Create app.py

app.py

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+import networkx as nx
+import gradio as gr
+
+def find_shortest_path(graph, start, end):
+    """
+    Find the shortest path in a graph using Dijkstra's algorithm.
+    
+    Parameters:
+    graph (dict): A dictionary representing the graph where keys are node names and values are dictionaries of neighboring nodes and their distances.
+    start (str): The starting node.
+    end (str): The destination node.
+    
+    Returns:
+    list: The shortest path from start to end.
+    float: The total distance of the shortest path.
+    """
+    G = nx.DiGraph()
+    
+    # Add edges to the graph
+    for node, neighbors in graph.items():
+        for neighbor, distance in neighbors.items():
+            G.add_edge(node, neighbor, weight=distance)
+    
+    # Find the shortest path using Dijkstra's algorithm
+    shortest_path = nx.dijkstra_path(G, start, end)
+    total_distance = nx.dijkstra_path_length(G, start, end)
+    
+    return shortest_path, total_distance
+
+def combine_maps(city_map, jam_map):
+    """
+    Combine city map and jam map by adding their weights.
+    
+    Parameters:
+    city_map (dict): A dictionary representing the city map where keys are node names and values are dictionaries of neighboring nodes and their distances.
+    jam_map (dict): A dictionary representing the jam map where keys are node names and values are dictionaries of neighboring nodes and their jam factors.
+    
+    Returns:
+    dict: A combined map with summed weights.
+    """
+    combined_map = {}
+    
+    for node in city_map:
+        combined_map[node] = {}
+        for neighbor in city_map[node]:
+            combined_map[node][neighbor] = city_map[node][neighbor] + jam_map[node][neighbor]
+    
+    return combined_map
+
+# Example graph representing the city map
+city_map = {
+    '重症外傷センター': {'池袋': 2, '中目黒': 5},
+    '池袋': {'重症外傷センター': 2, '中目黒': 3, '六本木': 4},
+    '中目黒': {'重症外傷センター': 5, '池袋': 3, '六本木': 2, '銀座': 6},
+    '六本木': {'池袋': 4, '中目黒': 2, '銀座': 1},
+    '銀座': {'中目黒': 6, '六本木': 1}
+}
+
+# Example graph representing the jam map
+jam_map = {
+    '重症外傷センター': {'池袋': 5, '中目黒': 2},
+    '池袋': {'重症外傷センター': 5, '中目黒': 4, '六本木': 3},
+    '中目黒': {'重症外傷センター': 5, '池袋': 6, '六本木': 3, '銀座': 2},
+    '六本木': {'池袋': 1, '中目黒': 3, '銀座': 5},
+    '銀座': {'中目黒': 3, '六本木': 4}
+}
+
+# Combine city map and jam map
+combined_map = combine_maps(city_map, jam_map)
+
+def shortest_path_interface(start_point, destination):
+    shortest_path, total_distance = find_shortest_path(combined_map, start_point, destination)
+    return f"一番短い{start_point}から{destination}までの経路は: {shortest_path}\nトータルの距離は{total_distance}ユニットです。"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=shortest_path_interface,
+    inputs=[gr.Textbox(label="出発地",value="銀座"),gr.Textbox(label="到着地",value="重症外傷センター")],
+    outputs="text",
+    title="救急搬送ルートの最適化",
+    description="出発地点と目的地を入力して、最短経路を計算します。"
+)
+
+# Launch the interface
+iface.launch()