Create app.py

app.py

@@ -0,0 +1,51 @@
+import streamlit as st
+import pandas as pd
+
+@st.cache_data
+def load_aisc_data():
+    df = pd.read_csv('AISC_Shape_Database.csv')
+    df.columns = df.columns.str.strip()
+    df['Designation'] = df['Designation'].str.upper().str.strip()
+    df.set_index('Designation', inplace=True)
+    return df
+
+def lbft_to_kgm(lb_per_ft):
+    return lb_per_ft * 1.48816
+
+def get_weight_for_section(section, aisc_df):
+    section = section.upper().strip()
+    if section in aisc_df.index:
+        return lbft_to_kgm(aisc_df.loc[section, 'W'])
+    else:
+        return None
+
+st.title("Steel Weight Estimator Using AISC Data")
+
+price_per_kg = st.number_input("Price per kg (e.g. 150)", min_value=0.0, format="%.2f")
+
+uploaded_file = st.file_uploader("Upload CSV with columns: Section, Length (meters)", type=["csv"])
+
+if uploaded_file:
+    user_df = pd.read_csv(uploaded_file)
+    user_df.columns = user_df.columns.str.strip()
+    user_df['Section'] = user_df['Section'].str.upper().str.strip()
+
+    weights = []
+    aisc_data = load_aisc_data()
+    for sec in user_df['Section']:
+        w = get_weight_for_section(sec, aisc_data)
+        weights.append(w)
+
+    user_df['Weight (kg/m)'] = weights
+    missing = user_df[user_df['Weight (kg/m)'].isna()]
+
+    if not missing.empty:
+        st.error(f"Missing sections in database: {', '.join(missing['Section'].unique())}")
+    else:
+        user_df['Total Weight (kg)'] = user_df['Weight (kg/m)'] * user_df['Length']
+        total_weight = user_df['Total Weight (kg)'].sum()
+        total_price = total_weight * price_per_kg
+
+        st.write(user_df[['Section', 'Length', 'Weight (kg/m)', 'Total Weight (kg)']])
+        st.write(f"Total Weight: {total_weight:.2f} kg")
+        st.write(f"Total Price: {total_price:.2f}")