Update app.py

app.py

@@ -70,35 +70,35 @@ def generate_from_prompt(prompt, provider, key_dict):
 
 # --- UI Definitions ---
 STANDARD_LABELS = [
-    "Person", "Organization", "Location", "Country", "City", "State", 
+    "Person", "Organisation", "Location", "Country", "City", "State", 
     "Nationality", "Group", "Date", "Event", "Law", "Legal Document", 
-    "Product", "Facility", "Work Of Art", "Language", "Time", "Percentage", 
+    "Product", "Facility", "Work of Art", "Language", "Time", "Percentage", 
     "Money", "Currency", "Quantity", "Ordinal Number", "Cardinal Number"
 ]
 MAX_CATEGORIES = 8
 
-with gr.Blocks(title="Historical Text Analysis Tool", css=".prose { word-break: break-word; }") as demo:
-    gr.Markdown("# Historical Text Analysis Tool")
+with gr.Blocks(title="Historical Text Analyser", css=".prose { word-break: break-word; }") as demo:
+    gr.Markdown("# Historical Text Analyser")
     gr.Markdown("""
-        This tool uses two forms of AI to accelerate historical research. First, a **Conceptual AI** generates a research framework with relevant search terms for your topic. Second, an **Extraction AI** scans your source text to find and highlight those terms with high precision.
-        ### Understanding "Entities" and "Labels"
-        In text analysis, this process is often called "Named Entity Recognition" (NER).
-        - An **Entity** is a specific piece of text in your document, like a name, a place, or a date (e.g., `Queen Victoria`, `1848`, `London`).
-        - A **Label** is the category that entity belongs to (e.g., `Person`, `Date`, `Location`).
-        This tool helps you define your labels and then automatically finds the corresponding entities in your text.
+        First, a **Conceptual AI**, powered by a large language model such as OpenAI's GPT-4, Anthropic's Claude, or Google's Gemini, suggests labels based on your chosen historical topic. These labels are grouped into broader categories (e.g. Economic Policies, Significant Events) to help focus your research. Second, an **Extraction AI**, powered by the GLiNER model, scans your source text to find and highlight matching entities - instances where those labels appear in the document - with high accuracy.
+        ### Understanding Entities and Labels ###
+        In text analysis, this process is often called Named Entity Recognition (NER).
+        - An **Entity** is a specific piece of text in your document, such as a name, a place, or a date (e.g. Queen Victoria, 1848).
+        - A **Label** is the category that the entity belongs to (e.g. Person, Date, Location).
+        This tool helps you to define your labels and then automatically finds the corresponding entities in your text.
         """)
     
-    gr.Markdown("--- \n## Step 1: Generate a Research Framework")
+    gr.Markdown("--- \n## Step 1: Generate Labels")
     with gr.Row():
-        topic = gr.Textbox(label="Enter a Historical Topic", placeholder="e.g., The Chartist Movement")
-        provider = gr.Dropdown(choices=list(MODEL_OPTIONS.keys()), label="Choose Conceptual AI Model")
+        topic = gr.Textbox(label="Enter a Historical Topic", placeholder="e.g. Britain during the Second World War")
+        provider = gr.Dropdown(choices=list(MODEL_OPTIONS.keys()), label="Choose AI Model")
     with gr.Row():
         openai_key = gr.Textbox(label="OpenAI API Key", type="password")
         anthropic_key = gr.Textbox(label="Anthropic API Key", type="password")
         google_key = gr.Textbox(label="Google API Key", type="password")
-    generate_btn = gr.Button("Generate Framework", variant="primary")
+    generate_btn = gr.Button("Generate Labels", variant="primary")
 
-    gr.Markdown("--- \n## Step 2: Define Labels and Analyze Source Text")
+    gr.Markdown("--- \n## Step 2: Confirm Labels and Analyse Source Text")
     gr.Markdown("#### 1. AI-Suggested Labels")
     dynamic_components = []
     with gr.Column():