Update app.py

app.py

@@ -31,12 +31,13 @@ from sklearn.metrics import silhouette_score
 from scipy.stats import spearmanr
 from functools import lru_cache
 from langchain.retrievers import MultiQueryRetriever
-from langchain.llms import HuggingFacePipeline
+from langchain_community.llms import HuggingFacePipeline
 from transformers import pipeline
 from sklearn.model_selection import ParameterGrid
 from tqdm import tqdm
 import random
 
+llm = HuggingFacePipeline(pipeline(model="HuggingFaceH4/zephyr-7b-beta"))
 
 # NLTK Resource Download
 def download_nltk_resources():
@@ -170,7 +171,7 @@ def optimize_query(query, llm_model, chunks, embedding_model, vector_store_type,
     #)
     #llm = HuggingFacePipeline(pipeline=pipe)
 
-    llm = HuggingFacePipeline(pipeline(model="HuggingFaceH4/zephyr-7b-beta"))
+    #llm = HuggingFacePipeline(pipeline(model="HuggingFaceH4/zephyr-7b-beta"))
     
     
     # Create a temporary vector store for query optimization
@@ -458,7 +459,7 @@ def optimize_query(query, llm_model, chunks, embedding_model, vector_store_type,
     #)
     #llm = HuggingFacePipeline(pipeline=pipe)
 
-    llm = HuggingFacePipeline(pipeline(model="HuggingFaceH4/zephyr-7b-beta"))
+    #llm = HuggingFacePipeline(pipeline(model="HuggingFaceH4/zephyr-7b-beta"))
     
     
     # Create a temporary vector store for query optimization
@@ -752,7 +753,7 @@ Provide your suggestions in a Python dictionary format."""
     #)
     #llm = HuggingFacePipeline(pipeline=pipe)
 
-    llm = HuggingFacePipeline(pipeline(model="HuggingFaceH4/zephyr-7b-beta"))
+    #llm = HuggingFacePipeline(pipeline(model="HuggingFaceH4/zephyr-7b-beta"))
     
     
     #llm = HuggingFacePipeline.from_model_id(