Update app.py

app.py

@@ -1,35 +1,11 @@
-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")
 
 # Gradio Interface
 iface = gr.Interface(
     fn=translate_instruction,
     inputs=[
-        gr.inputs.Textbox(label="Enter Context"),
+        context_element,
         gr.inputs.Textbox(label="Enter Human Instruction")
     ],
     outputs=gr.outputs.Textbox(label="Generated CLI Command")