Update app.py

app.py

@@ -1,143 +1,151 @@
-import streamlit as st
-import requests
-import pandas as pd
-import matplotlib.pyplot as plt
-from plot_app import Plot
-plotter = Plot()
-from recipes import (
-    recipes_1_cotton_dark,
-    recipes_2_cotton_medium,
-    recipes_3_cotton_light,
-    recipes_4_polyester_dark,
-    recipes_5_pc_dark,
-)
-recipes_dict = {
-    ("cotton", "dark"): recipes_1_cotton_dark,
-    ("cotton", "medium"): recipes_2_cotton_medium,
-    ("cotton", "light"): recipes_3_cotton_light,
-    ("polyester", "dark"): recipes_4_polyester_dark,
-    ("polycotton", "dark"): recipes_5_pc_dark,
-}
-
-def calculate_outputs(mlr, recipes, capacity):
-    """Perform calculations for each machine."""
-    steam_consumption = []
-    moment_time = 0
-    time = []
-    initial_temp = []
-    final_temp = []
-    temp_grad = []
-    for i in recipes:
-        time.append(moment_time)
-        delta_t = recipes[i]["Duration"]
-        moment_time += delta_t
-
-        temp_diff = recipes[i]["final_temp"] - recipes[i]["init_temp"]
-        final_temp.append(recipes[i]["final_temp"])
-        initial_temp.append(recipes[i]["init_temp"])
-        temp_grad.append(recipes[i]["temp.grad"])
-        temp_diff = abs(temp_diff)
-        steam_use = (capacity * mlr * temp_diff) + (capacity * temp_diff)
-        steam_consumption.append(steam_use)
-    
-    return {
-        "Steam Consumption": steam_consumption,
-        "Time": time,
-        "init_Temp": initial_temp,
-        "final_Temp": final_temp,
-        "temp_grad": temp_grad
-    }
-
-def calculate_peak_avg_load(machine_results):
-    """Calculate peak and average steam load for all machines."""
-    combined_steam_load = {}
-    start_time_offset = 0
-    
-    for machine, results in machine_results.items():
-        for t, load in zip(results["Time"], results["Steam Consumption"]):
-            adjusted_time = t + start_time_offset
-            if adjusted_time in combined_steam_load:
-                combined_steam_load[adjusted_time] += load
-            else:
-                combined_steam_load[adjusted_time] = load
-        start_time_offset += 10  # Each machine starts 10 units after the previous one
-    
-    peak_load = max(combined_steam_load.values())
-    avg_load = sum(combined_steam_load.values()) / len(combined_steam_load)
-    
-    return peak_load, avg_load
-
-def convert_df_to_csv(df):
-    return df.to_csv(index=False).encode('utf-8')
-
-st.title("Dyeing Machine Load & Steam Calculator")
-
-machine_type = st.selectbox("Select Type of Machine", ['SoftFlow'])
-num_machines = st.number_input("Number of Dyeing Machines", min_value=1, value=5)
-mlr = st.number_input("Enter the MLR for the process", min_value=2, value=6)
-machine_capacities = {}
-
-st.write("### Enter Machine Capacities")
-for i in range(1, num_machines + 1):
-    capacity = st.number_input(f"Capacity of Machine {i} (kg)", min_value=1, value=100)
-    machine_capacities[f"{machine_type} {i}"] = capacity
-
-toggle_fetch = st.checkbox("Take pre-built Recipe")
-if toggle_fetch:
-    fabric = st.radio("Choose Fabric Type", ["Cotton", "Polyester", "PolyCotton"], index=0)
-    shade = st.radio("Choose your Shade", ["Dark", "Medium", "Light", "White"])
-    fabric_type = fabric.strip().lower()
-    shade = shade.strip().lower()
-    recipes = recipes_dict.get((fabric_type, shade), {})
-else:
-    recipes = {}
-
-st.write("### Results")
-if "current_machine_index" not in st.session_state:
-    st.session_state["current_machine_index"] = 0
-
-machine_names = list(machine_capacities.keys())
-machine_results = {}
-for machine in machine_names:
-    machine_results[machine] = calculate_outputs(mlr, recipes, machine_capacities[machine])
-
