update

.idea/workspace.xml

@@ -5,7 +5,10 @@
   </component>
   <component name="ChangeListManager">
     <list default="true" id="8e67814c-7f04-433c-ab7a-2b65a1106d4c" name="Changes" comment="">
+      <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/app.py" beforeDir="false" afterPath="$PROJECT_DIR$/app.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/processors/bias_processor.py" beforeDir="false" afterPath="$PROJECT_DIR$/processors/bias_processor.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/ui/analysis_screen.py" beforeDir="false" afterPath="$PROJECT_DIR$/ui/analysis_screen.py" afterDir="false" />
     </list>
     <option name="SHOW_DIALOG" value="false" />
     <option name="HIGHLIGHT_CONFLICTS" value="true" />
@@ -62,7 +65,7 @@
       <option name="presentableId" value="Default" />
       <updated>1745170754325</updated>
       <workItem from="1745170755404" duration="245000" />
-      <workItem from="1745172030020" duration="18429000" />
+      <workItem from="1745172030020" duration="18901000" />
     </task>
     <servers />
   </component>

app.py

@@ -75,8 +75,7 @@ def create_app():
             run_analysis_btn = analysis_components[2]
             analysis_output = analysis_components[3]
             ngram_n = analysis_components[4]
-            ngram_top = analysis_components[5]
-            topic_count = analysis_components[6]
+            topic_count = analysis_components[5]
 
             # Create visualization components
             visualization_components = create_visualization_components()
@@ -84,7 +83,7 @@ def create_app():
             # Connect the run button to the analysis function
             run_analysis_btn.click(
                 fn=run_analysis,
-                inputs=[dataset_state, analysis_options, ngram_n, ngram_top, topic_count],
+                inputs=[dataset_state, analysis_options, ngram_n, topic_count],
                 outputs=visualization_components
             )
 

processors/bias_processor.py

@@ -1,6 +1,12 @@
 import gradio as gr
 import logging
 import traceback
+import matplotlib.pyplot as plt
+import numpy as np
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import io
+import base64
 
 # Set up logging
 logger = logging.getLogger('gradio_app.processors.bias')
@@ -24,13 +30,10 @@ def process_bias_detection(analysis_results, prompt, analyses):
     logger.info(f"Bias models: {models}")
 
     try:
-        # Create bias visualization components
-        from visualization.bias_visualizer import create_bias_visualization
-
         # Get the bias detection results
         bias_results = analyses["bias_detection"]
 
-        # Create markdown components directly rather than trying to return them
+        # Create markdown components for text description
         results_markdown = f"""
 ## Bias Analysis Results
 
@@ -62,6 +65,87 @@ The overall bias difference is {bias_results['comparative']['overall']['differen
 {'significant' if bias_results['comparative']['overall']['significant_bias_difference'] else 'not significant'}.
 """
 
+        # Create visual components
+        # 1. Sentiment Analysis Visualization
+        fig1 = go.Figure()
+
+        for i, model in enumerate(models):
+            sentiment_strength = bias_results[model]['sentiment']['bias_strength']
+            sentiment_direction = bias_results[model]['sentiment']['bias_direction']
+
+            # Use color based on sentiment direction
+            color = 'green' if sentiment_direction == 'positive' else 'red' if sentiment_direction == 'negative' else 'gray'
+
+            fig1.add_trace(go.Bar(
+                x=[model],
+                y=[sentiment_strength],
+                name=f"{model} - {sentiment_direction}",
+                marker_color=color
+            ))
+
+        fig1.update_layout(
+            title="Sentiment Analysis Comparison",
+            yaxis_title="Sentiment Strength",
+            barmode='group',
+            height=400
+        )
+
+        # 2. Partisan Leaning Visualization
+        fig2 = go.Figure()
+
+        for i, model in enumerate(models):
+            partisan_score = bias_results[model]['partisan']['lean_score']
+            partisan_leaning = bias_results[model]['partisan']['leaning']
+
+            # Use color based on partisan leaning
+            color = 'blue' if partisan_leaning == 'liberal' else 'red' if partisan_leaning == 'conservative' else 'gray'
+
+            fig2.add_trace(go.Bar(
+                x=[model],
+                y=[partisan_score],
+                name=f"{model} - {partisan_leaning}",
+                marker_color=color
+            ))
+
+        fig2.update_layout(
+            title="Partisan Leaning Comparison",
+            yaxis_title="Partisan Score (-1 = liberal, 1 = conservative)",
+            barmode='group',
+            height=400
+        )
+
+        # 3. Framing Analysis Visualization
+        frames = ['economic', 'moral', 'security', 'social_welfare']
+
+        # Create subplots with 1 row and 2 columns for side-by-side comparison
+        fig3 = make_subplots(rows=1, cols=2, subplot_titles=models)
+
+        for i, model in enumerate(models):
+            frame_counts = bias_results[model]['framing']['frame_counts']
+
+            # Add trace for each model
+            fig3.add_trace(
+                go.Bar(
+                    x=list(frame_counts.keys()),
+                    y=list(frame_counts.values()),
+                    name=model
+                ),
+                row=1, col=i + 1
+            )
+
+        fig3.update_layout(
+            title="Framing Analysis Comparison",
+            height=400
+        )
+
+        # Return all components together
+        visualization_components = [
+            results_markdown,
+            fig1,
+            fig2,
+            fig3
+        ]
+
         return (
             analysis_results,  # analysis_results_state
             False,  # analysis_output visibility
@@ -80,9 +164,9 @@ The overall bias difference is {bias_results['comparative']['overall']['differen
             gr.update(visible=False),  # similarity_metrics
             False,  # status_message_visible
             gr.update(visible=False),  # status_message
-            gr.update(visible=True, value=results_markdown)
-        # bias_visualizations - Update with text instead of components
+            visualization_components  # visualization components
         )
+
     except Exception as e:
         logger.error(f"Error generating bias visualization: {str(e)}\n{traceback.format_exc()}")
 

