Update util.py

util.py

@@ -1,51 +1,51 @@
-import os
-import json
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores.faiss import FAISS
-from langchain.chains.question_answering import load_qa_chain
-from langchain.prompts import PromptTemplate
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-from langchain_google_genai import ChatGoogleGenerativeAI
-import google.generativeai as genai
-from dotenv import load_dotenv
-load_dotenv()
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-
-def extract_text(json_path):
-    with open(json_path, 'r') as file:
-        data = json.load(file)
-        
-    text = ""
-    for professor in data['professors']:
-        professor_id = professor.get('professor_id')
-        name = professor.get('name')
-        course = professor.get('course')
-        reviews = professor.get('reviews', [])
-                
-        text += f'\nProfessor ID: {professor_id}, Professor Name: {name}, Course: {course}\n '
-        if reviews:
-            for review in reviews:
-                rating = review.get('rating')
-                review_text = review.get('review_text')
-                text += f"Rating: {rating}, Review: {review_text}\n"
-        else:
-            print("No reviews available.")
-    return text
-
-def split_text_into_chunks(text):
-    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-    text_chunks = splitter.split_text(text)
-    return text_chunks
-
-def create_vector_store(chunks):
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-    vector_store = FAISS.from_texts(chunks, embedding=embeddings)
-    vector_store.save_local("reviews_index")
-
-def main(json_path):
-    text = extract_text(json_path)
-    chunks = split_text_into_chunks(text)
-    create_vector_store(chunks)
-
-json_path = 'reviews.json'
+import os
+import json
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores.faiss import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain_google_genai import ChatGoogleGenerativeAI
+import google.generativeai as genai
+from dotenv import load_dotenv
+
+def extract_text(json_path):
+    with open(json_path, 'r') as file:
+        data = json.load(file)
+        
+    text = ""
+    for professor in data['professors']:
+        professor_id = professor.get('professor_id')
+        name = professor.get('name')
+        course = professor.get('course')
+        reviews = professor.get('reviews', [])
+                
+        text += f'\nProfessor ID: {professor_id}, Professor Name: {name}, Course: {course}\n '
+        if reviews:
+            for review in reviews:
+                rating = review.get('rating')
+                review_text = review.get('review_text')
+                text += f"Rating: {rating}, Review: {review_text}\n"
+        else:
+            print("No reviews available.")
+    return text
+
+def split_text_into_chunks(text):
+    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    text_chunks = splitter.split_text(text)
+    return text_chunks
+
+def create_vector_store(chunks):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vector_store = FAISS.from_texts(chunks, embedding=embeddings)
+    vector_store.save_local("reviews_index")
+
+def main(json_path):
+    load_dotenv()
+    genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+    text = extract_text(json_path)
+    chunks = split_text_into_chunks(text)
+    create_vector_store(chunks)
+
+json_path = 'reviews.json'
 main(json_path)