From 5ee9d0a0d0cf6bd7b9cf2af5b1bdb2a7eca63175 Mon Sep 17 00:00:00 2001 From: Preston M Date: Mon, 20 May 2024 02:13:24 -0500 Subject: [PATCH] Add files via upload --- chromaViewMaster.py | 454 ++++++++++++++++++++++++++++++++++++++++++ static/background.jpg | Bin 0 -> 197775 bytes static/background.png | Bin 0 -> 928718 bytes 3 files changed, 454 insertions(+) create mode 100644 chromaViewMaster.py create mode 100644 static/background.jpg create mode 100644 static/background.png diff --git a/chromaViewMaster.py b/chromaViewMaster.py new file mode 100644 index 0000000..9b923fb --- /dev/null +++ b/chromaViewMaster.py @@ -0,0 +1,454 @@ +import chromadb +from chromadb.config import Settings +from chromadb.utils import embedding_functions +import pandas as pd +import streamlit as st +import argparse +from sklearn.decomposition import LatentDirichletAllocation +from sklearn.feature_extraction.text import CountVectorizer +import networkx as nx +import spacy +from wordcloud import WordCloud +import plotly.express as px +import plotly.graph_objects as go +from collections import Counter +from textblob import TextBlob +from pyvis.network import Network +import streamlit.components.v1 as components + + + + +# Load spaCy model for entity extraction +nlp = spacy.load("en_core_web_sm") + +# Set Streamlit page configuration for wide layout +st.set_page_config(layout="wide") + +# Define the URL for the background image +background_image_url = "static/background.jpg" + + + +# Inject custom CSS for full-screen layout with a custom background image and reduced checkbox font size +st.markdown( + f""" + + """, + unsafe_allow_html=True +) + +parser = argparse.ArgumentParser(description='Set the Chroma DB path to view collections') +parser.add_argument('db') + +pd.set_option('display.max_columns', 4) + +def preprocess_for_sunburst(df): + if 'category' in df.columns and 'subcategory' in df.columns and 'value' in df.columns: + df['level_1'] = df['category'] + df['level_2'] = df['subcategory'] + df['level_3'] = df['subcategory'] + return df[['level_1', 'level_2', 'level_3', 'value']] + else: + st.write("The DataFrame does not contain the required columns for the Sunburst chart.") + return pd.DataFrame() + +def preprocess_for_knowledge_graph(df): + entities = [] + relationships = [] + for doc in nlp.pipe(df['documents'].astype(str)): + for ent in doc.ents: + entities.append(ent.text) + for token in doc: + if token.dep_ == 'ROOT' and token.head.dep_ != 'ROOT': + relationships.append((token.head.text, token.text)) + entity_freq = Counter(entities) + return entities, relationships, entity_freq + +# Updated function to render collection statistics +def render_collection_statistics(df): + st.subheader("Collection Statistics") + st.write("View statistics on the current collection.") + + num_documents = len(df) + doc_lengths = df['documents'].apply(len) + avg_doc_length = doc_lengths.mean() + median_doc_length = doc_lengths.median() + std_doc_length = doc_lengths.std() + + most_common_words = Counter(" ".join(df['documents']).split()).most_common(10) + most_common_words_str = ", ".join([word for word, count in most_common_words]) + + entities = [ent.text for doc in nlp.pipe(df['documents'].astype(str)) for ent in doc.ents] + most_common_entities = Counter(entities).most_common(10) + most_common_entities_str = ", ".join([entity for entity, count in most_common_entities]) + + df['sentiment'] = df['documents'].apply(lambda x: TextBlob(x).sentiment.polarity) + sentiment_counts = df['sentiment'].value_counts(bins=3, sort=False) + sentiment_distribution = { + 'Negative': sentiment_counts.iloc[0], + 