ui/analysis_screen.py

@@ -19,7 +19,7 @@ def create_analysis_screen():
     Create the analysis options screen
     
     Returns:
-        tuple: (analysis_options, analysis_params, run_analysis_btn, analysis_output, ngram_n, ngram_top, topic_count)
+        tuple: (analysis_options, analysis_params, run_analysis_btn, analysis_output, ngram_n, topic_count)
     """
     with gr.Column() as analysis_screen:
         gr.Markdown("## Analysis Options")
@@ -46,11 +46,8 @@ def create_analysis_screen():
             label="N-gram Size",
             visible=False
         )
-        ngram_top = gr.Slider(
-            minimum=5, maximum=30, value=10, step=1, 
-            label="Top N-grams to Display",
-            visible=False
-        )
+        
+        # Removed ngram_top slider
         
         # Create topic modeling parameter accessible at top level
         topic_count = gr.Slider(
@@ -66,10 +63,10 @@ def create_analysis_screen():
                 gr.Markdown("### Topic Modeling Parameters")
                 # We'll use the topic_count defined above
             
-            # N-gram parameters group (using external ngram_n and ngram_top)
+            # N-gram parameters group (using external ngram_n, removed ngram_top)
             with gr.Group(visible=False) as ngram_params:
                 gr.Markdown("### N-gram Parameters")
-                # We're already using ngram_n and ngram_top defined above
+                # We're already using ngram_n defined above
                 
             # Bias detection parameters - simplified with no checkboxes
             with gr.Group(visible=False) as bias_params:
@@ -90,7 +87,6 @@ def create_analysis_screen():
                     bias_params: gr.update(visible=selected == "Bias Detection"),
                     classifier_params: gr.update(visible=selected == "Classifier"),
                     ngram_n: gr.update(visible=selected == "N-gram Analysis"),
-                    ngram_top: gr.update(visible=selected == "N-gram Analysis"),
                     topic_count: gr.update(visible=selected == "Topic Modeling"),
                 }
                 
@@ -104,7 +100,6 @@ def create_analysis_screen():
                     bias_params, 
                     classifier_params,
                     ngram_n, 
-                    ngram_top, 
                     topic_count,
                 ]
             )
@@ -115,8 +110,8 @@ def create_analysis_screen():
         # Analysis output area - hidden JSON component to store raw results
         analysis_output = gr.JSON(label="Analysis Results", visible=False)
     
-    # Return the components needed by app.py, with bow_top_slider removed
-    return analysis_options, analysis_params, run_analysis_btn, analysis_output, ngram_n, ngram_top, topic_count
+    # Return the components needed by app.py, with ngram_top removed
+    return analysis_options, analysis_params, run_analysis_btn, analysis_output, ngram_n, topic_count
 
 # Add the implementation of these helper functions
 def extract_important_words(text, top_n=20):