Update app.py

app.py

@@ -37,10 +37,6 @@ KEYWORD_COLORS = [
     '#10AC84', '#EE5A24', '#0FBC89', '#5F27CD', '#FF3838'
 ]
 
-# Global variable to store current keywords for filtering
-current_keywords = []
-current_text = ""
-
 class KeywordExtractionManager:
     def __init__(self):
         self.rake_extractor = None
@@ -457,7 +453,7 @@ def get_relevance_level(score, max_score):
     else:
         return 'low'
 
-def create_highlighted_html(text, keywords, filter_level='all'):
+def create_highlighted_html(text, keywords):
     """Create HTML with highlighted keywords in the text"""
     if not keywords:
         return f"<div style='padding: 15px; border: 1px solid #ddd; border-radius: 5px; background-color: #fafafa;'><p>{text}</p></div>"
@@ -474,17 +470,13 @@ def create_highlighted_html(text, keywords, filter_level='all'):
         keyword = kw_data['keyword']
         score = kw_data['score']
         color = get_score_color(score, max_score)
-        relevance = get_relevance_level(score, max_score)
-        
-        # Apply filtering based on filter_level
-        opacity = '1' if filter_level == 'all' or relevance == filter_level else '0.3'
         
         # Create regex pattern for whole word matching (case-insensitive)
         pattern = r'\b' + re.escape(keyword) + r'\b'
         
         # Replace with highlighted version
         replacement = f'<span style="background-color: {color}; padding: 2px 4px; ' \
-                     f'border-radius: 3px; margin: 0 1px; opacity: {opacity}; ' \
+                     f'border-radius: 3px; margin: 0 1px; ' \
                      f'border: 1px solid {color}; color: white; font-weight: bold;" ' \
                      f'title="Score: {score:.3f}">{keyword}</span>'
         
@@ -497,8 +489,8 @@ def create_highlighted_html(text, keywords, filter_level='all'):
     </div>
     """
 
-def create_keyword_table_html(keywords, filter_level='all'):
-    """Create HTML table for keywords with filtering capability"""
+def create_keyword_table_html(keywords):
+    """Create HTML table for keywords"""
     if not keywords:
         return "<p style='text-align: center; padding: 20px;'>No keywords found.</p>"
     
@@ -506,18 +498,6 @@ def create_keyword_table_html(keywords, filter_level='all'):
     sorted_keywords = sorted(keywords, key=lambda x: x['score'], reverse=True)
     max_score = sorted_keywords[0]['score'] if sorted_keywords else 1
     
-    # Filter keywords based on filter_level
-    if filter_level != 'all':
-        filtered_keywords = []
-        for kw in sorted_keywords:
-            relevance = get_relevance_level(kw['score'], max_score)
-            if relevance == filter_level:
-                filtered_keywords.append(kw)
-        sorted_keywords = filtered_keywords
-    
-    if not sorted_keywords:
-        return f"<p style='text-align: center; padding: 20px;'>No {filter_level} relevance keywords found.</p>"
-    
     table_html = """
     <div style='max-height: 600px; overflow-y: auto; border: 2px solid #ddd; border-radius: 8px; padding: 20px; background-color: #fafafa;'>
         <h3 style="margin: 0 0 20px 0;">🎯 Extracted Keywords</h3>
@@ -575,7 +555,6 @@ def create_legend_html():
     html = """
     <div style='margin: 15px 0; padding: 15px; background-color: #f8f9fa; border-radius: 8px;'>
         <h4 style='margin: 0 0 15px 0;'>🎨 Relevance Score Legend</h4>
-        <p style='font-size: 14px; color: #666; margin-bottom: 15px;'>Use the radio buttons below to filter keywords by relevance level</p>
         <div style='display: flex; flex-wrap: wrap; gap: 15px;'>
             <span style='background-color: #00B894; padding: 4px 12px; border-radius: 15px; color: white; font-weight: bold;'>
                 High Relevance (70%+)
@@ -597,16 +576,12 @@ keyword_manager = KeywordExtractionManager()
 
 def process_text(text, selected_model, num_keywords, ngram_min, ngram_max, progress=gr.Progress()):
     """Main processing function for Gradio interface with progress tracking"""
-    global current_keywords, current_text
     
     if not text.strip():
-        return "❌ Please enter some text to analyse", "", "", gr.update(visible=False)
+        return "❌ Please enter some text to analyse", "", ""
     
     progress(0.1, desc="Initialising...")
     
-    # Store the text globally for filtering
-    current_text = text
-    
     # Extract keywords
     progress(0.2, desc="Extracting keywords...")
     keywords = keyword_manager.extract_keywords(
@@ -618,10 +593,7 @@ def process_text(text, selected_model, num_keywords, ngram_min, ngram_max, progr
     )
     
     if not keywords:
-        return "❌ No keywords found. Try adjusting the parameters.", "", "", gr.update(visible=False)
-    
-    # Store keywords globally for filtering
-    current_keywords = keywords
+        return "❌ No keywords found. Try adjusting the parameters.", "", ""
     
     progress(0.8, desc="Processing results...")
     
@@ -645,26 +617,7 @@ def process_text(text, selected_model, num_keywords, ngram_min, ngram_max, progr
     
     progress(1.0, desc="Complete!")
     
-    # Make filter controls visible
-    return summary, legend_html + highlighted_html, results_html, gr.update(visible=True)
-
-def filter_results(filter_level):
-    """Filter the results based on relevance level"""
-    global current_keywords, current_text
-    
-    if not current_keywords:
-        return "", ""
-    
-    # Update highlighted text
-    highlighted_html = create_highlighted_html(current_text, current_keywords, filter_level)
-    
-    # Update table
-    results_html = create_keyword_table_html(current_keywords, filter_level)
-    
-    # Add legend to highlighted output
-    legend_html = create_legend_html()
-    
-    return legend_html + highlighted_html, results_html
+    return summary, legend_html + highlighted_html, results_html
 
 # Create Gradio interface
 def create_interface():
@@ -679,7 +632,6 @@ def create_interface():
         2. **🎯 Select a model** from the dropdown for keyword extraction
         3. **⚙️ Adjust parameters** (number of keywords, n-gram range)
         4. **🔍 Click "Extract Keywords"** to see results with organized output
-        5. **🎨 Use the filter buttons** to show keywords by relevance level
         """)
         
         # Add tip box
@@ -769,21 +721,6 @@ def create_interface():
         
         extract_btn = gr.Button("🔍 Extract Keywords", variant="primary", size="lg")
         
-        # Filter controls (initially hidden)
-        with gr.Row(visible=False) as filter_row:
-            gr.Markdown("### 🎯 Filter by Relevance Level:")
-            filter_radio = gr.Radio(
-                choices=[
-                    ("Show All", "all"),
-                    ("High Relevance (70%+)", "high"),
-                    ("Medium Relevance (40-70%)", "medium"),
-                    ("Low Relevance (<40%)", "low")
-                ],
-                value="all",
-                label="",
-                interactive=True
-            )
-        
         # Output sections
         with gr.Row():
             summary_output = gr.Markdown(label="Summary")
@@ -807,14 +744,7 @@ def create_interface():
                 ngram_min,
                 ngram_max
             ],
-            outputs=[summary_output, highlighted_output, results_output, filter_row]
-        )
-        
-        # Connect filter radio to filter function
-        filter_radio.change(
-            fn=filter_results,
-            inputs=[filter_radio],
-            outputs=[highlighted_output, results_output]
+            outputs=[summary_output, highlighted_output, results_output]
         )
         
         gr.Examples(