Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import numpy as np
+
+class FallacyFinder:
+    def __init__(self):
+        # Fallacy labels mapping
+        self.fallacy_labels = {
+            'ad_hominem': 'Ad Hominem (Personal Attack)',
+            'strawman': 'Strawman (Misrepresenting Argument)', 
+            'whataboutism': 'Whataboutism (Deflecting with Counter-Accusations)',
+            'gaslighting': 'Gaslighting (Making Someone Question Reality)',
+            'false_dichotomy': 'False Dichotomy (Only Two Options When More Exist)',
+            'appeal_to_emotion': 'Appeal to Emotion (Using Emotions Instead of Logic)',
+            'darvo': 'DARVO (Deny, Attack, Reverse Victim/Offender)',
+            'moving_goalposts': 'Moving Goalposts (Changing Criteria When Challenged)',
+            'cherry_picking': 'Cherry Picking (Selecting Only Supporting Evidence)',
+            'appeal_to_authority': 'Appeal to Authority (Inappropriate Use of Authority)',
+            'slippery_slope': 'Slippery Slope (One Thing Leads to Extreme Consequences)',
+            'motte_and_bailey': 'Motte and Bailey (Defending Weak Position with Stronger One)',
+            'gish_gallop': 'Gish Gallop (Overwhelming with Many Weak Arguments)',
+            'kafkatrapping': 'Kafkatrapping (Denial Proves Guilt)',
+            'sealioning': 'Sealioning (Persistent Bad-Faith Requests for Evidence)',
+            'no_fallacy': 'No Fallacy (Clean, Logical Statement)'
+        }
+        
+        # Fallacy descriptions
+        self.fallacy_descriptions = {
+            'ad_hominem': "Attacking the person making the argument rather than addressing the argument itself.",
+            'strawman': "Misrepresenting someone's argument to make it easier to attack.",
+            'whataboutism': "Deflecting criticism by pointing out similar issues elsewhere instead of addressing the original concern.",
+            'gaslighting': "Making someone question their own memory, perception, or sanity.",
+            'false_dichotomy': "Presenting only two options when more alternatives exist.",
+            'appeal_to_emotion': "Using emotional manipulation instead of logical reasoning to persuade.",
+            'darvo': "A manipulation tactic: Deny the abuse, Attack the victim, Reverse Victim and Offender roles.",
+            'moving_goalposts': "Changing the criteria for evidence or success when the original criteria are met.",
+            'cherry_picking': "Selecting only data or examples that support your position while ignoring contradictory evidence.",
+            'appeal_to_authority': "Citing an authority figure inappropriately to support an argument.",
+            'slippery_slope': "Arguing that one small step will lead to a chain of negative consequences.",
+            'motte_and_bailey': "Switching between a defensible position (motte) and a controversial one (bailey).",
+            'gish_gallop': "Overwhelming an opponent with many rapid-fire arguments, regardless of their quality.",
+            'kafkatrapping': "A logical trap where denial of guilt is taken as evidence of guilt.",
+            'sealioning': "Persistently requesting evidence or debate in a way that appears reasonable but is actually harassment.",
+            'no_fallacy': "The statement appears to be logically sound and free of common fallacies."
+        }
+        
+        # Try to load model, fallback to rule-based if not available
+        self.model = None
+        self.tokenizer = None
+        self.use_model = False
+        
+        try:
+            # This would be your trained model path
+            # For now, we'll use a rule-based approach
+            # self.tokenizer = AutoTokenizer.from_pretrained("your-model-path")
+            # self.model = AutoModelForSequenceClassification.from_pretrained("your-model-path")
+            # self.use_model = True
+            pass
+        except:
+            print("Model not found, using rule-based approach")
+    
+    def predict_with_rules(self, text):
+        """Rule-based fallacy detection for when model isn't available"""
+        text_lower = text.lower().strip()
+        
+        # Ad Hominem patterns
+        ad_hominem_patterns = [
+            'you\'re', 'you are', 'stupid', 'idiot', 'moron', 'dumb', 'ignorant',
+            'loser', 'pathetic', 'crazy', 'insane', 'nuts'
+        ]
+        if any(pattern in text_lower for pattern in ad_hominem_patterns) and ('you' in text_lower):
+            return 'ad_hominem', 0.85
+        
+        # Strawman patterns
+        strawman_patterns = [
+            'so you\'re saying', 'so you think', 'so you believe', 'so you want',
+            'according to you', 'you\'re advocating', 'you want to destroy'
+        ]
+        if any(pattern in text_lower for pattern in strawman_patterns):
+            return 'strawman', 0.82
+        
+        # Whataboutism patterns
+        whataboutism_patterns = [
+            'what about', 'but what about', 'how about when', 'what about when you',
+            'but you', 'but when you'
+        ]
+        if any(pattern in text_lower for pattern in whataboutism_patterns):
+            return 'whataboutism', 0.88
+        
+        # Gaslighting patterns
+        gaslighting_patterns = [
+            'that never happened', 'you\'re imagining', 'you\'re being too sensitive',
+            'you\'re overreacting', 'that\'s not what i said', 'you\'re remembering wrong',
+            'you\'re being paranoid', 'you\'re being dramatic'
+        ]
