add app

app.py

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+import streamlit as st
+import pandas as pd
+import io
+from pyomo.environ import ConcreteModel, Var, Objective, Constraint, SolverFactory, NonNegativeReals, RangeSet, Param, minimize, value, Reals,Set
+
+def get_output(df,df1,df2):
+  n = df['ID projet'].nunique()
+  task = df.groupby('ID projet').count()['Nom projet']
+  project =df.groupby('ID projet').count().index
+
+  J_sizes = {i:task[i-1] for i in range(1,n+1)}
+
+  Months = ['Janvier', 'Février', 'Mars', 'Avril', 'Mai', 'Juin', 'Juillet', 'Août', 'Septembre', 'Octobre', 'Novembre', 'Décembre']
+  months = 12  # Number of months in set M
+  H_data = {(i, j, month): df.loc[df['ID projet']==project[i-1]].loc[df.loc[df['ID projet']==project[i-1]].index[j-1],Months[month-1]] for i in range(1, n + 1) for j in range(1, J_sizes[i] + 1) for month in range(1, months + 1)}
+
+  df1.fillna(0, inplace=True)
+
+  h = df1['Ressource'].nunique()
+  A_data = {(i, j, k): int(df.loc[df['ID projet']==project[i-1]].loc[df.loc[df['ID projet']==project[i-1]].index[j-1], 'Equipe']  == df1.loc[df1.index[k-1],'Equipe']) for i in range(1, n + 1) for j in range(1, J_sizes[i] + 1) for k in range(1, h + 1)}
+  per= [0.08,0.08,0.09,0.09,0.08,0.09,0.07,0.07,0.09,0.09,0.09,0.08]
+  C_data = {(k, month): df1.loc[df1.index[k-1],'Capacité']*per[month-1] for k in range(1, h + 1) for month in range(1, months + 1)}
+
+  p_data = {i:df2.loc[df2.index[i-1],'Pond']  for i in range(1, n + 1)}
+
+    # Define model
+  model = ConcreteModel()
+
+  # Sets
+  model.I = RangeSet(1, n)
+  model.M = RangeSet(1, months)
+  model.K = RangeSet(1, h)
+  model.J = Set(model.I, initialize=lambda model, i: RangeSet(1, J_sizes[i]))
+
+  # Flatten J for use in parameter definition
+  flat_J = [(i, j) for i in model.I for j in model.J[i]]
+  flat_J_pairs = [(i, j, l) for i in model.I for j in model.J[i] for l in model.J[i] if j != l]
+
+  # Parameters
+  model.H = Param(flat_J, model.M, initialize=H_data)
+  model.A = Param(flat_J, model.K, initialize=A_data)
+  model.C = Param(model.K, model.M, initialize=C_data)
+  model.p = Param(model.I, initialize=p_data)
+
+  # Variables
+  model.x = Var(flat_J, model.K, model.M, domain=NonNegativeReals)
+
+  # Auxiliary variables for max(0, ...)
+  model.max_0_terms = Var(flat_J, model.M, domain=NonNegativeReals)
+  model.max_0_terms_2 = Var(flat_J_pairs, model.M, domain=NonNegativeReals)
+
+  # Objective function
+  def objective_rule(model):
+      return sum(model.p[i] * (
+          sum(model.max_0_terms[i, j, month] for j in model.J[i]) +
+          sum(model.max_0_terms_2[i, j, l, month] for j in model.J[i] for l in model.J[i] if l != j)
+      ) for i in model.I for month in model.M)
+  model.objective = Objective(rule=objective_rule, sense=minimize)
+
+  # Constraints to handle max(0, ...)
+  def max_0_term_constraint_1(model, i, j, month):
+      return model.max_0_terms[i, j, month] >= model.H[i, j, month] - sum(model.A[i, j, k] * model.x[i, j, k, month] for k in model.K)
+  model.max_0_term_constraint_1 = Constraint(flat_J, model.M, rule=max_0_term_constraint_1)
+
+  def max_0_term_constraint_2(model, i, j, month):
+      return model.max_0_terms[i, j, month] >= 0
+  model.max_0_term_constraint_2 = Constraint(flat_J, model.M, rule=max_0_term_constraint_2)
+
+  def max_0_term_2_constraint_1(model, i, j, l, month):
+      return model.max_0_terms_2[i, j, l, month] >= model.H[i, l, month] - sum(model.A[i, l, k] * model.x[i, l, k, month] for k in model.K)
+  model.max_0_term_2_constraint_1 = Constraint(flat_J_pairs, model.M, rule=max_0_term_2_constraint_1)
+
+  def max_0_term_2_constraint_2(model, i, j, l, month):
+      return model.max_0_terms_2[i, j, l, month] >= 0
+  model.max_0_term_2_constraint_2 = Constraint(flat_J_pairs, model.M, rule=max_0_term_2_constraint_2)
+
+  # Capacity constraint
+  def capacity_constraint(model, k, month):
+      return sum(model.x[i, j, k, month] for (i, j) in flat_J) <= model.C[k, month]
+  model.capacity_constraint = Constraint(model.K, model.M, rule=capacity_constraint)
+
+  # Solver
+  solver = SolverFactory('glpk')  # Example using GLPK
+  result = solver.solve(model, tee=True)
+
+  Months= ['Janvier', 'Février', 'Mars', 'Avril', 'Mai', 'Juin', 'Juillet', 'Août', 'Septembre', 'Octobre', 'Novembre', 'Décembre']
+  results = []
+  for (i, j) in flat_J:
+      for k in model.K:
+        result = {}
+        result['i']=project[i-1]
+        result['j']= j
+        result['k']=df1.loc[df1.index[k-1],'Ressource']
+        for month in model.M:
+                
+                result[Months[month-1]] = value(model.x[i, j, k, month])
+        results.append(result)
+
+
+  output_df = pd.DataFrame(results)
+  return output_df
+
+
+
+def main():
+    st.title("XLSX Upload and Download")
+
+    # File upload section
+    uploaded_file = st.file_uploader("Choose an XLSX file to upload", type="xlsx")
+
+    if uploaded_file is not None:
+        # Load the uploaded file into a Pandas DataFrame
+        df = pd.read_excel(uploaded_file, sheet_name='PMC1')
+        df1 = pd.read_excel(uploaded_file, sheet_name ='Base de ressource1')
+        df2 =  pd.read_excel(uploaded_file, sheet_name ='Priorisation')
+
+        df_out = get_output(df,df1,df2)
+        # Display the uploaded DataFrame
+        st.write("Estimation")
+        st.dataframe(df_out)
+
+        # Download section
+        excel_file = io.BytesIO()
+        df_out.to_excel(excel_file, index=False)
+
+        st.download_button(
+            label="Download XLSX",
+            data=excel_file.getvalue(),
+            file_name="downloaded_file.xlsx",
+            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+        )
+
+if __name__ == "__main__":
+    main()

dockerfile

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+# Start with a builder image
+FROM python:3.9.13-slim as builder
+
+# Copy only requirements.txt initially to leverage Docker cache
+COPY requirements.txt .
+RUN python3 -m pip install --upgrade pip
+RUN python3 -m pip install --no-cache-dir -r requirements.txt
+
+
+# sentence transformers deps
+# ----------------------
+
+
+
+# Set working directory
+WORKDIR /app
+# Copy the rest of the application from the current directory to /app inside the container
+COPY . .
+
+RUN sudo apt-get install -y glpk-utils
+# Expose port 80 to the outside world
+EXPOSE 8501
+# Command to run the Uvicorn server
+CMD ["streamlit", "run", "app.py"]

requirments.txt

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+streamlit
+openpyxl
+pyomo