Delete app (2).py

app (2).py

@@ -1,174 +0,0 @@
-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