app.py

app.py

@@ -0,0 +1,29 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the model and tokenizer
+model_name = "KevSun/Personality_LM"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Function to predict personality traits
+def predict_personality(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    labels = ["Introverted", "Extroverted", "Open", "Agreeable", "Conscientious", "Neurotic"]
+    predictions = {label: prob.item() for label, prob in zip(labels, probs[0])}
+    return predictions
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict_personality,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a sentence here..."),
+    outputs=gr.Label(),
+    title="Personality Analyzer",
+    description="Enter a sentence and get a prediction of personality traits."
+)
+
+# Launch the Gradio app
+interface.launch()