Update app.py

app.py

@@ -1,5 +1,5 @@
 
-!pip install datasets transformers accelerate peft bitsandbytes
+
 from datasets import load_dataset
 
 # Load 70% of the Wikipedia dataset
@@ -89,9 +89,7 @@ def prepare_labels(batch):
 # Apply the transformation to add labels
 tokenized_dataset = tokenized_dataset.map(prepare_labels, batched=True)
 # Step 1: Install FAISS for the Vector Database
-!pip install faiss-cpu
-!pip install faiss-gpu
-!pip install sentence_transformersimport torch
+
 from datasets import Dataset
 from transformers import AutoModel, AutoTokenizer
 import faiss
@@ -232,8 +230,7 @@ def rag_pipeline(question, faiss_index, dataset, top_k=3):
     answer = generate_answer(question, retrieved_texts)
 
     return answer
-!pip install ollama
-!ollama pull llama2
+
 # Import the necessary modules
 from langchain_community.llms import Ollama