Update app.py

app.py

@@ -1,4 +1,5 @@
 # app.py
+
 import streamlit as st
 import spacy
 import subprocess
@@ -13,20 +14,27 @@ def install_spacy_model():
         spacy.load("en_core_web_sm")
     except OSError:
         subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
+        spacy.load("en_core_web_sm")
+
 install_spacy_model()
 
 # Load spaCy model
 nlp = spacy.load("en_core_web_sm")
 
 # === Neo4j credentials ===
-uri = "neo4j+s://ff701b1c.databases.neo4j.io"
-username = "neo4j"
-password = "BfZM7YRKpFz1b_V7acAmOtaSQHPU9xK03rJlfPep88g"  
+NEO4J_URI = "neo4j+s://ff701b1c.databases.neo4j.io"
+NEO4J_USERNAME = "neo4j"
+NEO4J_PASSWORD = "BfZM7YRKpFz1b_V7acAmOtaSQHPU9xK03rJlfPep88g"  
 
-# Connect to Neo4j
-driver = GraphDatabase.driver(uri, auth=(username, password))
+def get_neo4j_driver():
+    try:
+        driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))
+        return driver
+    except Exception as e:
+        st.error(f"Failed to connect to Neo4j: {e}")
+        return None
 
-# === TF-IDF Filtering ===
+# === TF-IDF Filtering (Optional for noise reduction) ===
 def compute_tfidf_keywords(text: str, top_n=100):
     vectorizer = TfidfVectorizer(stop_words='english')
     X = vectorizer.fit_transform([text])
@@ -34,10 +42,10 @@ def compute_tfidf_keywords(text: str, top_n=100):
     sorted_scores = sorted(scores, key=lambda x: x[1], reverse=True)
     return {word for word, _ in sorted_scores[:top_n]}
 
-# === Enhanced Triple Extraction ===
-def extract_triples(text):
+# === Triple Extraction ===
+def extract_triples(text, use_tfidf=False):
     doc = nlp(text)
-    tfidf_keywords = compute_tfidf_keywords(text)
+    tfidf_keywords = compute_tfidf_keywords(text) if use_tfidf else None
     triples = []
 
     for sent in doc.sents:
@@ -51,44 +59,56 @@ def extract_triples(text):
             verb = root[0].lemma_
 
         for chunk in noun_chunks:
-            if chunk.root.dep_ in ("nsubj", "nsubjpass"):
+            if chunk.root.dep_ in ("nsubj", "nsubjpass") and not subject:
                 subject = chunk.text
-            elif chunk.root.dep_ in ("dobj", "pobj", "attr"):
+            elif chunk.root.dep_ in ("dobj", "pobj", "attr") and not obj:
                 obj = chunk.text
 
         if subject and verb and obj:
-            triples.append((subject.strip(), verb.strip(), obj.strip()))
+            if tfidf_keywords:
+                if subject.lower() in tfidf_keywords or obj.lower() in tfidf_keywords:
+                    triples.append((subject.strip(), verb.strip(), obj.strip()))
+            else:
+                triples.append((subject.strip(), verb.strip(), obj.strip()))
 
     return triples
 
 # === Visualization Function ===
 def show_graph(triples):
+    if not triples:
+        st.warning("No triples found to visualize.")
+        return
+
     G = nx.DiGraph()
     for s, p, o in triples:
         G.add_node(s)
         G.add_node(o)
         G.add_edge(s, o, label=p)
-    pos = nx.spring_layout(G)
+
+    pos = nx.spring_layout(G, seed=42)  # fixed layout
     plt.figure(figsize=(10, 8))
-    nx.draw(G, pos, with_labels=True, node_color='skyblue', node_size=2000, font_size=10)
+    nx.draw(G, pos, with_labels=True, node_color='skyblue', node_size=2000, font_size=10, edge_color='gray')
     nx.draw_networkx_edge_labels(G, pos, edge_labels={(u, v): d['label'] for u, v, d in G.edges(data=True)})
-    st.pyplot(plt)
+    st.pyplot(plt.gcf())
+    plt.clf()
 
 # === Streamlit UI ===
-st.title("🧠 Knowledge Graph Generator:")
+st.title("🧠 Knowledge Graph Generator")
 
-text_input = st.text_area("Paste your text here", height=200)
+text_input = st.text_area("Paste your text here:", height=200)
+use_tfidf = st.checkbox("Use TF-IDF filtering (Optional: Recommended for large texts)")
 
 if st.button("Generate Graph"):
     if text_input:
-        all_triples = extract_triples(text_input)
+        all_triples = extract_triples(text_input, use_tfidf=use_tfidf)
 
-        # Display only the first 10
-        st.write("### Extracted Triples (showing top 10)")
-        for t in all_triples[:10]:
-            st.write("🔗", t)
+        if all_triples:
+            st.subheader("🔗 Extracted Triples:")
+            for triple in all_triples:
+                st.markdown(f"- **({triple[0]} → {triple[1]} → {triple[2]})**")
 
-        # Visualize all triples
-        show_graph(all_triples)
+            show_graph(all_triples)
+        else:
+            st.warning("No valid triples could be extracted. Try different text.")
     else:
         st.warning("Please enter some text.")