Add app, model, and requirements

app.py

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+
+import gradio as gr
+import torch
+from transformers import XLNetTokenizer, XLNetModel
+import numpy as np
+import joblib
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
+xlnet_model = XLNetModel.from_pretrained('xlnet-base-cased').to(device)
+
+random_forest_classifier = joblib.load("random_forest_model.pkl")
+
+def get_embedding(text):
+    inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True, max_length=512).to(device)
+    with torch.no_grad():
+        outputs = xlnet_model(**inputs)
+    return outputs.last_hidden_state.mean(dim=1).squeeze().cpu().numpy()
+
+def predict_complexity(sentence, target_word):
+    try:
+        sentence_embedding = get_embedding(sentence)
+        word_embedding = get_embedding(target_word)
+        combined_embedding = np.concatenate([sentence_embedding, word_embedding]).reshape(1, -1)
+        prediction = random_forest_classifier.predict(combined_embedding)[0]
+        return f"🔍 Predicted Complexity Level: **{prediction}**"
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+with gr.Blocks() as demo:
+    gr.Markdown("## ✨ Word Complexity Predictor")
+    with gr.Row():
+        sentence_input = gr.Textbox(label="Full Sentence", placeholder="Type a full sentence...")
+        word_input = gr.Textbox(label="Target Word", placeholder="Type the target word...")
+    output = gr.Markdown()
+    gr.Button("Predict Complexity").click(predict_complexity, [sentence_input, word_input], output)
+
+demo.launch()

random_forest_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b52464cc9f5eb892d0fd9de1cabf7a101df5131a3adba20fdcd00798f4740f74
+size 38174881

requirements.txt

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+
+gradio
+transformers
+torch
+scikit-learn
+pandas
+numpy