testing

app.py

@@ -1,7 +1,26 @@
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-def greet(name):
-    return "Hello " + name + "!!"
+model_name = "cross-encoder/multi-nli-xlm-r-100"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+def generate_prediction(input_text):
+    input_ids = tokenizer.encode(input_text, truncation=True, padding=True, return_tensors='pt')
+    outputs = model(input_ids)
+    predicted_label = torch.argmax(outputs.logits)
+    label_map = {0: "entailment", 1: "neutral", 2: "contradiction"}
+    predicted_label_text = label_map[predicted_label.item()]
+    return predicted_label_text
+
+input_text = gr.inputs.Textbox(label="Input text")
+output_text = gr.outputs.Textbox(label="Output text")
+
+gr.Interface(
+    generate_prediction,
+    inputs=input_text,
+    outputs=output_text,
+    title="Text Classifier",
+    description="A Hugging Face cross-encoder model for text classification.",
+).launch()