Update app.py

app.py

@@ -33,20 +33,20 @@ def get_pdf(pdf_docs):
     return text
 
 def text_splitter(text):
-  text_splitter = RecursiveCharacterTextSplitter(
+    text_splitter = RecursiveCharacterTextSplitter(
     # Set a really small chunk size, just to show.
     chunk_size=500,
     chunk_overlap=20,
     separators=["\n\n","\n"," ",".",","])
-  chunks=text_splitter.split_text(text)
-  return chunks
+    chunks=text_splitter.split_text(text)
+    return chunks
 
 GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
 
 def embedding(chunk):
-  embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-  vector = Chroma.from_documents(chunk)
-  db = Chroma.from_documents(vector, embeddings, persist_directory="./chroma_db")
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vector = Chroma.from_documents(chunk)
+    db = Chroma.from_documents(vector, embeddings, persist_directory="./chroma_db")
 
 def get_conversational_chain():
     prompt_template = """
@@ -63,12 +63,13 @@ def get_conversational_chain():
     return chain
 
 def user_call(query):
-  embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-  db3 = Chroma(persist_directory="./chroma_db", embedding_function=embeddings)
-  docs = db3.similarity_search(query)
-  chain = get_conversational_chain()
-  response = chain({"input_documents": docs, "question": query}, return_only_outputs=True)
-  st.write("Reply: ", response["output_text"])
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    #client = chromadb.HttpClient(host='127.0.0.1', port=8000, settings=Settings(allow_reset=True, anonymized_telemetry=False))
+    db3 = Chroma(persist_directory="./chroma_db", embedding_function=embeddings)
+    docs = db3.similarity_search(query)
+    chain = get_conversational_chain()
+    response = chain({"input_documents": docs, "question": query}, return_only_outputs=True)
+    st.write("Reply: ", response["output_text"])
 
 def main():
     st.header("Chat with your pdf💁")