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93a7dad 2694319 93a7dad 2694319 93a7dad 2694319 93a7dad 2694319 93a7dad 2694319 93a7dad 2694319 93a7dad 2694319 93a7dad 2694319 93a7dad 2694319 93a7dad 2694319 93a7dad 2694319 93a7dad | 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 | import gradio as gr
from huggingface_hub import InferenceClient
from utils import is_financial_text, load_qa_data
# Load CSV Q&A pairs (if you want to use them later)
qa_pairs = load_qa_data()
# Hugging Face client
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Define system message template
DEFAULT_SYSTEM_MSG = "You are a helpful assistant that answers only finance-related questions. Respond truthfully and avoid unrelated topics."
def respond(message, history, system_message, max_tokens, temperature, top_p):
# Check if user question is finance-related before asking the model
if not is_financial_text(message):
yield "I'm specialized in finance and can't assist with that question."
return
# Prepare messages for Zephyr
messages = [{"role": "system", "content": system_message or DEFAULT_SYSTEM_MSG}]
for user_msg, bot_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if bot_msg:
messages.append({"role": "assistant", "content": bot_msg})
messages.append({"role": "user", "content": message})
# Get model response (streamed)
response = ""
for msg in client.chat_completion(
messages=messages,
stream=True,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
):
token = msg.choices[0].delta.content
if token:
response += token
yield response
# Gradio interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value=DEFAULT_SYSTEM_MSG, 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)"),
],
title="💰 Finance Assistant",
description="Ask finance-related questions only. The assistant will not respond to unrelated topics.",
)
if __name__ == "__main__":
demo.launch()
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