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b0c4b8a deb56f6 95e88bf deb56f6 95e88bf e259930 76d60e5 e259930 95e88bf 9dfcf37 ffa9354 76d60e5 ffa9354 e072b38 ffa9354 b0c4b8a ffa9354 b0c4b8a 00c8bf3 | 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 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | import gradio as gr
from huggingface_hub import InferenceClient
##########
import os
from dotenv import load_dotenv
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_chroma import Chroma
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings import HuggingFaceEmbeddings
import gradio as gr
MODEL = "gpt-4o-mini"
db_name = "vector_db"
CSS = """
.contain { display: flex; flex-direction: column; }
.gradio-container { height: 100vh !important; }
#component-0 { height: 100%; }
#chatbot { flex-grow: 1; overflow: auto;}
"""
load_dotenv()
os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')
vectorstore = Chroma(persist_directory=db_name, embedding_function=OpenAIEmbeddings())
llm = ChatOpenAI(temperature=0.7, model_name=MODEL)
memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
retriever = vectorstore.as_retriever()
conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)
def chat(question, history):
# Get model response
result = conversation_chain.invoke({"question": question})
bot_response = (result["answer"]).replace(f"\\[", " $$ ").replace(f"\\]", " $$ ")
# Append the latest conversation
history.append((question, bot_response))
return history, "" # Return updated history and clear the input box
# Create Gradio UI
with gr.Blocks(css=CSS) as demo:
chatbot = gr.Chatbot(latex_delimiters=[{"left": "$$", "right": "$$", "display": True},
{"left": "\\(", "right": "\\)", "display": False}], elem_id="chatbot")
msg = gr.Textbox(placeholder="Ask something...", interactive=True)
btn = gr.Button("Send")
# Trigger chat function when "Enter" is pressed in the textbox
msg.submit(chat, [msg, chatbot], [chatbot, msg])
# Button click also triggers chat function
btn.click(chat, [msg, chatbot], [chatbot, msg])
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demoX = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
#if __name__ == "__main__":
demo.launch(inbrowser=True)
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