Update app.py

app.py

@@ -49,24 +49,13 @@ except Exception as e:
     print(f"⚠️ Error loading model: {e}")
     raise e
 
-# Optimized thresholds from training (you can update these after validation)
-BEST_THRESHOLDS = np.array([0.5, 0.5, 0.5, 0.5, 0.5])  # Default thresholds
+# Optimized thresholds from training
+BEST_THRESHOLDS = np.array([0.5, 0.5, 0.5, 0.5, 0.5])
 
 def predict_emotions(text):
-    """
-    Predict emotions from text
-    
-    Args:
-        text: Input text string
-        
-    Returns:
-        Dictionary with emotion predictions and probabilities
-    """
+    """Predict emotions from text"""
     if not text or not text.strip():
-        return {
-            "⚠️ Error": "Please enter some text to analyze",
-            "Detected Emotions": "None"
-        }
+        return "⚠️ Please enter some text to analyze"
     
     try:
         # Tokenize
@@ -91,32 +80,27 @@ def predict_emotions(text):
         
         # Format results
         detected = []
-        all_probs = {}
+        output = "## 🎯 Detected Emotions:\n\n"
         
-        for i, (emotion, emoji, prob, pred) in enumerate(zip(EMOTIONS, EMOTION_EMOJIS, probs, predictions)):
-            all_probs[f"{emoji} {emotion.capitalize()}"] = float(prob)
+        for emotion, emoji, prob, pred in zip(EMOTIONS, EMOTION_EMOJIS, probs, predictions):
             if pred == 1:
-                detected.append(f"{emoji} {emotion.capitalize()} ({prob:.1%})")
+                detected.append(f"{emoji} **{emotion.capitalize()}**")
         
-        if not detected:
-            detected_str = "No strong emotions detected (all probabilities below threshold)"
+        if detected:
+            output += ", ".join(detected) + "\n\n"
         else:
-            detected_str = " • ".join(detected)
+            output += "*No strong emotions detected (all below threshold)*\n\n"
         
-        result = {
-            "🎯 Detected Emotions": detected_str,
-            "📊 All Probabilities": all_probs,
-            "📝 Text Length": f"{len(text)} characters",
-            "🔍 Analysis": f"Analyzed with RoBERTa-base model"
-        }
+        output += "## 📊 All Probabilities:\n\n"
+        for emotion, emoji, prob in zip(EMOTIONS, EMOTION_EMOJIS, probs):
+            bar_length = int(prob * 20)
+            bar = "█" * bar_length + "░" * (20 - bar_length)
+            output += f"{emoji} **{emotion.capitalize()}**: {bar} {prob:.1%}\n\n"
         
-        return result
+        return output
         
     except Exception as e:
-        return {
-            "⚠️ Error": f"Prediction failed: {str(e)}",
-            "Detected Emotions": "Error"
-        }
+        return f"⚠️ Error: {str(e)}"
 
 # Example texts
 examples = [
@@ -129,17 +113,14 @@ examples = [
 ]
 
 # Create Gradio Interface
-with gr.Blocks(theme=gr.themes.Soft(), title="Multi-Label Emotion Detection") as demo:
+with gr.Blocks() as demo:
     gr.Markdown(
         """
         # 😊 Multi-Label Emotion Classification
         
-        This model detects **multiple emotions** in text using a fine-tuned RoBERTa transformer.
-        
-        ### Emotions Detected:
-        😠 Anger | 😨 Fear | 😊 Joy | 😢 Sadness | 😲 Surprise
+        Detect **multiple emotions** in text using a fine-tuned RoBERTa transformer.
         
-        ---
+        **Emotions:** 😠 Anger | 😨 Fear | 😊 Joy | 😢 Sadness | 😲 Surprise
         """
     )
     
@@ -148,25 +129,18 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Multi-Label Emotion Detection") as
             text_input = gr.Textbox(
                 label="Enter your text",
                 placeholder="Type or paste text here to analyze emotions...",
-                lines=5,
-                max_lines=10
+                lines=5
             )
-            
-            with gr.Row():
-                clear_btn = gr.Button("🗑️ Clear", variant="secondary")
-                analyze_btn = gr.Button("🔮 Analyze Emotions", variant="primary", scale=2)
+            analyze_btn = gr.Button("🔮 Analyze Emotions", variant="primary")
+            clear_btn = gr.Button("🗑️ Clear")
         
         with gr.Column():
-            output_json = gr.JSON(
-                label="📊 Analysis Results",
-                show_label=True
-            )
+            output = gr.Markdown(label="Analysis Results")
     
