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| import gradio as gr | |
| import networkx as nx | |
| import matplotlib.pyplot as plt | |
| import spacy | |
| import pandas as pd | |
| import numpy as np | |
| from pathlib import Path | |
| # Load SpaCy model | |
| nlp = spacy.load("en_core_web_sm") | |
| # Categories and their colors | |
| CATEGORIES = { | |
| "Main Themes": "#004d99", | |
| "Events": "#006400", | |
| "People": "#8b4513", | |
| "Laws/Policies": "#4b0082", | |
| "Concepts": "#800000" | |
| } | |
| def load_historical_data(): | |
| """Load and process the Unit 5 text data.""" | |
| try: | |
| with open("Unit5_OCR.txt", "r", encoding="utf-8") as f: | |
| content = f.read() | |
| return content | |
| except FileNotFoundError: | |
| return "Historical data file not found." | |
| def extract_entities(text): | |
| """Extract named entities and important terms from text.""" | |
| doc = nlp(text) | |
| entities = {} | |
| # Extract named entities | |
| for ent in doc.ents: | |
| if ent.label_ in ["PERSON", "EVENT", "DATE", "LAW", "ORG"]: | |
| if ent.text not in entities: | |
| entities[ent.text] = { | |
| "type": ent.label_, | |
| "count": 1, | |
| "context": [] | |
| } | |
| else: | |
| entities[ent.text]["count"] += 1 | |
| return entities | |
| def find_related_terms(term, text, window_size=100): | |
| """Find terms that appear near the search term.""" | |
| term = term.lower() | |
| text = text.lower() | |
| related = {} | |
| # Find all occurrences of the term | |
| index = text.find(term) | |
| while index != -1: | |
| # Get surrounding context | |
| start = max(0, index - window_size) | |
| end = min(len(text), index + len(term) + window_size) | |
| context = text[start:end] | |
| # Process context to find other entities | |
| doc = nlp(context) | |
| for ent in doc.ents: | |
| if ent.text.lower() != term: | |
| if ent.text not in related: | |
| related[ent.text] = { | |
| "type": ent.label_, | |
| "count": 1, | |
| "relevance": 1.0 | |
| } | |
| else: | |
| related[ent.text]["count"] += 1 | |
| related[ent.text]["relevance"] += 0.5 | |
| index = text.find(term, index + 1) | |
| return related | |
| def generate_context_map(term): | |
| """Generate a network visualization for the given term.""" | |
| if not term.strip(): | |
| return None | |
| # Load historical data | |
| content = load_historical_data() | |
| if content == "Historical data file not found.": | |
| return None | |
| # Create network graph | |
| G = nx.Graph() | |
| # Find related terms | |
| related_items = find_related_terms(term, content) | |
| # Add central node | |
| G.add_node(term, category="Main Themes") | |
| # Add related nodes (limit to top 10 by relevance) | |
| sorted_items = sorted(related_items.items(), | |
| key=lambda x: x[1]["relevance"], | |
| reverse=True)[:10] | |
| for item_name, item_data in sorted_items: | |
| G.add_node(item_name, category=item_data["type"]) | |
| G.add_edge(term, item_name, | |
| weight=item_data["relevance"], | |
| length=2.0/item_data["relevance"]) | |
| # Create visualization | |
| plt.figure(figsize=(12, 12)) | |
| plt.clf() | |
| # Set up the layout | |
| pos = nx.spring_layout(G, k=1, iterations=50) | |
| # Draw nodes | |
| for category, color in CATEGORIES.items(): | |
| nodes = [node for node, attr in G.nodes(data=True) | |
| if attr.get("category", "") == category] | |
| nx.draw_networkx_nodes(G, pos, nodelist=nodes, | |
| node_color=color, | |
| node_size=2000) | |
| # Draw edges | |
| nx.draw_networkx_edges(G, pos, edge_color='white', | |
| width=1, alpha=0.5) | |
| # Add labels | |
| labels = {node: node for node in G.nodes()} | |
| nx.draw_networkx_labels(G, pos, labels, font_size=8, | |
| font_color='white') | |
| # Set dark background | |
| plt.gca().set_facecolor('#1a1a1a') | |
| plt.gcf().set_facecolor('#1a1a1a') | |
| # Add title | |
| plt.title(f"Historical Context Map for '{term}'", | |
| color='white', pad=20) | |
| return plt.gcf() | |
| # Create Gradio interface | |
| iface = gr.Interface( | |
| fn=generate_context_map, | |
| inputs=gr.Textbox(label="Enter a historical term from Unit 5", | |
| placeholder="e.g., Civil War, Abraham Lincoln, Reconstruction"), | |
| outputs=gr.Plot(), | |
| title="Historical Context Mapper", | |
| description="This tool generates a network visualization showing the historical context and connections for terms from Unit 5 (1844-1877).", | |
| theme="darkhuggingface", | |
| examples=[ | |
| ["Civil War"], | |
| ["Abraham Lincoln"], | |
| ["Reconstruction"], | |
| ["Manifest Destiny"], | |
| ["Transcontinental Railroad"] | |
| ] | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() |