Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import gradio as gr
+
+# Load pre-trained model and tokenizer
+model_name = "borisn70/bert-43-multilabel-emotion-detection"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Labels corresponding to different emotions
+labels = [
+    'admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 
+    'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 
+    'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 
+    'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'
+]
+
+# Function to predict emotions based on input text
+def predict_emotions(text):
+    inputs = tokenizer(text, return_tensors="pt")  # Tokenize the input text
+    with torch.no_grad():
+        logits = model(**inputs).logits  # Get the model's output logits
+    probs = torch.sigmoid(logits)[0]  # Apply sigmoid to get probabilities
+    
+    # Filter results with probability > 0.5
+    results = {label: float(prob) for label, prob in zip(labels, probs) if prob > 0.5}
+    return results
+
+# Set up Gradio interface
+iface = gr.Interface(fn=predict_emotions, inputs="text", outputs="label")
+
+# Launch the app
+iface.launch()