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from huggingface_hub import InferenceClient
from init import ACCESS_TOKEN, SYSTEM_PROMPT
from utils import extract_sql, is_sql
from database import execute
client = InferenceClient(api_key=ACCESS_TOKEN)
messages = [{"role": "system", "content": SYSTEM_PROMPT}]
def respond(message, history, system_message, max_tokens, temperature, top_p):
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.completions.create(
model="Qwen/Qwen2.5-3B-Instruct",
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
messages=messages,
):
token = message.choices[0].delta.content
response += token
yield response
if is_sql(response):
sql_query = extract_sql(response)
sql_result = execute(sql_query)
reformulation_prompt = f"Kết quả truy vấn SQL:\n{sql_result}\n\nHãy diễn đạt lại kết quả cho người dùng một cách dễ hiểu."
messages.append({"role": "user", "content": reformulation_prompt})
reformulated_response = ""
for msg in client.chat.completions.create(
model="Qwen/Qwen2.5-3B-Instruct",
max_tokens=512,
stream=True,
temperature=temperature,
top_p=top_p,
messages=messages,
):
token = msg.choices[0].delta.content
reformulated_response += token
yield reformulated_response
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