'Neutral': sentiment_counts.iloc[1], + 'Positive': sentiment_counts.iloc[2] + } + + statistics = { + "Number of Documents": num_documents, + "Average Document Length": avg_doc_length, + "Median Document Length": median_doc_length, + "Standard Deviation of Document Length": std_doc_length, + "Most Common Words": most_common_words_str, + "Most Common Entities": most_common_entities_str, + "Negative Sentiment Documents": sentiment_distribution['Negative'], + "Neutral Sentiment Documents": sentiment_distribution['Neutral'], + "Positive Sentiment Documents": sentiment_distribution['Positive'] + } + + # Convert all values to strings to avoid conversion errors + statistics_str = {k: str(v) for k, v in statistics.items()} + + st.table(pd.DataFrame(statistics_str.items(), columns=['Statistic', 'Value'])) + +# Visualization Functions +def render_topic_modeling(df, collection_name): + num_topics = st.slider("Number of Topics", min_value=2, max_value=10, value=5, key=f"{collection_name}_num_topics", help="Select the number of topics to be discovered in the documents.") + + # Vectorize the documents + vectorizer = CountVectorizer(stop_words='english') + doc_term_matrix = vectorizer.fit_transform(df['documents'].astype(str)) + + # Fit LDA model + lda = LatentDirichletAllocation(n_components=num_topics, random_state=42) + lda.fit(doc_term_matrix) + topic_probabilities = lda.transform(doc_term_matrix) + + # Create topic probability dataframe + topic_df = pd.DataFrame(topic_probabilities, columns=[f"Topic {i+1}" for i in range(num_topics)]) + topic_df['Document'] = df['documents'] + + st.subheader("Topic Modeling Results") + st.write("Topic modeling helps identify main themes and subjects discussed in the documents.") + + # Display topic summaries + feature_names = vectorizer.get_feature_names_out() + topic_summaries = [] + for i, component in enumerate(lda.components_): + top_words = ', '.join([feature_names[idx] for idx in component.argsort()[:-10 - 1:-1]]) + topic_summaries.append(f"Topic {i+1}: {top_words}") + st.write("Topic Summaries:") + st.write('\n'.join(topic_summaries)) + + # Create heatmap with improved color scheme and tooltips + fig = px.imshow(topic_df.set_index('Document').iloc[:, :-1], labels={'color': 'Probability'}, title='Document-Topic Probability Distribution', color_continuous_scale='Viridis', aspect="auto") + fig.update_traces(hovertemplate='Document: %{y}
Topic: %{x}
Probability: %{z:.2f}') + fig.update_xaxes(side="top") + fig.update_layout(xaxis_title="Topics", yaxis_title="Documents", coloraxis_colorbar=dict(title="Probability")) + st.plotly_chart(fig) + +def render_similarity_search(df, collection_name): + query = st.text_input("Enter a query document", key=f"{collection_name}_query", help="Enter text to search for similar documents within the collection.") + if query: + # Reverting to the previously working code + results = df[df['documents'].str.contains(query, case=False, na=False)] + + st.subheader("Similarity Search Results") + st.write("Similarity search allows you to find documents within the collection that are most similar to a given query document.") + + for i, result in enumerate(results['documents'].head(5)): + st.markdown(f"**Result {i+1}:**") + st.write(result) + +def render_knowledge_graph_plotly(df, collection_name): + entities, relationships, entity_freq = preprocess_for_knowledge_graph(df) + top_entities = entity_freq.most_common(10) + entity_labels = [item[0] for item in top_entities] + entity_counts = [item[1] for item in top_entities] + + G = nx.Graph() + G.add_nodes_from(entity_labels) + G.add_edges_from(relationships) + + pos = nx.spring_layout(G) + for node in G.nodes(): + G.nodes[node]['pos'] = pos[node] + + edge_x = [] + edge_y = [] + for edge in G.edges(): + x0, y0 = G.nodes[edge[0]]['pos'] + x1, y1 = G.nodes[edge[1]]['pos'] + edge_x.append(x0) + edge_x.append(x1) + edge_x.append(None) + edge_y.append(y0) + edge_y.append(y1) + edge_y.append(None) + + edge_trace = go.Scatter( + x=edge_x, y=edge_y, + line=dict(width=0.5, color='#888'), + hoverinfo='none', + mode='lines') + + node_x = [] + node_y = [] + for node in G.nodes(): + x, y = G.nodes[node]['pos'] + node_x.append(x) + node_y.append(y) + + node_trace = go.Scatter( + x=node_x, y=node_y, + mode='markers', + hoverinfo='text', + marker=dict( + showscale=True, + colorscale='YlGnBu', + reversescale=True, + color=[], + size=10, + colorbar=dict( + thickness=15, + title='Node Connections', + xanchor='left', + titleside='right' + ), + line_width=2)) + + node_adjacencies = [] + node_text = [] + for node, adjacencies in enumerate(G.adjacency()): + node_adjacencies.append(len(adjacencies[1])) + node_text.append(f'{node}\n# of connections: {len(adjacencies[1])}') + + node_trace.marker.color = node_adjacencies + node_trace.text = node_text + + fig = go.Figure(data=[edge_trace, node_trace], + layout=go.Layout( + title='
Knowledge Graph', + titlefont_size=16, + showlegend=False, + hovermode='closest', + margin=dict(b=20, l=5, r=5, t=40), + annotations=[dict( + text="Python code: https://plotly.com/ipython-notebooks/network-graphs/", + showarrow=False, + xref="paper", yref="paper", + x=0.005, y=-0.002)], + xaxis=dict(showgrid=False, zeroline=False, showticklabels=False), + yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)) + ) + st.plotly_chart(fig) + st.write("A knowledge graph visually represents entities and their relationships extracted from the documents.") + +def render_knowledge_graph_pyvis(df, collection_name): + entities, relationships, entity_freq = preprocess_for_knowledge_graph(df) + top_entities = entity_freq.most_common(10) + entity_labels = [item[0] for item in top_entities] + + G = nx.Graph() + G.add_nodes_from(entity_labels) + G.add_edges_from(relationships) + + net = Network(height='750px', width='100%', notebook=True) + net.from_nx(G) + net.show_buttons(filter_=['physics']) + + html_file = f"{collection_name}_knowledge_graph.html" + net.save_graph(html_file) + + st.subheader("Knowledge Graph (PyVis)") + st.write("A knowledge graph visually represents entities and their relationships extracted from the documents.") + components.html(open(html_file, 'r', encoding='utf-8').read(), height=800) + +def render_tag_cloud(df): + text = ' '.join(df['documents']) + wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text) + + st.subheader("Tag Cloud") + st.write("A tag cloud visualizes the prominence of words in the documents. Words that appear larger are more frequent in the text.") + st.image(wordcloud.to_array(), use_column_width=True) + +def render_sunburst_chart(df): + st.subheader("Sunburst Chart") + st.write("A Sunburst chart visualizes hierarchical data spanning outwards radially from root to leaves.") + sunburst_df = preprocess_for_sunburst(df) + if not sunburst_df.empty: + fig = px.sunburst(sunburst_df, path=['level_1', 'level_2', 'level_3'], values='value', title="Sunburst Chart") + st.plotly_chart(fig) + else: + st.write("The DataFrame does not contain the required columns for the Sunburst chart.") + +def render_document_length_analysis(df): + df['document_length'] = df['documents'].apply(len) + st.subheader("Document Length Analysis") + st.write("Analyze the length of the documents to