Update app.py

app.py

@@ -11,9 +11,22 @@ import streamlit.components.v1 as components
 
 st.set_page_config(page_title="DebateKG")
 st.title("DebateKG - Automatic Policy Debate Case Creation")
+st.write("WIP, give me a few more days before reviewing!")
 st.caption("github: https://github.com/Hellisotherpeople/DebateKG")
 
 
+form = st.sidebar.form("Main Settings")
+form.header("Main Settings")
+number_of_paths = form.number_input("Enter the cutoff number of paths for all shortest path search", value = 4)
+highlight_threshold = form.number_input("Enter the minimum similarity value needed to highlight" , value = 4)
+show_extract = form.checkbox("Show extracts", value = False)
+show_abstract = form.checkbox("Show abstract", value = False)
+show_full_doc = form.checkbox("Show full doc", value = False)
+show_citation = form.checkbox("Show citation", value = False)
+rerank_word = form.text_area("Enter the word", value = "Full-Document")
+rerank_topic = form.text_area("Enter the topic", value = "Full-Document")
+
+form.form_submit_button("Submit")
 
 
 
@@ -22,7 +35,7 @@ seg = pysbd.Segmenter(language="en", clean=False)
 
 
 embeddings = Embeddings({
- "path": "entence-transformers/all-mpnet-base-v2",
+ "path": "sentence-transformers/all-mpnet-base-v2",
  "content": True,
  "functions": [
  {"name": "graph", "function": "graph.attribute"},
@@ -44,4 +57,73 @@ embeddings = Embeddings({
 embeddings.load("DebateSum_SemanticGraph_mpnet_extract.tar.gz")
 graph = embeddings.graph
 
-print(graph.backend.number_of_nodes(), graph.backend.number_of_edges())
+def david_distance(source, target, attrs):
+ distance = max(1.0 - attrs["weight"], 0.0)
+ return distance if distance >= 0.15 else 1.00
+
+def david_showpath(source, target, the_graph):
+ return nx.shortest_path(the_graph, source, target, david_distance)
+
+
+
+import string
+
+def highlight(index, result):
+ output = f"{index}. "
+ spans = [(token, score, "#fff59d" if score > 0.01 else None) for token, score in result["tokens"]]
+
+ for token, _, color in spans:
+ output += f"<span style='background-color: {color}'>{token}</span> " if color else f"{token} "
+
+ return output
+
+
+
+def showpath_any(list_of_arguments, strip_punctuation = True, the_graph=graph.backend):
+ list_of_paths = []
+ for x, y in zip(list_of_arguments, list_of_arguments[1:]):
+ a_path = david_showpath(x, y, the_graph)
+ list_of_paths.extend(a_path)
+ #print(list_of_paths)
+ path = [graph.attribute(p, "text") for p in list_of_paths]
+ list_of_evidence_ids = []
+ for text in path:
+ if strip_punctuation:
+ text = text.translate(str.maketrans("","", string.punctuation))
+ list_of_evidence_ids.append(int(embeddings.search(f"select id from txtai where similar('{text}') limit 1")[0]['id']))
+ print(list_of_evidence_ids)
+
+ sections = []
+ for x, p in enumerate(path):
+ if x == 0:
+ # Print start node
+ 
+ sections.append(f"{x + 1}. {p}")
+ #sections.append(dataset["Abstract"][list_of_evidence_ids[x]])
+ #sections.append(dataset["Citation"][list_of_evidence_ids[x+1]])
+ #sections.append(dataset["Full-Document"][list_of_evidence_ids[x]])
+
+ if x < len(path) - 1:
+ # Explain and highlight next path element
+ results = embeddings.explain(p, [path[x + 1]], limit=1)[0]
+ sections.append(highlight(x + 2, results))
+ #sections.append(dataset["Abstract"][list_of_evidence_ids[x+1]])
+ #sections.append(dataset["Citation"][list_of_evidence_ids[x+1]])
+ #sections.append(dataset["Full-Document"][list_of_evidence_ids[x+1]])
+
+ return components.html("<br/><br/>".join(sections), scrolling = True, width = 800, height = 1000)
+
+def question(text, rerank_word = "", rerank_topic = "", limit = 100):
+ return embeddings.search(f"select id, text, topic, evidence_id, score from txtai where similar('{text}') and text like '%{rerank_word}%' and topic like '%{rerank_topic}%' limit {limit}")
+
+
+
+query_form = st.form("Query the Index:")
+query_form.write("Write a SQL query")
+query_form_submitted = query_form.form_submit_button("Click me to get ")
+
+
+#showpath_any([3, 12, 15])
+
+with st.expander("mine", expanded = False):
+ st.write(embeddings.search(f"select * from txtai where similar('you') and text like '%the%' limit 10"))