Create app.py

app.py

@@ -0,0 +1,167 @@
+import streamlit as st
+import networkx as nx
+import matplotlib.pyplot as plt
+import time
+import random
+
+# 1 Dynamic Query Decomposition and Visualization (动态查询分解和可视化)
+def decompose_query(query):
+    # 模拟MindSearch的查询分解，返回一个DAG表示分解流程
+    dag = nx.DiGraph()
+    dag.add_edges_from([ 
+        ("Main Query", "Sub-question 1"),
+        ("Main Query", "Sub-question 2"),
+        ("Sub-question 1", "Sub-question 1.1"),
+        ("Sub-question 1", "Sub-question 1.2"),
+        ("Sub-question 2", "Sub-question 2.1")
+    ])
+    explanations = {
+        "Main Query": "The main query to decompose.",
+        "Sub-question 1": "First major component of the query.",
+        "Sub-question 2": "Second major component of the query.",
+        "Sub-question 1.1": "Detail of Sub-question 1.",
+        "Sub-question 1.2": "Another aspect of Sub-question 1.",
+        "Sub-question 2.1": "Detail of Sub-question 2."
+    }
+    return dag, explanations
+
+st.title("Dynamic Query Decomposition and Visualization")
+
+# 修改：使用唯一的 key 来避免重复
+query_1 = st.text_input("Enter your query for Query Decomposition:", key="query_1")
+if query_1:
+    st.subheader("Query Decomposition")
+    dag, explanations = decompose_query(query_1)
+    
+    # Visualize DAG
+    plt.figure(figsize=(10, 6))
+    pos = nx.spring_layout(dag)
+    nx.draw(dag, pos, with_labels=True, node_color="lightblue", node_size=3000, font_size=10, font_weight="bold")
+    st.pyplot(plt)
+    
+    # Display explanations
+    st.subheader("Explanations for Sub-queries")
+    for node, explanation in explanations.items():
+        st.write(f"**{node}:** {explanation}")
+
+# 2 Search Result Summarization and Comparison (搜寻结果摘要及比较)
+def fetch_results(query, source):
+    # 模拟API结果返回
+    return [
+        {"title": f"{source} Result 1", "snippet": f"{source} snippet for {query}."},
+        {"title": f"{source} Result 2", "snippet": f"{source} snippet for {query}."}
+    ]
+
+def summarize_results(results):
+    return " ".join([result["snippet"] for result in results])
+
+st.title("Search Result Summarization and Comparison")
+
+# 修改：使用唯一的 key 来避免重复
+query_2 = st.text_input("Enter your query for Result Summarization:", key="query_2")
+if query_2:
+    st.subheader("Fetching Results...")
+    bing_results = fetch_results(query_2, "Bing")
+    google_results = fetch_results(query_2, "Google")
+    
+    st.subheader("Results Comparison")
+    col1, col2 = st.columns(2)
+    with col1:
+        st.write("**Bing Results:**")
+        for result in bing_results:
+            st.write(f"- {result['title']}: {result['snippet']}")
+        st.write("**Summary:**", summarize_results(bing_results))
+    with col2:
+        st.write("**Google Results:**")
+        for result in google_results:
+            st.write(f"- {result['title']}: {result['snippet']}")
+        st.write("**Summary:**", summarize_results(google_results))
+
+
+# 3 Search Engine Efficiency Test (搜索引擎效率测试)
+def mock_parallel_search(query):
+    time.sleep(random.uniform(0.5, 1.0))  # Simulate API latency
+    return random.randint(5, 15)  # Mock number of pages retrieved
+
+st.title("Search Engine Efficiency Test")
+
+# 修改：使用唯一的 key 来避免重复
+query_3 = st.text_input("Enter your query for Efficiency Test:", key="query_3")
+if query_3:
+    st.subheader("Efficiency Comparison")
+    start_time = time.time()
+    
+    # Simulate parallel search
+    st.write("Executing parallel search...")
+    pages_retrieved = [mock_parallel_search(query_3) for _ in range(3)]
+    parallel_time = time.time() - start_time
+    
+    # Simulate sequential search
+    st.write("Executing sequential search...")
+    start_time = time.time()
+    sequential_pages_retrieved = sum([mock_parallel_search(query_3) for _ in range(3)])
+    sequential_time = time.time() - start_time
+    
+    # Display metrics
+    st.write(f"**Parallel Search:** {sum(pages_retrieved)} pages retrieved in {parallel_time:.2f} seconds.")
+    st.write(f"**Sequential Search:** {sequential_pages_retrieved} pages retrieved in {sequential_time:.2f} seconds.")
+
+
+# 4 Integration and optimization (整合与优化)
+# def main():
+#     st.sidebar.title("MindSearch Demos")
+#     demo = st.sidebar.radio("Select Demo", ["Query Decomposition", "Result Summarization", "Efficiency Test"])
+    
+#     if demo == "Query Decomposition":
+#         # 使用唯一的 key
+#         query_1 = st.text_input("Enter your query for Query Decomposition:", key="query_1_decomposition")
+#         if query_1:
+#             st.subheader("Query Decomposition")
+#             dag, explanations = decompose_query(query_1)
+#             plt.figure(figsize=(10, 6))
+#             pos = nx.spring_layout(dag)
+#             nx.draw(dag, pos, with_labels=True, node_color="lightblue", node_size=3000, font_size=10, font_weight="bold")
+#             st.pyplot(plt)
+#             st.subheader("Explanations for Sub-queries")
+#             for node, explanation in explanations.items():
+#                 st.write(f"**{node}:** {explanation}")
+    
+#     elif demo == "Result Summarization":
+#         # 使用唯一的 key
+#         query_2 = st.text_input("Enter your query for Result Summarization:", key="query_2_summarization")
+#         if query_2:
+#             st.subheader("Fetching Results...")
+#             bing_results = fetch_results(query_2, "Bing")
+#             google_results = fetch_results(query_2, "Google")
+#             st.subheader("Results Comparison")
+#             col1, col2 = st.columns(2)
+#             with col1:
+#                 st.write("**Bing Results:**")
+#                 for result in bing_results:
+#                     st.write(f"- {result['title']}: {result['snippet']}")
+#                 st.write("**Summary:**", summarize_results(bing_results))
+#             with col2:
+#                 st.write("**Google Results:**")
+#                 for result in google_results:
+#                     st.write(f"- {result['title']}: {result['snippet']}")
+#                 st.write("**Summary:**", summarize_results(google_results))
+    
+#     elif demo == "Efficiency Test":
+#         # 使用唯一的 key
+#         query_3 = st.text_input("Enter your query for Efficiency Test:", key="query_3_efficiency")
+#         if query_3:
+#             st.subheader("Efficiency Comparison")
+#             start_time = time.time()
+#             st.write("Executing parallel search...")
+#             pages_retrieved = [mock_parallel_search(query_3) for _ in range(3)]
+#             parallel_time = time.time() - start_time
+#             st.write("Executing sequential search...")
+#             start_time = time.time()
+#             sequential_pages_retrieved = sum([mock_parallel_search(query_3) for _ in range(3)])
+#             sequential_time = time.time() - start_time
+#             st.write(f"**Parallel Search:** {sum(pages_retrieved)} pages retrieved in {parallel_time:.2f} seconds.")
+#             st.write(f"**Sequential Search:** {sequential_pages_retrieved} pages retrieved in {sequential_time:.2f} seconds.")
+
+
+# if __name__ == "__main__":
+#     main()