understand the distribution of document sizes.") + fig = px.histogram(df, x='document_length', nbins=50, title="Document Length Distribution") + st.plotly_chart(fig) + +def render_entity_cooccurrence_matrix(df): + entities = [ent.text for doc in nlp.pipe(df['documents'].astype(str)) for ent in doc.ents] + entity_cooc = pd.crosstab(pd.Series(entities), pd.Series(entities)) + st.subheader("Entity Co-occurrence Matrix") + st.write("Shows the frequency with which entities co-occur within the documents.") + fig = px.imshow(entity_cooc, title='Entity Co-occurrence Matrix') + st.plotly_chart(fig) + +def render_sentiment_analysis(df): + df['sentiment'] = df['documents'].apply(lambda x: TextBlob(x).sentiment.polarity) + st.subheader("Sentiment Analysis") + st.write("Perform sentiment analysis on the documents and visualize the results. Sentiment values range from -1 (negative) to 1 (positive).") + + df['sentiment_category'] = pd.cut(df['sentiment'], bins=[-1, -0.5, 0.5, 1], labels=['Negative', 'Neutral', 'Positive']) + + fig = px.histogram(df, x='sentiment', nbins=50, title="Sentiment Distribution") + fig.add_annotation(x=-0.75, y=0, text='Negative', showarrow=False, yshift=10, font=dict(size=12, color='red')) + fig.add_annotation(x=0, y=0, text='Neutral', showarrow=False, yshift=10, font=dict(size=12, color='orange')) + fig.add_annotation(x=0.75, y=0, text='Positive', showarrow=False, yshift=10, font=dict(size=12, color='green')) + + st.plotly_chart(fig) + + sentiment_counts = df['sentiment_category'].value_counts() + st.write("Sentiment Category Counts:") + st.write(sentiment_counts) + +def render_network_centrality(G, collection_name): + centrality = nx.degree_centrality(G) + st.subheader("Network Centrality Metrics") + st.write("Displays the centrality metrics for nodes in the knowledge graph. Nodes with higher centrality are more connected within the graph.") + st.write(pd.DataFrame.from_dict(centrality, orient='index', columns=['Centrality'])) + +def view_collections(dir): + st.header("ChromaView Master 1.0") + st.markdown("DB Path: %s" % dir) + st.write("Below are the collections found in the specified Chroma DB path. You can explore the collections by performing various actions like topic modeling, similarity search, and knowledge graph visualization. Select a chroma collection below.") + client = chromadb.PersistentClient(path=dir) + + collections = client.list_collections() + collection_names = [collection.name for collection in collections] + selected_collection_name = st.selectbox("**Begin By Selecting a Collection**", collection_names) + + + if selected_collection_name: + for collection in collections: + if collection.name == selected_collection_name: + data = collection.get() + ids = data['ids'] + embeddings = data["embeddings"] + metadata = data["metadatas"] + documents = data["documents"] + + df = pd.DataFrame.from_dict(data) + + col1, col2 = st.columns([1, 3]) + + with col1: + st.subheader("Options") + view_collection_statistics = st.checkbox("View Collection Statistics", key=f"{collection.name}_collection_statistics", help="View statistics on the current collection.") + perform_topic_modeling = st.checkbox("Perform Topic Modeling", key=f"{collection.name}_topic_modeling", help="Analyze the main topics discussed in the documents.") + perform_similarity_search = st.checkbox("Perform Similarity Search", key=f"{collection.name}_similarity_search", help="Find documents similar to a given query.") + visualize_knowledge_graph = st.checkbox("Visualize Knowledge Graph (Plotly)", key=f"{collection.name}_knowledge_graph_plotly", help="Visualize the entities