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import json
import spaces
import gradio as gr
import torch
from distilabel.llms import LlamaCppLLM
from distilabel.steps.tasks.argillalabeller import ArgillaLabeller
file_path = os.path.join(os.path.dirname(__file__), "qwen2-0_5b-instruct-fp16.gguf")
download_url = "https://huggingface.co/Qwen/Qwen2-0.5B-Instruct-GGUF/resolve/main/qwen2-0_5b-instruct-fp16.gguf?download=true"
if not os.path.exists(file_path):
import requests
import tqdm
response = requests.get(download_url, stream=True)
total_length = int(response.headers.get("content-length"))
with open(file_path, "wb") as f:
for chunk in tqdm.tqdm(
response.iter_content(chunk_size=1024),
total=total_length,
unit="KB",
unit_scale=True,
):
f.write(chunk)
llm = LlamaCppLLM(
model_path=file_path,
n_gpu_layers=-1,
n_ctx=1024 * 4,
)
task = ArgillaLabeller(llm=llm)
task.load()
def load_examples():
with open("examples.json", "r") as f:
return json.load(f)
# Create Gradio examples
examples = load_examples()
def process_fields(fields):
if isinstance(fields, str):
fields = json.loads(fields)
if isinstance(fields, dict):
fields = [fields]
return [field if isinstance(field, dict) else json.loads(field) for field in fields]
@spaces.GPU
def process_records_gradio(records, example_records, fields, question):
try:
# Convert string inputs to dictionaries
records = json.loads(records)
example_records = json.loads(example_records) if example_records else None
fields = process_fields(fields) if fields else None
question = json.loads(question) if question else None
print(fields)
print(question)
print(example_records)
if not fields and not question:
return "Error: Either fields or question must be provided"
runtime_parameters = {"fields": fields, "question": question}
if example_records:
runtime_parameters["example_records"] = example_records
print(runtime_parameters)
task.set_runtime_parameters(runtime_parameters)
results = []
for record in records:
output = next(task.process(inputs=[{"records": record}]))
results.append(output[0]["suggestions"])
return json.dumps({"results": results}, indent=2)
except Exception as e:
return f"Error: {str(e)}"
interface = gr.Interface(
fn=process_records_gradio,
inputs=[
gr.Code(label="Records (JSON)", language="json", lines=5),
gr.Code(label="Example Records (JSON, optional)", language="json", lines=5),
gr.Code(label="Fields (JSON, optional)", language="json"),
gr.Code(label="Question (JSON, optional)", language="json"),
],
examples=examples,
outputs=gr.Code(label="Suggestions", language="json", lines=10),
title="Record Processing Interface",
description="Enter JSON data for `rg.Record.to_dict()`, `List[rg.Record.to_dict()]`, `List[Field].serialize()`, or `List[rg.Question.serialize()]` At least one of fields or question must be provided.",
)
if __name__ == "__main__":
interface.launch()
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