File size: 1,941 Bytes
abc16ec 3ee66e5 abc16ec 863cee5 abc16ec 3ee66e5 40cb81b 3ee66e5 abc16ec 9ad3866 8daf606 abc16ec 95be932 8daf606 95be932 8daf606 10362c2 95be932 8daf606 95be932 e960d9b 95be932 e960d9b 95be932 e960d9b 95be932 abc16ec 3ee66e5 abc16ec 936abfc 3ee66e5 abc16ec 3ee66e5 ecec617 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
import os
from typing import Optional, Tuple
from threading import Lock
import pickle
import gradio as gr
from query_data import get_chain
class ChatWrapper:
def __init__(self):
self.lock = Lock()
def __call__(self, inp: str, history: Optional[Tuple[str, str]]):
self.lock.acquire()
api_key = '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'
#chain = self.set_openai_api_key(api_key)
try:
with open("vectorstore.pkl", "rb") as f:
vectorstore = pickle.load(f)
os.environ["OPENAI_API_KEY"] = api_key
qa_chain = get_chain(vectorstore)
print("Chat with your docs!")
with open("vectorstore.pkl", "rb") as f:
vectorstore = pickle.load(f)
qa_chain = get_chain(vectorstore)
chat_history = []
print("Chat with your docs!")
while True:
print("Human:")
question = inp
output = qa_chain({"question": question, "chat_history": chat_history})
chat_history.append((question, output["answer"]))
print("AI:")
print(result["answer"])
# while True:
# print("Human:")
# history = history or []
# output = qa_chain({"question": inp, "chat_history": history})["answer"]
#history.append((inp, output))
# print("AI:")
#print(output["answer"])
chatResult = (output, chat_history)
except Exception as e:
raise e
finally:
self.lock.release()
return chatResult
chat = ChatWrapper()
state = gr.outputs.State()
gradio_interface = gr.Interface(chat, inputs=["text", state], outputs=["text", state])
gradio_interface.launch(debug=True)
|