Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer from Hugging Face
+# model_name = "your-username/your-model-name"  # replace with your model path
+# tokenizer = AutoTokenizer.from_pretrained(model_name)
+# model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Use id2label from the config
+# id2label = model.config.id2label
+
+# Inference function
+def classify_text(text):
+    # inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    #with torch.no_grad():
+     #   outputs = model(**inputs)
+      #  probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+       # pred_class = torch.argmax(probs, dim=1).item()
+        #confidence = probs[0, pred_class].item()
+   # label = id2label[pred_class]
+    # return f"Predicted Level: {label} (Confidence: {confidence:.2f})"
+    return f"Predicted Level: a2 (Confidence: 0.5)"
+
+# Gradio interface
+demo = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(lines=4, placeholder="Schreibe etwas auf Deutsch..."),
+    outputs=gr.Textbox(label="Language Level Prediction"),
+    title="German Language Level Classifier",
+    description="Enter German text and get the predicted CEFR level (A1 to B2)."
+)
+
+# Launch app
+demo.launch()