Update app.py

app.py

@@ -1,3 +1,30 @@
+import torch
+from transformers import BartForConditionalGeneration, BartTokenizer
+import gradio as gr
+
+# Load the fine-tuned model and tokenizer
+model_path = "Asna-DifiNative/AIBuddy"  # Path to the pretrained fine-tuned model
+model = BartForConditionalGeneration.from_pretrained(model_path)
+tokenizer = BartTokenizer.from_pretrained(model_path)
+
+# Translate function using the fine-tuned model
+def translate_instruction(context, input_text):
+    full_input = context + " " + input_text
+    input_ids = tokenizer.encode(full_input, truncation=True, return_tensors='pt')
+    with torch.no_grad():
+        outputs = model.generate(input_ids, max_length=50, num_beams=4, early_stopping=True)
+    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return translated_text
+
+# Example instructions (without context)
+example_instructions = [
+    "Show me a list of pods in the current namespace.",
+    "Display the pods running in the 'development' namespace.",
+    "List all pods in the 'production' environment.",
+    "How can I see the pods in the 'testing' namespace?",
+    "What command should I use to get the list of containers?"
+]
+
 # Create context element
 context_element = gr.inputs.Textbox(lines=3, label="Context")
 
@@ -12,4 +39,4 @@ iface = gr.Interface(
 )
 
 # Launch the Gradio app using Ngrok
-iface.launch(examples=[["", instruction] for instruction in example_instructions])
+iface.launch(examples=[[context, instruction] for instruction in example_instructions])