Create app.py

app.py

@@ -0,0 +1,58 @@
+import os
+import sys
+import openai
+import gradio as gr
+from langchain.chains import ConversationalRetrievalChain, RetrievalQA
+from langchain.chat_models import ChatOpenAI
+from langchain.document_loaders import DirectoryLoader, TextLoader
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.indexes.vectorstore import VectorStoreIndexWrapper
+from langchain.llms import OpenAI
+from langchain.vectorstores import Chroma
+
+import constants
+
+os.environ["OPENAI_API_KEY"] = constants.APIKEY
+
+# Enable to save to disk & reuse the model (for repeated queries on the same data)
+PERSIST = False
+
+query = None
+
+if PERSIST and os.path.exists("persist"):
+    print("Reusing index...\n")
+    vectorstore = Chroma(persist_directory="persist",
+                         embedding_function=OpenAIEmbeddings())
+    index = VectorStoreIndexWrapper(vectorstore=vectorstore)
+else:
+    loader = DirectoryLoader("data/")
+    if PERSIST:
+        index = VectorstoreIndexCreator(
+            vectorstore_kwargs={"persist_directory": "persist"}).from_loaders([loader])
+    else:
+        index = VectorstoreIndexCreator().from_loaders([loader])
+
+chain = ConversationalRetrievalChain.from_llm(
+    llm=ChatOpenAI(model="gpt-3.5-turbo"),
+    retriever=index.vectorstore.as_retriever(search_kwargs={"k": 1}),
+)
+
+chat_history = []
+
+
+async def getQuery(query: str):
+    if not query:
+        query = input("Prompt: ")
+    if query in ['quit', 'q', 'exit']:
+        sys.exit()
+    result = chain({"question": query, "chat_history": chat_history})
+    print(result['answer'])
+
+    chat_history.append((query, result['answer']))
+    query = None
+    return result['answer']
+
+demo = gr.Interface(fn=getQuery, inputs="text", outputs="text")
+
+demo.launch()