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"))