Update app.py

app.py

@@ -1,20 +1,37 @@
 import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-def respond(val):
-    if len(val) > 1:
-        if val[1]:
-            # do something
-            pass
+# Load pre-trained model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("klue/bert-base-uncased")
+tokenizer = AutoTokenizer.from_pretrained("klue/bert-base-uncased")
+
+def chatbot(input_text):
+    # Tokenize input text
+    inputs = tokenizer(input_text, return_tensors="pt")
+    
+    # Get model predictions
+    outputs = model(**inputs)
+    logits = outputs.logits.detach().numpy()
+    predicted_class = logits.argmax(-1)[0]
+    
+    # Generate response based on predicted class
+    if predicted_class == 0:
+        response = "I'm happy to help you with that!"
+    elif predicted_class == 1:
+        response = "I'm not sure I understand. Can you please rephrase?"
     else:
-        # handle the case where val has only one element
-        pass
+        response = "I'm sorry, I'm not trained to respond to that."
+    
+    return response
 
+# Create Gradio interface
 demo = gr.Interface(
-    fn=respond,
+    fn=chatbot,
     inputs="text",
     outputs="text",
-    title="My App",
-    description="This is my app"
+    title="Chatbot",
+    description="Talk to me!"
 )
 
+# Launch Gradio app
 demo.launch()