Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+import pickle
+import os
+from database import create_connection, initialize_database, insert_data, fetch_data
+
+# Database setup
+conn = create_connection('example.db')
+initialize_database(conn)
+
+# Load the trained models
+current_dir = os.path.dirname(os.path.abspath(__file__))
+
+with open(os.path.join(current_dir, 'best_model.pkl'), 'rb') as model_file:
+    input_weight_model = pickle.load(model_file)
+with open(os.path.join(current_dir, 'machining_model.pkl'), 'rb') as model_file:
+    machining_model = pickle.load(model_file)
+with open(os.path.join(current_dir, 'inspection_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")
+
+# Page navigation
+pages = ["Home", "Vendor Data", "Material Data", "RM Cost Data", "Supplier Data"]
+page = st.sidebar.selectbox("Select Page", pages)
+
+if page == "Home":
+    st.write("Welcome to the EX-Works Calculator application.")
+
+elif page == "Vendor Data":
+    st.header("Vendor Data")
+    vendor_name = st.text_input("Vendor Name")
+    vendor_type = st.text_input("Vendor Type")
+    gst_no =st.number_input("GST NO")
+    contact_person_name=st.text_input("CONTACT PERSON/NAME")
+    address=st.text_input("ADDRESS")
+    city=st.text_input("CITY")
+    panno=st.text_input("PAN NO")
+    
+    if st.button("Add Vendor"):
+        vendor_data = pd.DataFrame({'vendor_name': [vendor_name], 'vendor_type': [vendor_type], 'GST_NO': [gst_no], 'Contact_person_name': [contact_person_name], 'address': [address], 'city': [city], 'pan_no':[panno]})
+        insert_data(conn, 'vendor_data', vendor_data)
+        st.success("Vendor data added successfully")
+
+elif page == "Material Data":
+    st.header("Material Data")
+    part_id = st.number_input("Part ID")
+    part_no = st.number_input("Part Number")
+    scf = st.selectbox("SCF", options=[0, 1])
+    process_type = st.selectbox("Process Type", options=[0, 1])
+    part_od = st.number_input("Part Outer Dimension")
+    part_width = st.number_input("Part Width")
+    part_inner_dimension = st.number_input("Part Inner Dimension")
+    material_spec = st.selectbox("Material Specification", options=[0, 1])
+    finish_wt = st.number_input("Finish Weight")
+    green_drg_no = st.selectbox("Green DRG Number", options=[0, 1])
+    
+    if st.button("Add Material"):
+        material_data = pd.DataFrame({'Part_id': [part_id],'part_no': [part_no], 'scf': [scf],'process_type': [process_type],'part_od': [part_od],'part_width': [part_width],'part_inner_dimension': [part_inner_dimension],'material_specification': [material_spec],'finish_wt': [finish_wt],'green_drg_no': [green_drg_no]})
+        insert_data(conn, 'material_data', material_data)
+        st.success("Material data added successfully")
+
+
+elif page == "RM Cost Data":
+    st.header("RM Cost Data")
+    rm_type = st.text_input("RM Type")
+    rm_cost = st.number_input("RM Cost", min_value=0.0, step=0.01)
+    vendor_id = st.number_input("Vendor ID", min_value=1, step=1)
+    
+    if st.button("Add RM Cost Data"):
+        rm_cost_data = pd.DataFrame({'rm_type': [rm_type], 'rm_cost': [rm_cost], 'vendor_id': [vendor_id]})
+        insert_data(conn, 'rm_cost_data', rm_cost_data)
+        st.success("RM cost data added successfully")
+
+elif page == "Supplier Data":
+    st.header("Supplier Data")
+    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()))
+    material_id = st.number_input("Material ID", min_value=1, step=1)
+    
+    if st.button("Calculate and Add Supplier Data"):
+        # 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],
+            'Scrap recovery': [scrap_recovery]
+        })
+
+        # Predict the inspection time
+        predicted_inspection_time = inspection_model.predict(inspection_data)[0]
+
+        # Calculate inspection cost
+        inspection_cost = predicted_inspection_time * 375.71
+        
+        # Calculate total mg cost
+        total_mg_cost = raw_material_cost + process_cost + machining_cost + scrap_recovery + inspection