-peak_load, avg_load = calculate_peak_avg_load(machine_results)
-
-if st.button("Calculate Summary"):
-    st.markdown(
-        f"""
-        <div style='padding:10px; border-radius:10px; background-color:#05f5f5;'>
-            <h3 style='color:black;'>Summary</h3>
-            <p style='color:black;'><strong>Peak Steam Load:</strong> {peak_load} units</p>
-            <p style='color:black;'><strong>Average Steam Load:</strong> {avg_load} units</p>
-        </div>
-        """,
-        unsafe_allow_html=True,
-    )
-
-col1, col2 = st.columns(2)
-with col1:
-    if st.button("Previous Machine"):
-        st.session_state["current_machine_index"] = (st.session_state["current_machine_index"] - 1) % len(machine_names)
-with col2:
-    if st.button("Next Machine"):
-        st.session_state["current_machine_index"] = (st.session_state["current_machine_index"] + 1) % len(machine_names)
-
-current_machine = machine_names[st.session_state["current_machine_index"]]
-st.write(f"### Calculations for {current_machine}")
-df_results = pd.DataFrame(machine_results[current_machine])
-st.table(df_results)
-
-csv = convert_df_to_csv(df_results)
-st.download_button("Download Table as CSV", data=csv, file_name=f"{current_machine}_results.csv", mime='text/csv')
-
-st.header("Steam Load Variation Over Time")
-if st.button("Plot Steam Load Variation"):
-    fig1 = plotter.plot_steam_load(machine_results[current_machine]['Time'], machine_results[current_machine]['Steam Consumption'])
-    st.pyplot(fig1)
-    
-st.header("Temperature Variation Over Time")
-if st.button("Plot Temperature Variation"):
-    fig2 = plotter.plot_temperature_curve(machine_results[current_machine]['Time'], machine_results[current_machine]['init_Temp'], machine_results[current_machine]['final_Temp'], machine_results[current_machine]['temp_grad'])
-    st.pyplot(fig2)
-
+import streamlit as st 
+import pandas as pd
+import matplotlib.pyplot as plt
+from plot_app import Plot
+plotter = Plot()
+from recipes import (
+    recipes_1_cotton_dark,
+    recipes_2_cotton_medium,
+    recipes_3_cotton_light,
+    recipes_4_polyester_dark,
+    recipes_5_pc_dark,
+    cotton_cotton_lycra
+)
+recipes_dict = {
+    ("cotton", "dark"): recipes_1_cotton_dark,
+    ("cotton", "medium"): recipes_2_cotton_medium,
+    ("cotton", "light"): recipes_3_cotton_light,
+    ("polyester", "dark"): recipes_4_polyester_dark,
+    ("polycotton", "dark"): recipes_5_pc_dark,
+    ("lycra","dark"): cotton_cotton_lycra
+}
+latent_heat  = 494
+    
+def calculate_outputs(mlr, recipes, capacity):
+    """Perform calculations for each machine."""
+    steam_consumption = []
+    moment_time = 0
+    time = []
+    initial_temp = []
+    final_temp = []
+    temp_grad = []
+    for i in recipes:
+        time.append(moment_time)
+        delta_t = recipes[i]["Duration"]
+        moment_time += delta_t
+
+        temp_diff = recipes[i]["final_temp"] - recipes[i]["init_temp"]
+        final_temp.append(recipes[i]["final_temp"])
+        initial_temp.append(recipes[i]["init_temp"])
+        temp_grad.append(recipes[i]["temp.grad"])
+        temp = recipes[i]["temp.grad"]
+        temp_diff = abs(temp_diff)
+        steam_use = (capacity * mlr * temp_diff*1) + (capacity * temp_diff*1)  # cp = 1
+        if temp != 0 :
+            steam_ = steam_use*60/temp
+        else:
+            steam_ = steam_use*60
+        steam_kg_hr = steam_/latent_heat
+        steam_consumption.append(round(steam_kg_hr,2))
+    
+    return {
+        "Steam Consumption": steam_consumption,
+        "Time": time,
+        "init_Temp": initial_temp,
+        "final_Temp": final_temp,
+        "temp_grad": temp_grad
+    }
+
+def calculate_peak_avg_load(machine_results):
+    """Calculate peak and average steam load for all machines."""
