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app.py

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+import gradio as gr
+import json
+from ortools.sat.python import cp_model
+
+def solve_task_order(requirements_text):
+    # Split and clean the input
+    requirements_text = requirements_text.lower()
+    lines = [l.strip() for l in requirements_text.strip().splitlines() if l.strip()]
+    if not lines:
+        return "No requirements specified."
+    
+    prerequisites = []
+    all_tasks = set()
+    for req in lines:
+        if "requires" not in req:
+            return f"Error: Each line must be like 'TaskA requires TaskB'. Bad line: {req}"
+        before, _, after = req.partition(" requires ")
+        before, after = before.strip(), after.strip()
+        prerequisites.append((before, after))
+        all_tasks |= {before, after}
+    task_list = sorted(all_tasks)
+    task_to_idx = {task: i for i, task in enumerate(task_list)}
+    n_tasks = len(task_list)
+    
+    # Model
+    model = cp_model.CpModel()
+    order = [model.NewIntVar(0, n_tasks - 1, f'order_{task}') for task in task_list]
+    model.AddAllDifferent(order)
+    constraints_satisfied = []
+    for before, after in prerequisites:
+        bidx = task_to_idx[before]
+        aidx = task_to_idx[after]
+        ok = model.NewBoolVar(f'prereq_{before}_after_{after}')
+        model.Add(order[aidx] < order[bidx]).OnlyEnforceIf(ok)
+        model.Add(order[aidx] >= order[bidx]).OnlyEnforceIf(ok.Not())
+        constraints_satisfied.append(ok)
+    model.Maximize(sum(constraints_satisfied))
+    solver = cp_model.CpSolver()
+    status = solver.Solve(model)
+    if status not in (cp_model.OPTIMAL, cp_model.FEASIBLE):
+        return "No feasible schedule could be found (cycle or conflict?)"
+    idx_to_task = {solver.Value(o): task for o, task in zip(order, task_list)}
+    schedule = [idx_to_task[i] for i in range(n_tasks)]
+    # Format output: numbered list, and json
+    display = "\n".join(f"{i+1}. {task}" for i, task in enumerate(schedule))
+    output_json = json.dumps(schedule, indent=2)
+    satisfied = int(solver.ObjectiveValue())
+    summary = f"Number of constraints satisfied: {satisfied} / {len(prerequisites)}"
+    return f"{display}\n\nJSON:\n{output_json}\n\n{summary}"
+
+example_input = """\
+Sleep requires dinner
+Sleep requires toothbrushing
+Dinner requires prep
+Dinner requires clean_dining_room
+Prep requires shopping
+Shopping requires money
+Clean_dining_room requires cleaning_time
+"""
+
+title = "Task Order Solver (Google OR-Tools Scheduler)"
+description = (
+    "Enter requirements like 'A requires B' (one per line). "
+    "The scheduler will order tasks so that as many requirements as possible are satisfied. "
+    "100% satisfaction is guaranteed if there are no cycles."
+)
+
+iface = gr.Interface(
+    fn=solve_task_order,
+    inputs=gr.Textbox(lines=12, label="Paste 'A requires B' constraints here"),
+    outputs=gr.Textbox(label="Task order (Numbered, JSON, Stats)"),
+    title=title,
+    description=description,
+    examples=[[example_input]],
+)
+
+if __name__ == "__main__":
+    iface.launch()