-    gr.Markdown("### 💡 Try these examples:")
     gr.Examples(
         examples=examples,
         inputs=text_input,
-        outputs=output_json,
+        outputs=output,
         fn=predict_emotions,
         cache_examples=False
     )
@@ -174,86 +148,31 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Multi-Label Emotion Detection") as
     gr.Markdown(
         """
         ---
-        ### 📈 How It Works
+        ## 📈 How It Works
         
-        This is a **multi-label classification** model, meaning:
-        - Each text can have **multiple emotions** simultaneously
-        - For example, "I'm excited but nervous" → Both Joy ✅ and Fear ✅
-        - Each emotion is predicted independently with a probability score
-        - Emotions above the threshold are marked as "detected"
+        This is a **multi-label classification** model - each text can have multiple emotions!
         
         ### 🎯 Model Details
+        - **Architecture**: RoBERTa-base (125M parameters)
+        - **Max Length**: 200 tokens
+        - **Training**: BCE Loss + Label Smoothing (0.05)
+        - **Evaluation**: Macro F1-Score with per-class threshold tuning
         
-        | Component | Details |
-        |-----------|---------|
-        | **Architecture** | RoBERTa-base (125M parameters) |
-        | **Training Data** | Multi-label emotion dataset |
-        | **Max Sequence Length** | 200 tokens |
-        | **Evaluation Metric** | Macro F1-Score |
-        | **Framework** | PyTorch + Transformers |
-        
-        ### 🏗️ Architecture
+        ### 🏗️ Architecture Flow
         ```
-        Input Text
-            ↓
-        RoBERTa Tokenizer (BPE)
-            ↓
-        RoBERTa Encoder (12 layers)
-            ↓
-        [CLS] Token Pooling
-            ↓
-        Dropout (0.35)
-            ↓
-        Linear Layer (768 → 5)
-            ↓
-        Sigmoid Activation
-            ↓
-        5 Emotion Probabilities
+        Input Text → Tokenizer → RoBERTa Encoder → [CLS] Pooling → 
+        Dropout (0.35) → Linear (768→5) → Sigmoid → 5 Emotion Probabilities
         ```
         
-        ### ⚙️ Training Configuration
-        - **Optimizer**: AdamW (lr=2e-5, weight_decay=0.02)
-        - **Scheduler**: Linear warmup (10% of steps)
-        - **Loss Function**: BCE with Logits + Label Smoothing (0.05)
-        - **Batch Size**: 8 (with 4x gradient accumulation = effective 32)
-        - **Epochs**: 8 (with early stopping, patience=3)
-        - **Validation**: Threshold tuning per emotion class
-        
-        ### 📊 Performance Optimization
-        - **Stratified split** by label distribution
-        - **Per-class threshold tuning** for optimal F1-score
-        - **Label smoothing** to prevent overconfidence
-        - **Early stopping** to prevent overfitting
-        
         ---
-        
-        ### 🔗 Resources
-        - **Model**: RoBERTa-base ([Hugging Face](https://huggingface.co/roberta-base))
-        - **Framework**: PyTorch + Transformers
-        - **Project**: 2025 Sep DLGenAI Course
-        
-        ---
-        
-        *Built with ❤️ using PyTorch, Transformers, and Gradio*
+        **Project**: 2025 Sep DLGenAI Course | **Built with**: PyTorch + Transformers + Gradio
         """
     )
     
-    # Button actions
-    analyze_btn.click(
-        fn=predict_emotions,
-        inputs=text_input,
-        outputs=output_json
-    )
-    
-    clear_btn.click(
-        fn=lambda: ("", None),
-        inputs=None,
-        outputs=[text_input, output_json]
-    )
+    # Event handlers
+    analyze_btn.click(fn=predict_emotions, inputs=text_input, outputs=output)
+    clear_btn.click(fn=lambda: ("", ""), inputs=None, outputs=[text_input, output])
+    text_input.submit(fn=predict_emotions, inputs=text_input, outputs=output)
 
-# Launch the app
 if __name__ == "__main__":
-    demo.launch(
-        share=False,
-        show_error=True
-    )
+    demo.launch()