Update README.md

another readme update

README.md

@@ -51,35 +51,35 @@ def predict_emotions(text, model_name, threshold=0.35):
         probabilities = torch.sigmoid(logits).numpy()[0]
     
     # Map probabilities to emotions
-    emotions = {{emotion: float(prob) for emotion, prob in zip(model.config.id2label.values(), probabilities)}}
+    emotions = {emotion: float(prob) for emotion, prob in zip(model.config.id2label.values(), probabilities)}
     
     # Get emotions above threshold and sort by probability
     predicted_emotions = [(emotion, prob) for emotion, prob in emotions.items() if prob >= threshold]
     predicted_emotions.sort(key=lambda x: x[1], reverse=True)
     
-    return {{
-        "text": {{text}},
-        "predicted_emotions": {{predicted_emotions}},
-        "all_probabilities": {{dict(sorted(emotions.items(), key=lambda x: x[1], reverse=True))}},
-        "threshold_used": {{threshold}}
-    }}
+    return {
+        "text": text,
+        "predicted_emotions": predicted_emotions,
+        "all_probabilities": dict(sorted(emotions.items(), key=lambda x: x[1], reverse=True)),
+        "threshold_used": threshold
+    }
 
 # Example usage
 result = predict_emotions(
     "I'm feeling really excited and happy about this news!", 
-    "model-name",
+    "your-username/model-name",
     threshold=0.35  # Customize threshold here
 )
 
 # Print results
-print(f"Text: {{result['text']}}")
+print(f"Text: {result['text']}")
 print("\nDetected emotions (sorted by probability):")
 for emotion, prob in result['predicted_emotions']:
-    print(f"  - {{emotion.upper()}} ({{prob:.4f}})")
+    print(f"  - {emotion.upper()} ({prob:.4f})")
 
 print("\nAll emotion probabilities (sorted):")
 for emotion, prob in result['all_probabilities'].items():
-    print(f"  {{'*' if prob >= result['threshold_used'] else ' '}} {{emotion}}: {{prob:.4f}}")
+    print(f"  {'✓' if prob >= result['threshold_used'] else ' '} {emotion}: {prob:.4f}")
 ```