+    combined_steam_load = {}
+    start_time_offset = 0
+    
+    for machine, results in machine_results.items():
+        for t, load in zip(results["Time"], results["Steam Consumption"]):
+            adjusted_time = t + start_time_offset
+            if adjusted_time in combined_steam_load:
+                combined_steam_load[adjusted_time] += load
+            else:
+                combined_steam_load[adjusted_time] = load
+        start_time_offset += 10  # Each machine starts 10 units after the previous one
+    
+    peak_load = max(combined_steam_load.values())
+    avg_load = sum(combined_steam_load.values()) / len(combined_steam_load)
+    
+    return peak_load, avg_load
+
+def convert_df_to_csv(df):
+    return df.to_csv(index=False).encode('utf-8')
+
+st.title("Dyeing Machine Load & Steam Calculator")
+
+machine_type = st.selectbox("Select Type of Machine", ['SoftFlow'])
+num_machines = st.number_input("Number of Dyeing Machines", min_value=1, value=5)
+mlr = st.number_input("Enter the MLR for the process", min_value=2, value=6)
+machine_capacities = {}
+
+st.write("### Enter Machine Capacities")
+for i in range(1, num_machines + 1):
+    capacity = st.number_input(f"Capacity of Machine {i} (kg)", min_value=1, value=100)
+    machine_capacities[f"{machine_type} {i}"] = capacity
+
+toggle_fetch = st.checkbox("Take pre-built Recipe")
+if toggle_fetch:
+    fabric = st.radio("Choose Fabric Type", ["Cotton", "Polyester", "PolyCotton","Lycra"], index=0)
+    shade = st.radio("Choose your Shade", ["Dark", "Medium", "Light", "White"])
+    fabric_type = fabric.strip().lower()
+    shade = shade.strip().lower()
+    recipes = recipes_dict.get((fabric_type, shade), {})
+else:
+    recipes = {}
+
+st.write("### Results")
+if "current_machine_index" not in st.session_state:
+    st.session_state["current_machine_index"] = 0
+
+machine_names = list(machine_capacities.keys())
+machine_results = {}
+for machine in machine_names:
+    machine_results[machine] = calculate_outputs(mlr, recipes, machine_capacities[machine])
+
+peak_load, avg_load = calculate_peak_avg_load(machine_results)
+
+if st.button("Calculate Summary"):
+    st.markdown(
+        f"""
+        <div style='padding:10px; border-radius:10px; background-color:#05f5f5;'>
+            <h3 style='color:black;'>Summary</h3>
+            <p style='color:black;'><strong>Peak Steam Load:</strong> {peak_load} kg/hr</p>
+            <p style='color:black;'><strong>Average Steam Load:</strong> {avg_load} kg/hr</p>
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+
+col1, col2 = st.columns(2)
+with col1:
+    if st.button("Previous Machine"):
+        st.session_state["current_machine_index"] = (st.session_state["current_machine_index"] - 1) % len(machine_names)
+with col2:
+    if st.button("Next Machine"):
+        st.session_state["current_machine_index"] = (st.session_state["current_machine_index"] + 1) % len(machine_names)
+
+current_machine = machine_names[st.session_state["current_machine_index"]]
+st.write(f"### Calculations for {current_machine}")
+df_results = pd.DataFrame(machine_results[current_machine])
+st.table(df_results)
+
+csv = convert_df_to_csv(df_results)
+st.download_button("Download Table as CSV", data=csv, file_name=f"{current_machine}_results.csv", mime='text/csv')
+
+st.header("Steam Load Variation Over Time")
+if st.button("Plot Steam Load Variation"):
+    fig1 = plotter.plot_steam_load(machine_results[current_machine]['Time'], machine_results[current_machine]['Steam Consumption'])
+    st.pyplot(fig1)
+    
+st.header("Temperature Variation Over Time")
+if st.button("Plot Temperature Variation"):
+    fig2 = plotter.plot_temperature_curve(machine_results[current_machine]['Time'], machine_results[current_machine]['init_Temp'], machine_results[current_machine]['final_Temp'], machine_results[current_machine]['temp_grad'])
+    st.pyplot(fig2)
+