Create app.py

app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Define emotion colors for visualization
+EMOTION_COLORS = {
+    'anger': '#E74C3C',     # Red
+    'joy': '#F1C40F',       # Yellow
+    'love': '#E91E63',      # Pink
+    'sadness': '#3498DB',   # Blue
+    'fear': '#7D3C98',      # Purple
+    'surprise': '#2ECC71'   # Green
+}
+
+# Load model and tokenizer
+print("Loading model and tokenizer...")
+model_name = "bhadresh-savani/bert-base-uncased-emotion"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Get emotion labels
+id2label = model.config.id2label
+emotions_list = list(id2label.values())
+print(f"Model loaded. Emotions: {emotions_list}")
+
+def analyze_emotion(text):
+    """Analyze emotion in text and return visualization and results"""
+    if not text or not text.strip():
+        return None, {"error": "Please enter some text to analyze"}
+    
+    try:
+        # Tokenize and get prediction
+        inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+        
+        with torch.no_grad():
+            outputs = model(**inputs)
+            scores = torch.nn.functional.softmax(outputs.logits, dim=1).squeeze().numpy()
+        
+        # Get emotion labels and their scores
+        emotion_scores = [(id2label[i], float(scores[i])) for i in range(len(scores))]
+        
+        # Sort by scores
+        emotion_scores.sort(key=lambda x: x[1], reverse=True)
+        emotions, scores = zip(*emotion_scores)
+        
+        # Create visualization
+        fig = create_visualization(emotions, scores, text)
+        
+        # Format output
+        output = {
+            "dominant_emotion": emotions[0],
+            "confidence": f"{scores[0]*100:.1f}%",
+            "detailed_scores": {emotion: f"{score*100:.1f}%" for emotion, score in zip(emotions, scores)}
+        }
+        
+        return fig, output
+        
+    except Exception as e:
+        print(f"Error in analyze_emotion: {str(e)}")
+        return None, {"error": f"Analysis failed: {str(e)}"}
+
+def create_visualization(emotions, scores, text=None):
+    """Create a bar chart visualization of emotion scores"""
+    fig, ax = plt.subplots(figsize=(10, 6))
+    
+    # Use custom colors for the bars
+    colors = [EMOTION_COLORS.get(emotion, '#1f77b4') for emotion in emotions]
+    
+    # Create horizontal bar chart
+    y_pos = np.arange(len(emotions))
+    ax.barh(y_pos, [score * 100 for score in scores], color=colors)
+    ax.set_yticks(y_pos)
+    ax.set_yticklabels(emotions)
+    ax.invert_yaxis()  # Labels read top-to-bottom
+    ax.set_xlabel('Confidence (%)')
+    
+    # Add value labels to the bars
+    for i, v in enumerate(scores):
+        ax.text(v * 100 + 1, i, f"{v*100:.1f}%", va='center')
+    
+    # Set title with truncated text if provided
+    if text:
+        display_text = text if len(text) < 50 else text[:47] + "..."
+        ax.set_title(f'Emotion Analysis: "{display_text}"', pad=20)
+    else:
+        ax.set_title('Emotion Analysis', pad=20)
+    
+    plt.tight_layout()
+    return fig
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=analyze_emotion,
+    inputs=gr.Textbox(lines=4, placeholder="Enter text to analyze emotions...", label="Input Text"),
+    outputs=[gr.Plot(label="Emotion Distribution"), gr.JSON(label="Analysis Results")],
+    title="🧠 Emotion Analysis App",
+    description="This app analyzes emotions in text using BERT. It can detect six emotions: anger, joy, love, sadness, fear, and surprise.",
+    examples=[
+        ["I'm so excited about this new opportunity!"],
+        ["The news made me very sad and disappointed."],
+        ["I can't believe what just happened! This is totally unexpected!"],
+        ["I'm really angry about how they treated me."],
+        ["I love spending time with my family and friends."],
+        ["I'm terrified of what might happen next."]
+    ]
+)
+
+# Launch the app
+demo.launch()