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Update app.py
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import datetime
import os
import gradio as gr
import langchain
import pickle
from langchain.llms import HuggingFaceHub
from chain import get_new_chain1
api_token = os.environ["HF_TOKEN"]
def get_faiss_store():
with open("docs.pkl", 'rb') as f:
faiss_store = pickle.load(f)
return faiss_store
def load_model():
print(langchain.__file__)
vectorstore = get_faiss_store()
flan_ul = HuggingFaceHub(repo_id="google/flan-ul2",
model_kwargs={"temperature":0.1, "max_new_tokens":200},
huggingfacehub_api_token=api_token)
qa_chain = get_new_chain1(vectorstore, flan_ul, flan_ul, isFlan=True)
return qa_chain
def chat(inp, history, agent):
history = history or []
if agent is None:
history.append((inp, "Please wait for model to load (3-5 seconds)"))
return history, history
print("\n==== date/time: " + str(datetime.datetime.now()) + " ====")
print("inp: " + inp)
history = history or []
output = agent({"question": inp, "chat_history": history})
answer = output["answer"]
history.append((inp, answer))
print(history)
return history, history
block = gr.Blocks(css=".gradio-container {background-color: lightgray}")
with block:
with gr.Row():
gr.Markdown("<h3><center>πŸ€— Transformers Chat</center></h3><p>Ask questions about the Hugging Face Transformers Library, Powered by Flan-UL2</p>")
chatbot = gr.Chatbot()
with gr.Row():
message = gr.Textbox(
label="What's your question?",
placeholder="What's the answer to life, the universe, and everything?",
lines=1,
)
submit = gr.Button(value="Send", variant="secondary").style(full_width=False)
gr.Examples(
examples=[
"How do I install transformers?",
"How do I load pretrained instances with an AutoClass?",
"How do I fine-tune a pretrained model?",
],
inputs=message,
)
gr.HTML(
"""
This simple application uses Langchain, an open-source LLM, and FAISS to do Q&A over the πŸ€— Transformers Documentation."""
)
gr.HTML(
"<center>Powered by <a href='huggingface.co'>Hugging Face πŸ€—</a> and <a href='https://github.com/hwchase17/langchain'>LangChain πŸ¦œοΈπŸ”—</a></center>"
)
state = gr.State()
agent_state = gr.State()
block.load(load_model, inputs=None, outputs=[agent_state])
submit.click(chat, inputs=[message, state, agent_state], outputs=[chatbot, state])
message.submit(chat, inputs=[message, state, agent_state], outputs=[chatbot, state])
block.launch(debug=True)