Create app.py

app.py

@@ -267,28 +267,40 @@ class FallacyFinder:
         # Format confidence as percentage
         confidence_percent = f"{confidence * 100:.1f}%"
         
-        # Determine confidence level
+        # Determine confidence level and colors
         if confidence >= 0.8:
-            confidence_level = "🔴 High Confidence"
+            confidence_level = "🔵 High Confidence"
             confidence_color = "high"
         elif confidence >= 0.6:
-            confidence_level = "🟡 Medium Confidence"
+            confidence_level = "🟣 Medium Confidence"
             confidence_color = "medium"
         else:
             confidence_level = "🟢 Low Confidence"
             confidence_color = "low"
         
-        # Format the main result
+        # Format the main result with better structure
         if predicted_label == 'no_fallacy':
-            result = f"✅ **No Fallacy Detected**\n\n{confidence_level} ({confidence_percent})"
-            icon = "✅"
+            result = f"✅ **No Fallacy Detected**\n\n**Confidence:** {confidence_level} ({confidence_percent})"
         else:
-            result = f"⚠️ **{fallacy_name}**\n\n{confidence_level} ({confidence_percent})"
-            icon = "⚠️"
-        
-        # Add detected patterns for debugging
-        if patterns:
-            result += f"\n\n**Debug Info:** {patterns[0] if patterns else 'No patterns'}"
+            result = f"⚠️ **{fallacy_name}**\n\n**Confidence:** {confidence_level} ({confidence_percent})"
+        
+        # Add top predictions in a clean list format
+        if patterns and patterns[0].startswith('model_prediction'):
+            # Extract top 3 predictions from debug info
+            try:
+                import ast
+                debug_str = patterns[0].replace('model_prediction: top_3: ', '')
+                top_predictions = ast.literal_eval(debug_str)
+                
+                result += f"\n\n**Top Predictions:**"
+                for i, (fallacy, score) in enumerate(top_predictions, 1):
+                    fallacy_display = self.fallacy_labels.get(fallacy, fallacy.replace('_', ' ').title())
+                    percentage = f"{float(score) * 100:.1f}%"
+                    result += f"\n{i}. {fallacy_display}: {percentage}"
+                    
+            except:
+                # Fallback if parsing fails
+                result += f"\n\n**Debug Info:** Model prediction analysis available in logs"
         
         # Format the explanation
         explanation = f"**What this means:**\n{description}"
@@ -322,33 +334,31 @@ def analyze_fallacy(message):
 
 # Create the Gradio interface
 with gr.Blocks(
-    theme=gr.themes.Soft(primary_hue="red", secondary_hue="orange"),
+    theme=gr.themes.Soft(primary_hue="blue", secondary_hue="indigo"),
     title="Fallacy Finder",
     css="""
     .gradio-container {
         max-width: 900px !important;
     }
     .high-confidence {
-        border-left: 4px solid #ef4444;
+        border-left: 4px solid #3b82f6;
     }
     .medium-confidence {
-        border-left: 4px solid #eab308;
+        border-left: 4px solid #8b5cf6;
     }
     .low-confidence {
-        border-left: 4px solid #22c55e;
+        border-left: 4px solid #10b981;
     }
     """
 ) as demo:
     
     gr.Markdown(
         """
-        # 🔍 Fallacy Finder (Debug Mode)
+        # 🔍 Fallacy Finder
         
         Analyze messages for logical fallacies and argumentative patterns. Enter any statement, argument, or message to identify potential fallacies and get suggestions for better communication.
         
         **Detects 15+ types of fallacies** including ad hominem, strawman, whataboutism, gaslighting, and more.
-        
-        **🐛 Debug Mode Active:** Check the console logs and fallacy_finder.log file for detailed analysis patterns.
         """
     )
     
@@ -419,9 +429,9 @@ with gr.Blocks(
     # Information section
     gr.Markdown(
         """
-        ## 🎯 How It Works
+        ### 🎯 How It Works
         
-        This tool analyzes text for common logical fallacies and argumentative patterns that can harm productive communication. It looks for:
+        This tool uses an AI model trained to detect logical fallacies and argumentative patterns that can harm productive communication. It analyzes:
         
         - **Personal attacks** instead of addressing arguments
         - **Misrepresentations** of opposing viewpoints  
@@ -435,15 +445,7 @@ with gr.Blocks(
         ✅ **Address the actual position being argued**  
         ✅ **Use evidence and logical reasoning**  
         ✅ **Acknowledge valid points from the other side**  
-        ✅ **Stay on topic and address concerns directly**  
-        
-        ### 🐛 Debug Information
-        
-        Check the console output or `fallacy_finder.log` file to see:
-        - Whether the trained model or rule-based system was used
-        - Specific patterns detected by rules
-        - Model prediction scores for all fallacy types
-        - Step-by-step analysis process
+        ✅ **Stay on topic and address concerns directly**
         """
     )