update

ui/analysis_screen.py

@@ -10,44 +10,130 @@ from processors.bow_analysis import compare_bow
 # from processors.metrics import calculate_similarity
 # from processors.diff_highlighter import highlight_differences
 # Add this import at the top
-from processors.text_classifiers import classify_formality, classify_sentiment, classify_complexity, compare_classifications
+from analysis.text_classifiers import classify_formality, classify_sentiment, classify_complexity, compare_classifications
 
 def create_analysis_screen():
     """
-    Create the UI components for the analysis options screen.
+    Create the analysis options screen
     
     Returns:
-        tuple: The analysis UI components
+        tuple: (analysis_options, analysis_params, run_analysis_btn, analysis_output, bow_top_slider, ngram_n, ngram_top, topic_count)
     """
-    with gr.Column() as analysis_container:
+    with gr.Column() as analysis_screen:
         gr.Markdown("## Analysis Options")
+        gr.Markdown("Select which analysis you want to run on the LLM responses.")
         
-        # Change from checkboxes to radio buttons for analysis type
-        analysis_options = gr.Radio(
-            choices=["Bag of Words", "N-gram Analysis", "Topic Modeling", "Classifier"],
-            label="Analysis Type",
-            value="Bag of Words"
+        # Change from CheckboxGroup to Radio for analysis selection
+        with gr.Group():
+            analysis_options = gr.Radio(
+                choices=[
+                    "Bag of Words",
+                    "N-gram Analysis",
+                    "Topic Modeling",
+                    "Bias Detection",
+                    "Classifier",  # New option for future development
+                    "LLM Analysis"  # New option for future development
+                ],
+                value="Bag of Words",  # Default selection
+                label="Select Analysis Type"
+            )
+        
+        # Create slider directly here for easier access
+        gr.Markdown("### Bag of Words Parameters")
+        bow_top_slider = gr.Slider(
+            minimum=10, maximum=100, value=25, step=5, 
+            label="Top Words to Compare", 
+            elem_id="bow_top_slider"
+        )
+        
+        # Create N-gram parameters accessible at top level
+        ngram_n = gr.Radio(
+            choices=["1", "2", "3"], value="2", 
+            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
         )
         
-        # Parameters for different analysis types
-        with gr.Column() as analysis_params:
-            bow_top_slider = gr.Slider(minimum=5, maximum=50, step=5, value=20, 
-                                      label="Number of top words to display (Bag of Words)")
+        # Create topic modeling parameter accessible at top level
+        topic_count = gr.Slider(
+            minimum=2, maximum=10, value=3, step=1,
+            label="Number of Topics",
+            visible=False
+        )
+        
+        # Parameters for each analysis type
+        with gr.Group() as analysis_params:
+            # Topic modeling parameters
+            with gr.Group(visible=False) as topic_params:
+                gr.Markdown("### Topic Modeling Parameters")
+                # We'll use the topic_count defined above
             
-            ngram_n = gr.Slider(minimum=1, maximum=5, step=1, value=2, 
-                               label="N-gram size")
+            # N-gram parameters group (using external ngram_n and 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
+                
+            # Bias detection parameters
+            with gr.Group(visible=False) as bias_params:
+                gr.Markdown("### Bias Detection Parameters")
+                bias_methods = gr.CheckboxGroup(
+                    choices=["Sentiment Analysis", "Partisan Leaning", "Framing Analysis"],
+                    value=["Sentiment Analysis", "Partisan Leaning"],
+                    label="Bias Detection Methods"
+                )
             
-            ngram_top = gr.Slider(minimum=5, maximum=50, step=5, value=15, 
-                                 label="Number of top n-grams to display")
+            # Classifier parameters for future development
+            with gr.Group(visible=False) as classifier_params:
+                gr.Markdown("### Classifier Parameters")
+                gr.Markdown("*Classifier options will be available in a future update*")
             
-            topic_count = gr.Slider(minimum=2, maximum=10, step=1, value=3,
-                                   label="Number of topics (Topic Modeling)")
-        
-        run_analysis_btn = gr.Button("Run Analysis", size="lg", variant="primary")
+            # LLM Analysis parameters for future development
+            with gr.Group(visible=False) as llm_params:
+                gr.Markdown("### LLM Analysis Parameters")
+                gr.Markdown("*LLM Analysis options will be available in a future update*")
+                
+            # Function to update parameter visibility based on selected analysis
+            def update_params_visibility(selected):
+                return {
+                    topic_params: gr.update(visible=selected == "Topic Modeling"),
+                    ngram_params: gr.update(visible=selected == "N-gram Analysis"),
+                    bias_params: gr.update(visible=selected == "Bias Detection"),
+                    classifier_params: gr.update(visible=selected == "Classifier"),
+                    llm_params: gr.update(visible=selected == "LLM Analysis"),
+                    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"),
+                    bow_top_slider: gr.update(visible=selected == "Bag of Words")
+                }
+                
+            # Set up event handler for analysis selection
+            analysis_options.change(
+                fn=update_params_visibility,
+                inputs=[analysis_options],
+                outputs=[
+                    topic_params, 
+                    ngram_params, 
+                    bias_params, 
+                    classifier_params,
+                    llm_params,
+                    ngram_n, 
+                    ngram_top, 
+                    topic_count,
+                    bow_top_slider
+                ]
+            )
         
-        # Output area - JSON view for debugging or advanced users
-        analysis_output = gr.JSON(value={}, visible=False, label="Raw Analysis Results")
+        # Run analysis button
+        run_analysis_btn = gr.Button("Run Analysis", variant="primary", size="large")
         
+        # 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
     return analysis_options, analysis_params, run_analysis_btn, analysis_output, bow_top_slider, ngram_n, ngram_top, topic_count
 
 # Process analysis request function