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import json
import os
from pathlib import Path
import faiss
from langchain.docstore.document import Document
from langchain.docstore.in_memory import InMemoryDocstore
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores.faiss import FAISS
import gradio as gr
from chain import get_chain
STORE_DIR = "store"
YOUTUBE_EMBED_TEMPLATE = """
<iframe width="354"
height="200"
src="{source}&cc_load_policy=1"
title="YouTube video player"
frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen>
</iframe>"""
def load_store():
def keys_to_int(x):
return {int(k): v for k, v in x.items()}
index_path = list(Path(STORE_DIR).glob("*.faiss"))
if len(index_path) == 0:
raise ValueError("No index found in path")
index_path = index_path[0]
index_name = index_path.name.split(".")[0]
with open(os.path.join(STORE_DIR, f"{index_name}_doc_idx.json"), "r") as f:
index_to_id = json.load(f, object_hook=keys_to_int)
with open(os.path.join(STORE_DIR, f"{index_name}_docs.json"), "r") as f:
docs = json.load(f)
embeddings = OpenAIEmbeddings()
return FAISS(
embedding_function=embeddings.embed_query,
index=faiss.read_index(str(index_path)),
docstore=InMemoryDocstore(
{index_to_id[i]: Document(**doc) for i, doc in enumerate(docs.values())}
),
index_to_docstore_id=index_to_id,
)
def set_openai_api_key(api_key, agent):
if api_key:
os.environ["OPENAI_API_KEY"] = api_key
vstore = load_store()
qa_chain = get_chain(vstore)
os.environ["OPENAI_API_KEY"] = ""
return qa_chain
def _to_embed(link):
return link.replace("watch?v=", "embed/").replace("&t=", "?start=")
def chat(inp, history, agent):
history = history or []
if agent is None:
history.append((inp, "Please paste your OpenAI key"))
return history, history
output = agent({"question": inp, "chat_history": history})
answer = output["answer"]
history.append((inp, answer))
source_iframes = []
for source in output["sources"]:
if "youtube.com" in source:
source_iframes.append(
YOUTUBE_EMBED_TEMPLATE.format(source=_to_embed(source))
)
source_html = f"""<div style='min-height:200px;display:flex;align-items:center;justify-content:space-around;'>
{''.join(source_iframes)}
</div>"""
return history, history, source_html
with gr.Blocks(css=".gradio-container {background-color: lightgray}") as demo:
state = gr.State()
agent_state = gr.State()
gr.Markdown("<h3><center>ToKBot🤖 - Ask ToKCast Questions</center></h3>")
openai_api_key_textbox = gr.Textbox(
placeholder="Paste your OpenAI API key (sk-...)",
show_label=False,
lines=1,
type="password",
)
sources = gr.HTML()
chatbot = gr.Chatbot()
with gr.Row():
message = gr.Textbox(
label="What's your question?",
placeholder="Type your question here...",
lines=1,
)
submit = gr.Button(value="Send", variant="secondary").style(full_width=False)
gr.Examples(
examples=[
'What does "the beginning of infinity" refer to?',
"How do memes differ from genes in their replication?",
"What is the nature of knowledge and how does it grow?",
],
inputs=message,
)
gr.HTML(
"""<p>A GPT-3/LangChain bot that answers questions about the TokCast podcast provides relevant video excerpts</p>"""
)
gr.HTML(
"<center>Powered by <a href='https://github.com/hwchase17/langchain'>LangChain 🦜️🔗</a></center>"
)
submit.click(
chat,
inputs=[message, state, agent_state],
outputs=[chatbot, state, sources],
)
message.submit(
chat,
inputs=[message, state, agent_state],
outputs=[chatbot, state, sources],
)
openai_api_key_textbox.change(
set_openai_api_key,
inputs=[openai_api_key_textbox, agent_state],
outputs=[agent_state],
)
demo.launch()
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