+        if any(pattern in text_lower for pattern in gaslighting_patterns):
+            return 'gaslighting', 0.80
+        
+        # False Dichotomy patterns
+        false_dichotomy_patterns = [
+            'either you\'re', 'either we', 'you\'re either', 'it\'s either',
+            'with us or against us', 'love it or leave it'
+        ]
+        if any(pattern in text_lower for pattern in false_dichotomy_patterns):
+            return 'false_dichotomy', 0.78
+        
+        # Appeal to Emotion patterns
+        emotion_patterns = [
+            'think of the children', 'innocent people will', 'if you have any heart',
+            'how can you sleep', 'break the hearts', 'suffer needlessly'
+        ]
+        if any(pattern in text_lower for pattern in emotion_patterns):
+            return 'appeal_to_emotion', 0.83
+        
+        # DARVO patterns
+        darvo_patterns = [
+            'i never did that', 'you\'re the one who', 'i\'m the victim here',
+            'you\'re attacking me', 'i\'m innocent', 'you started this'
+        ]
+        if any(pattern in text_lower for pattern in darvo_patterns) and len([p for p in darvo_patterns if p in text_lower]) >= 2:
+            return 'darvo', 0.75
+        
+        # Moving Goalposts patterns
+        goalpost_patterns = [
+            'that doesn\'t count because', 'you need better sources', 'that\'s not enough evidence',
+            'those statistics don\'t count', 'that\'s different because'
+        ]
+        if any(pattern in text_lower for pattern in goalpost_patterns):
+            return 'moving_goalposts', 0.77
+        
+        # Appeal to Authority patterns
+        authority_patterns = [
+            'my doctor said', 'the expert said', 'the ceo thinks', 'einstein believed',
+            'my professor told me', 'the government says'
+        ]
+        if any(pattern in text_lower for pattern in authority_patterns):
+            return 'appeal_to_authority', 0.72
+        
+        # Slippery Slope patterns
+        slope_patterns = [
+            'if we allow this', 'this will lead to', 'give them an inch',
+            'this is just the beginning', 'where will it end', 'slippery slope'
+        ]
+        if any(pattern in text_lower for pattern in slope_patterns):
+            return 'slippery_slope', 0.74
+        
+        # Check for healthy communication (No Fallacy)
+        healthy_patterns = [
+            'i understand your perspective', 'i disagree because', 'based on the evidence',
+            'i appreciate your input', 'let\'s examine', 'i think we need more information',
+            'i see the merit', 'thank you for sharing'
+        ]
+        if any(pattern in text_lower for pattern in healthy_patterns):
+            return 'no_fallacy', 0.90
+        
+        # Default to no fallacy if nothing detected
+        return 'no_fallacy', 0.60
+    
+    def predict_fallacy(self, text):
+        """Main prediction function"""
+        if not text.strip():
+            return None, 0, "Please enter a message to analyze."
+        
+        if self.use_model and self.model is not None:
+            # Use trained model
+            inputs = self.tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+            
+            with torch.no_grad():
+                outputs = self.model(**inputs)
+                predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+                predicted_class_id = predictions.argmax().item()
+                confidence = predictions.max().item()
+            
+            # Map to fallacy label
+            label_keys = list(self.fallacy_labels.keys())
+            predicted_label = label_keys[predicted_class_id]
+            
+        else:
+            # Use rule-based approach
+            predicted_label, confidence = self.predict_with_rules(text)
+        
+        fallacy_name = self.fallacy_labels[predicted_label]
+        description = self.fallacy_descriptions[predicted_label]
+        
+        return predicted_label, confidence, fallacy_name, description
+    
+    def analyze_message(self, message):
+        """Analyze a message and return formatted results"""
+        if not message.strip():
+            return "Please enter a message to analyze.", "", ""
+        
+        predicted_label, confidence, fallacy_name, description = self.predict_fallacy(message)
+        
+        # Format confidence as percentage
+        confidence_percent = f"{confidence * 100:.1f}%"
+        
+        # Determine confidence level
+        if confidence >= 0.8:
+            confidence_level = "🔴 High Confidence"
+            confidence_color = "high"
+        elif confidence >= 0.6:
+            confidence_level = "🟡 Medium Confidence"
+            confidence_color = "medium"
+        else:
+            confidence_level = "🟢 Low Confidence"
+            confidence_color = "low"
+        
+        # Format the main result
+        if predicted_label == 'no_fallacy':
+            result = f"✅ **No Fallacy Detected**\n\n{confidence_level} ({confidence_percent})"
+            icon = "✅"
+        else:
+            result = f"⚠️ **{fallacy_name}**\n\n{confidence_level} ({confidence_percent})"
+            icon = "⚠️"
+        
+        # Format the explanation
+        explanation = f"**What this means:**\n{description}"
+        
+        # Add examples or tips based on fallacy type
+        if predicted_label == 'ad_hominem':
+            explanation += "\n\n**Better approach:** Focus on the argument itself rather than the person making it."
