Update app.py

app.py

@@ -2,9 +2,10 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline
 
 
-title = "Code Explainer"
-description = "This is a space to convert Python code into english text explaining what it does using [codeparrot-small-code-to-text](https://huggingface.co/codeparrot/codeparrot-small-code-to-text),\
- a code generation model for Python finetuned on [github-jupyter-code-to-text](https://huggingface.co/datasets/codeparrot/github-jupyter-code-to-text) a dataset of Python code followed by a docstring explaining it, the data was originally extracted from Jupyter notebooks."
+title = "🎅 Santa Explains Code"
+description = "This space converts Python code into English text that explains its function using [SantaCoder-Code-To-Text](https://huggingface.co/bigcode/santacoder-code-to-text),
+a code generation model that has been fine-tuned on the [github-jupyter-code-to-text](https://huggingface.co/datasets/codeparrot/github-jupyter-code-to-text) dataset.
+This dataset includes Python code accompanied by docstrings that explain it. The data was sourced from Jupyter notebooks."
 
 EXAMPLE_1 = "def sort_function(arr):\n n = len(arr)\n \n # Traverse through all array elements\n for i in range(n):\n \n # Last i elements are already in place\n for j in range(0, n-i-1):\n \n # traverse the array from 0 to n-i-1\n # Swap if the element found is greater\n # than the next element\n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]"
 EXAMPLE_2 = "from sklearn import model_selection\nX_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=0.2)"
@@ -59,7 +60,6 @@ iface = gr.Interface(
  outputs=gr.Textbox(label="Predicted explanation", lines=10),
  examples=example,
  layout="horizontal",
- theme="peach",
  description=description,
  title=title
 )