gptapi / app.py
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Update app.py
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import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import argparse
def generate_prompt(question, prompt_file="prompt.md", metadata_file="metadata.sql"):
with open(prompt_file, "r") as f:
prompt = f.read()
with open(metadata_file, "r") as f:
table_metadata_string = f.read()
prompt = prompt.format(
user_question=question, table_metadata_string=table_metadata_string
)
return prompt
def get_tokenizer_model(model_name):
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
torch_dtype=torch.float16,
device_map="auto",
use_cache=True,
)
return tokenizer, model
def run_inference(question, prompt_file="prompt.md", metadata_file="metadata.sql"):
tokenizer, model = get_tokenizer_model("defog/sqlcoder-34b-alpha")
prompt = generate_prompt(question, prompt_file, metadata_file)
# make sure the model stops generating at triple ticks
# eos_token_id = tokenizer.convert_tokens_to_ids(["```"])[0]
eos_token_id = tokenizer.eos_token_id
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=300,
do_sample=False,
num_beams=5, # do beam search with 5 beams for high quality results
)
generated_query = (
pipe(
prompt,
num_return_sequences=1,
eos_token_id=eos_token_id,
pad_token_id=eos_token_id,
)[0]["generated_text"]
.split("```sql")[-1]
.split("```")[0]
.split(";")[0]
.strip()
+ ";"
)
return generated_query
if __name__ == "__main__":
# Parse arguments
parser = argparse.ArgumentParser(description="Run inference on a question")
parser.add_argument("-q","--question", type=str, help="Question to run inference on")
args = parser.parse_args()
question = args.question
print("Loading a model and generating a SQL query for answering your question...")
print(run_inference(question))