and their relationships extracted from the documents using Plotly.") + visualize_knowledge_graph_pyvis = st.checkbox("Visualize Knowledge Graph (PyVis)", key=f"{collection.name}_knowledge_graph_pyvis", help="Visualize the entities and their relationships extracted from the documents using PyVis.") + generate_tag_cloud = st.checkbox("Generate Tag Cloud", key=f"{collection.name}_tag_cloud", help="Visualize the prominence of words in the documents.") + generate_sunburst_chart = st.checkbox("Generate Sunburst Chart", key=f"{collection.name}_sunburst_chart", help="Visualize the hierarchical structure of the data using a sunburst chart.") + analyze_document_length = st.checkbox("Analyze Document Length", key=f"{collection.name}_document_length", help="Analyze the length of the documents.") + display_entity_cooccurrence = st.checkbox("Display Entity Co-occurrence", key=f"{collection.name}_entity_cooccurrence", help="Display the co-occurrence matrix of entities.") + perform_sentiment_analysis = st.checkbox("Perform Sentiment Analysis", key=f"{collection.name}_sentiment_analysis", help="Analyze the sentiment of the documents.") + + with col2: + st.subheader("Results") + st.write("Your output from the left selection will appear here.") + + if view_collection_statistics: + render_collection_statistics(df) + + if perform_topic_modeling: + render_topic_modeling(df, collection.name) + + if perform_similarity_search: + render_similarity_search(df, collection.name) + + if visualize_knowledge_graph: + render_knowledge_graph_plotly(df, collection.name) + + if visualize_knowledge_graph_pyvis: + render_knowledge_graph_pyvis(df, collection.name) + + if generate_tag_cloud: + render_tag_cloud(df) + + if generate_sunburst_chart: + render_sunburst_chart(df) + + if analyze_document_length: + render_document_length_analysis(df) + + if display_entity_cooccurrence: + render_entity_cooccurrence_matrix(df) + + if perform_sentiment_analysis: + render_sentiment_analysis(df) + +if __name__ == "__main__": + try: + args = parser.parse_args() + print("Opening database: %s" % args.db) + view_collections(args.db) + except: + pass diff --git a/static/background.jpg b/static/background.jpg new file mode 100644 index 0000000000000000000000000000000000000000..0e6b7145a4084ee1693062c7b054063e75fa51d0 GIT binary patch literal 197775 zcmeFZdpy(c|35w^D?}$DhgB*?iWDP^dWDcBv2tcglvCtvBSkrEhQr<_P}J z9fd$S;Qw=+FZ|!v{O6DS`|_^^{dz zUkm(yTVQ(x^aLdEpC%{(&~QOPL9h@QXhOn5f15Bw_&*Ku-)-lA+OEG%^naVc-%o+T zJAiM|ox(f+_x%6$!S)!y>kYP>Kznxz0Y_kgqaeY(0^q#@+g}9U0wTnY|9Zmz=?3te zLOX;ZJ9mkQ?g0r3fWd-7;2k@J0O3I(;;)n-w0Fn8BkG32`z>7{NBs_*i%QPjsd%Zp zS@K3V>zIb?!^gWs4oXSO96EkN>EtQp^A|L=VA?vD|G8phY;x7q>ZY{~^44uzl-r%V z?)N-A{R18a1_g(NMn8EP6Z`CW+>4adwDgx5uU@~+%P%M-6um3{_^G0@s=B83b4%-& zHZrCCYe!FSU;od6!J*-)Y1+)}FZvu~erb7Sb!~lv&EW#t#(#AOc>S*){lEIN7wC_m zkPui1^0z+%f7WL-`}tOY$N%z zR8XCs!@v34EDftf9zXhmJoLof8QeQkiFV|7DO~K^H-4!%pi>VfPojEm?OEaz?{)3x ze;rws3)}`BC8?t?4mD#AluFZcVe^>Qk%hRQCufV$C&MF_ei7ySC?U#MDSGO|jo<CRa|NhXmG_YCT>sYLZV^Zbx&Lz$(MZilH-XQvAKiT~35o!giUypMi=w=i4g7WKg`vM+ZjG z(o2Dv&R5SW{Ui4fLtqRSl9Q9yva^DZ$r`RJE|(P_6$wL9b(C=DCAUVcgvsh`@iDl_ z>W{N+ngb$-&Vlsw^pxc+p7j|=v=hYBjAWxuie;(W2s~IB{#0K2wa%|6VgL2VqkdMx z^4GwJlr5qUboE8t6X2{KxcjC#zdZ-CKhB(M^-w)7HzHzp{+(Ic0pT_tHZ|MxtX14f zgdI6OJw&!ij&Dtuaysy!Z|aU*0562b+fq&y%eslns~_Al?#W8)6jq^|A)Z%#B#Emt zxdpEnB^N2I-Lwr-+y<3e zuWo~$4%^>&C1WUuHE7~JE4u?~~ zw+g4Qbkrz{cu5#eg_^R>Xaj9S&e?Z7wAUm#{lsug4R)27)dZ89t~8;SZz%^$tmni` zPs40!(cKT#DKH)vIfrQ31|@XOtA9bg3HK5@{<_KIHh#Z#9D^L2l>^n+}`W zJky#Q?CzfZnE@NCym{M0f$)5Zox$1Te92KGH3%dDnJ@j~R`n$C5SORm@rUNe4=Cx! 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