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app.py

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+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Load the model and tokenizer
+model_name = "ahmetyaylalioglu/text-emotion-classifier"  # Replace with your actual model path
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Function to predict emotion
+def predict_emotion(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    prediction = torch.argmax(probabilities, dim=-1).item()
+
+    emotion = model.config.id2label[prediction]
+    confidence = probabilities[0][prediction].item()
+
+    return f"Emotion: {emotion}\nConfidence: {confidence:.2f}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
+    outputs="text",
+    title="Emotion Classifier",
+    description="Enter some text and click 'Submit' to predict the emotion."
+)
+
+# Launch the app
+iface.launch()

requirements.txt

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+gradio
+transformers
+torch