.load(model_file) with open('machining_model.pkl', 'rb') as model_file: inspection_model = pickle.load(model_file) # Final landed cost based on grade type final_landed_cost = { '1 MT XX (25-95 dia)': 103, '1 MT XX (100-210 dia)': 113, '1 MT YY (25-95 dia)': 160, '1 MT YY (100-125 dia)': 173, '1 MT XY (25-95 dia))': 106, '1 MT 8319 (100-210 dia)':116, '1 MT 8319':104 } # Function to calculate raw material cost def calculate_raw_material_cost(process_type, input_weight, grade_type): if process_type == 0: # 0 represents casting return 0 elif process_type == 1: # 1 represents forging return input_weight * final_landed_cost[grade_type] # Function to calculate process cost def calculate_process_cost(process_type, input_weight): if process_type == 0: # 0 represents casting return (input_weight * (120.57788 / 1000)*1000) elif process_type == 1: # 1 represents forging return input_weight * 30 # Streamlit interface st.title("EX-Works Calculator") # User input form with st.form("input_form"): part_no = st.number_input("Part No", min_value=1, step=1) process_type = st.selectbox("Process Type", options=[0,1]) part_od = st.number_input("Part Od", min_value=0.0, step=0.1) part_id = st.number_input("Part ID", min_value=0.0, step=0.1) part_width = st.number_input("Part Width", min_value=0, step=1) finish_wt = st.number_input("Finish Wt", min_value=0.0, step=0.1) grade_type = st.selectbox("Grade Type", options=list(final_landed_cost.keys())) submitted = st.form_submit_button("Calculate") if submitted: # Prepare the input data for prediction input_data = pd.DataFrame({ 'Process type': [process_type], 'Part Od': [part_od], 'Part ID': [part_id], 'Part Width': [part_width], 'Finish Wt': [finish_wt] }) # Predict the input weight predicted_input_weight = input_weight_model.predict(input_data)[0] # Calculate raw material cost raw_material_cost = calculate_raw_material_cost(process_type, predicted_input_weight, grade_type) # Calculate process cost process_cost = calculate_process_cost(process_type, predicted_input_weight) # Prepare the data for machining time prediction machining_data = pd.DataFrame({ 'Process type': [process_type], 'Part Od': [part_od], 'Part ID': [part_id], 'Part Width': [part_width], 'Finish Wt': [finish_wt], 'Input Weight': [predicted_input_weight], 'Raw material cost': [raw_material_cost], 'Process cost': [process_cost] }) # Predict the machining time predicted_machining_time = machining_model.predict(machining_data)[0] # Calculate machining cost machining_cost = predicted_machining_time * 375.71 # Calculate scrap recovery scrap_recovery = (predicted_input_weight - finish_wt) * 11.5 # Prepare the data for inspection time prediction inspection_data = pd.DataFrame({ 'Process type': [process_type], 'Part Od': [part_od], 'Part ID': [part_id], 'Part Width': [part_width], 'Finish Wt': [finish_wt], 'Input Weight': [predicted_input_weight], 'Raw material cost': [raw_material_cost], 'Process cost': [process_cost], 'Machining Time': [predicted_machining_time], 'Machining cost ': [machining_cost], }) # Predict the inspection time predicted_inspection_time = inspection_model.predict(inspection_data)[0] # Calculate inspection cost inspection_cost = predicted_inspection_time * 435.43 # Calculate total Mg cost total_mg_cost = raw_material_cost + process_cost + machining_cost + inspection_cost - scrap_recovery # Calculate rejection on manufacturing cost rejection_on_manufacturing_cost = total_mg_cost * 0.003 # Calculate 'Oiling, Inspection' cost oiling_inspection_cost = total_mg_cost * 0.005 # Calculate 'Transport and packing (BIN + 80 micron bag)' cost transport_packing_cost = total_mg_cost * 0.01 # Calculate 'Overheads & Profit on Material' overheads_profit_material = raw_material_cost * 0.003 # Calculate 'Overheads & Profit on Conversion' overheads_profit_conversion = (total_mg_cost - raw_material_cost) * 0.07 # Calculate 'ICC' icc = total_mg_cost * 0.01 # Calculate 'Ex-works' ex_works = total_mg_cost + rejection_on_manufacturing_cost + oiling_inspection_cost + \ transport_packing_cost + overheads_profit_material + overheads_profit_conversion # Create DataFrame to display the results data = { 'Part No': [part_no], 'Process type': ['casting' if process_type == 0 else 'forging'], 'Part Od': [part_od], 'Part ID': [part_id], 'Part Width': [part_width], 'Finish Wt': [finish_wt], 'Predicted Input Weight': [predicted_input_weight], 'Grade type': [grade_type], 'Raw material cost': [raw_material_cost], 'Process cost': [process_cost], 'Predicted Machining Time': [predicted_machining_time], 'Machining Cost': [machining_cost], 'Scrap Recovery': [scrap_recovery], 'Predicted Inspection Time': [predicted_inspection_time], 'Inspection Cost': [inspection_cost], 'Total Mg Cost': [total_mg_cost], 'Rejection on Manufacturing cost': [rejection_on_manufacturing_cost], 'Oiling, Inspectio': [oiling_inspection_cost], # Ensure the column name remains the same 'Transport and packing( BIN + 80 micron bag)': [transport_packing_cost], 'Overheads & Profit on Material': [overheads_profit_material], 'Overheads & Profit on Conversion': [overheads_profit_conversion], 'ICC': [icc], 'Ex-works': [ex_works] } df = pd.DataFrame(data) st.write("Input Data, Predicted Input Weight, and Calculated Costs:") st.dataframe(df)