Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# ✅ Load model & tokenizer
+model_name = "microsoft/deberta-v3-base"  # Change if needed
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=16)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+# ✅ Define prediction function
+def predict_mbti(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=256)
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    predictions = torch.argmax(outputs.logits, dim=1).cpu().item()
+    
+    # Mapping predicted labels back to MBTI types
+    mbti_types = [
+        "INFJ", "ENTP", "INTP", "INTJ", "ENTJ", "ENFJ", "INFP", "ENFP",
+        "ISFP", "ISTP", "ISFJ", "ISTJ", "ESTP", "ESFP", "ESTJ", "ESFJ"
+    ]
+    
+    return mbti_types[predictions]
+
+# ✅ Create Gradio UI
+interface = gr.Interface(
+    fn=predict_mbti,
+    inputs=gr.Textbox(lines=3, placeholder="Enter a text to predict MBTI type"),
+    outputs="text",
+    title="MBTI Personality Predictor",
+    description="Enter a text and get the predicted MBTI personality type."
+)
+
+# ✅ Launch app
+if __name__ == "__main__":
+    interface.launch()