Create app.py

app.py

@@ -0,0 +1,31 @@
+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+import numpy as np
+
+# Load model and tokenizer
+model_name = "KevSun/IELTS_essay_scoring"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Prediction function
+def score_essay(essay):
+    inputs = tokenizer(essay, return_tensors="pt", truncation=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    preds = outputs.logits.squeeze().numpy()
+    normalized = (preds / preds.max()) * 9
+    rounded = np.round(normalized * 2) / 2
+    labels = ["Task Achievement", "Coherence & Cohesion", "Vocabulary", "Grammar", "Overall"]
+    return {label: float(score) for label, score in zip(labels, rounded)}
+
+# Gradio UI
+iface = gr.Interface(
+    fn=score_essay,
+    inputs=gr.Textbox(lines=10, placeholder="Paste your IELTS essay here..."),
+    outputs=[gr.Label(num_top_classes=5)],
+    title="Automated IELTS Essay Scorer",
+    description="Predicts scores for multiple dimensions of your essay"
+)
+
+iface.launch()