Create app.py

app.py

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+import streamlit as st
+import streamlit.components.v1 as components
+from pyvis.network import Network
+import networkx as nx
+import re
+import emoji
+
+st.title('Pyvis VisJS Knowledge Graph from Markdown')
+
+# Make Network show itself with repr_html
+def net_repr_html(self):
+    nodes, edges, height, width, options = self.get_network_data()
+    html = self.template.render(height=height, width=width, nodes=nodes, edges=edges, options=options)
+    return html
+
+Network._repr_html_ = net_repr_html
+
+st.sidebar.title('Upload a Markdown File')
+uploaded_file = st.sidebar.file_uploader("Choose a file", type=["md"])
+
+physics = st.sidebar.checkbox('Add physics interactivity?')
+
+def draw_knowledge_graph(file_contents, physics):
+    # Extract sentences and emojis from the file
+    sentences = re.split(r'\n', file_contents)
+    emojis_list = [emoji.emojize(x) for x in emoji.UNICODE_EMOJI['en']]
+    emojis = [ch for ch in file_contents if ch in emojis_list]
+    
+    # Create a graph
+    G = nx.Graph()
+
+    # Add nodes and edges
+    for idx, sentence in enumerate(sentences):
+        G.add_node(idx, label=sentence)
+        words = set(sentence.lower().split())
+        for j, other_sentence in enumerate(sentences[:idx]):
+            other_words = set(other_sentence.lower().split())
+            common_words = words & other_words
+            common_emojis = set(emojis) & set(sentence) & set(other_sentence)
+            if common_words or common_emojis:
+                G.add_edge(idx, j, weight=len(common_words) + len(common_emojis))
+    
+    # Visualize the graph
+    net = Network(notebook=True)
+    net.from_nx(G)
+    net.toggle_physics(physics)
+
+    return net._repr_html_()
+
+if uploaded_file is not None:
+    file_contents = uploaded_file.read().decode("utf-8")
+    html = draw_knowledge_graph(file_contents, physics)
+    components.html(html, height=1200, width=1000)
+else:
+    st.warning("Please upload a markdown file to visualize the knowledge graph.")