Delete pages/21_GraphRag.py

pages/21_GraphRag.py

@@ -1,82 +0,0 @@
-import streamlit as st
-import graphrag
-import networkx as nx
-import matplotlib.pyplot as plt
-from sentence_transformers import SentenceTransformer
-import torch
-import nltk
-from nltk.tokenize import sent_tokenize, word_tokenize
-nltk.download('punkt', quiet=True)
-
-@st.cache_resource
-def load_models():
-    # Load SentenceTransformer model for sentence embeddings
-    sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
-    return sentence_model
-
-def text_to_graph(text, sentence_model):
-    # Tokenize text into sentences
-    sentences = sent_tokenize(text)
-    
-    # Create graph
-    G = nx.Graph()
-    
-    # Add nodes (sentences) to the graph
-    for i, sentence in enumerate(sentences):
-        embedding = sentence_model.encode(sentence)
-        G.add_node(i, text=sentence, embedding=embedding)
-    
-    # Add edges between sentences based on cosine similarity
-    for i in range(len(sentences)):
-        for j in range(i+1, len(sentences)):
-            similarity = torch.cosine_similarity(
-                torch.tensor(G.nodes[i]['embedding']),
-                torch.tensor(G.nodes[j]['embedding']),
-                dim=0
-            )
-            if similarity > 0.5:  # Adjust this threshold as needed
-                G.add_edge(i, j, weight=similarity.item())
-    
-    return G, sentences
-
-def analyze_text(text, sentence_model):
-    G, sentences = text_to_graph(text, sentence_model)
-    
-    # Basic graph analysis
-    num_nodes = G.number_of_nodes()
-    num_edges = G.number_of_edges()
-    avg_degree = sum(dict(G.degree()).values()) / num_nodes
-    
-    # Identify important sentences using PageRank
-    pagerank = nx.pagerank(G)
-    important_sentences = sorted(pagerank, key=pagerank.get, reverse=True)[:3]
-    
-    return G, sentences, num_nodes, num_edges, avg_degree, important_sentences
-
-st.title("GraphRAG-based Text Analysis")
-
-sentence_model = load_models()
-
-text_input = st.text_area("Enter text for analysis:", height=200)
-
-if st.button("Analyze Text"):
-    if text_input:
-        G, sentences, num_nodes, num_edges, avg_degree, important_sentences = analyze_text(text_input, sentence_model)
-        
-        st.write(f"Number of sentences: {num_nodes}")
-        st.write(f"Number of connections: {num_edges}")
-        st.write(f"Average connections per sentence: {avg_degree:.2f}")
-        
-        st.subheader("Most important sentences:")
-        for i in important_sentences:
-            st.write(f"- {sentences[i]}")
-        
-        # Visualize graph
-        plt.figure(figsize=(10, 6))
-        pos = nx.spring_layout(G)
-        nx.draw(G, pos, with_labels=False, node_size=30, node_color='lightblue', edge_color='gray')
-        plt.title("Text as Graph")
-        st.pyplot(plt)
-        
-    else:
-        st.write("Please enter some text to analyze.")