+        elif predicted_label == 'strawman':
+            explanation += "\n\n**Better approach:** Address the actual argument being made, not a distorted version of it."
+        elif predicted_label == 'whataboutism':
+            explanation += "\n\n**Better approach:** Address the original concern directly before discussing other issues."
+        elif predicted_label == 'gaslighting':
+            explanation += "\n\n**Better approach:** Acknowledge the other person's experience and work toward understanding."
+        elif predicted_label == 'no_fallacy':
+            explanation = "**Great communication!** This message appears to use logical reasoning and respectful language."
+        
+        return result, explanation, confidence_color
+
+# Initialize the fallacy finder
+finder = FallacyFinder()
+
+def analyze_fallacy(message):
+    return finder.analyze_message(message)
+
+# Create the Gradio interface
+with gr.Blocks(
+    theme=gr.themes.Soft(primary_hue="red", secondary_hue="orange"),
+    title="Fallacy Finder",
+    css="""
+    .gradio-container {
+        max-width: 900px !important;
+    }
+    .high-confidence {
+        border-left: 4px solid #ef4444;
+    }
+    .medium-confidence {
+        border-left: 4px solid #eab308;
+    }
+    .low-confidence {
+        border-left: 4px solid #22c55e;
+    }
+    """
+) as demo:
+    
+    gr.Markdown(
+        """
+        # 🔍 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.
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            message_input = gr.Textbox(
+                label="Enter a message to analyze",
+                placeholder="e.g., 'You're just saying that because you're too young to understand politics'",
+                lines=4,
+                info="Paste any statement, argument, or message you want to check for logical fallacies"
+            )
+            
+            analyze_btn = gr.Button("🔍 Analyze for Fallacies", variant="primary", size="lg")
+            
+        with gr.Column(scale=1):
+            gr.Markdown(
+                """
+                ### Quick Examples:
+                
+                **Ad Hominem:**  
+                *"You're too stupid to understand"*
+                
+                **Strawman:**  
+                *"So you want to destroy the economy?"*
+                
+                **Whataboutism:**  
+                *"What about when you did the same thing?"*
+                
+                **No Fallacy:**  
+                *"I disagree because the evidence shows..."*
+                """
+            )
+    
+    with gr.Row():
+        with gr.Column():
+            result_output = gr.Textbox(
+                label="Analysis Result",
+                lines=4,
+                interactive=False
+            )
+            
+            explanation_output = gr.Textbox(
+                label="Explanation & Suggestions",
+                lines=6,
+                interactive=False
+            )
+    
+    # Examples section
+    gr.Markdown("## 📋 Try These Examples")
+    
+    example_messages = [
+        "You're clearly too young and inexperienced to have a valid opinion on this topic",
+        "So you're saying we should just let criminals run free and destroy society?", 
+        "What about when you lied to me last year? Why should I trust you now?",
+        "That never happened, you're just being overly emotional and dramatic about nothing",
+        "Either you support the troops or you hate America - there's no middle ground",
+        "I understand your concerns, but based on the research I've seen, I think we should consider other options"
+    ]
+    
+    examples = gr.Examples(
+        examples=[[msg] for msg in example_messages],
+        inputs=[message_input],
+        outputs=[result_output, explanation_output],
+        fn=analyze_fallacy,
+        cache_examples=False
+    )
+    
+    # Information section
+    gr.Markdown(
+        """
+        ## 🎯 How It Works
+        
+        This tool analyzes text for common logical fallacies and argumentative patterns that can harm productive communication. It looks for:
+        
+        - **Personal attacks** instead of addressing arguments
+        - **Misrepresentations** of opposing viewpoints  
+        - **Emotional manipulation** instead of logical reasoning
+        - **Deflection tactics** that avoid the main issue
+        - **False choices** that ignore other options
+        
+        ### 💡 Tips for Better Arguments
+        
+        ✅ **Focus on the argument, not the person**  
+        ✅ **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**  
+        """
+    )
+    
+    # Connect the function
+    analyze_btn.click(
+        fn=analyze_fallacy,
+        inputs=[message_input],
+        outputs=[result_output, explanation_output]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch(
+        share=True,
+        server_name="0.0.0.0",
+        server_port=7860
+    )