Update app.py

app.py

@@ -3,24 +3,17 @@ import streamlit as st
 from transformers import pipeline
 from datasets import load_dataset
 from huggingface_hub import hf_hub_download
-import subprocess
-import os
-
-# Clone the dataset repository if not already cloned
-repo_url = "https://huggingface.co/datasets/BEE-spoke-data/survivorslib-law-books"
-repo_dir = "./survivorslib-law-books"
+from datasets import load_dataset
 
-if not os.path.exists(repo_dir):
-    subprocess.run(["git", "clone", repo_url], check=True)
 
-# Load the dataset from the cloned repository
-dataset_path = os.path.join(repo_dir, "train.parquet")
-ds = load_dataset("parquet", data_files=dataset_path)
 
 # Initialize text-generation pipeline with the model
 model_name = "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF"
 pipe = pipeline("text-generation", model=model_name)
 
+# Load the dataset from the cloned local direc/tory
+ds = load_dataset("./canadian-legal-data", split="train",verify=False)
+
 # Gradio Interface setup
 def respond(
     message,
@@ -93,4 +86,4 @@ if __name__ == "__main__":
                 gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
             ],
         )
-        demo.launch()
+        demo.launch()