Create app.py

app.py

@@ -0,0 +1,31 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import numpy as np
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("michellejieli/emotion_text_classifier")
+model = AutoModelForSequenceClassification.from_pretrained("michellejieli/emotion_text_classifier")
+
+# Function to classify emotions
+def classify_emotion(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    probabilities = torch.nn.functional.softmax(logits, dim=-1)
+    probs = probabilities.detach().numpy()[0]
+    labels = ["anger", "happy", "sad", "afraid", "worried"]
+    results = {label: prob for label, prob in zip(labels, probs)}
+    return results
+
+# Gradio interface setup
+iface = gr.Interface(
+    fn=classify_emotion,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a sentence to analyze emotions", label="Input Text"),
+    outputs=gr.Label(label="Emotion Probabilities"),
+    title="Emotion Classifier",
+    description="Enter a sentence and see the probabilities of different emotions."
+)
+
+if __name__ == "__main__":
+    iface.launch()