examples added

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load model from the Hub
+model_name = "enansari/emotion_roberta_weighted" 
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+emotion_labels = ["sadness", "joy", "love", "anger", "fear", "surprise"]
+
+def classify_emotion(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_id = torch.argmax(logits, dim=-1).item()
+    return emotion_labels[predicted_id]
+
+iface = gr.Interface(
+    fn=classify_emotion,
+    inputs=gr.Textbox(lines=2, placeholder="Type a sentence...", label="Input Text"),
+    outputs=gr.Textbox(label="Predicted Emotion"),
+    title="Sentiment Analysis with RoBERTa",
+    description="Detect emotions (joy, sadness, anger, etc.) in text.",
+    examples=[
+        ["I am extremely happy today!"],
+        ["This creates a lot of frustration."],
+        ["I feel so lonely and cold."]
+    ]
+)
+
+iface.launch()

requirements.txt

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+torch
+transformers
+gradio