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import os
import json
import logging
import typing as t
import gradio as gr
from huggingface_hub import InferenceClient
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
)
logger = logging.getLogger(__name__)
HF_API_TOKEN = os.getenv("HF_API_TOKEN")
MODEL_REPO_ID= "google/gemma-2-9b-it"
if HF_API_TOKEN is None:
logger.error("HF_API_TOKEN environment variable is not set.")
raise ValueError("Error: HF_API_TOKEN environment variable is not set.")
def build_messages(input_json: str) -> t.Sequence[t.Mapping[str, str]]:
data_structure = (
"You are given a restaurant menu in Spanish. You MUST return a single valid JSON in this format:\n"
"Menu Format:\n"
"<menu> ::= '{' \"restaurant_name\": <string>, \"categories\": [ <category>* ] '}'\n"
"<category> ::= '{' \"category\": <string>, \"items\": [ <item>* ] '}'\n"
"<item> ::= '{' \"name\": <string>, \"price\": <number>, \"description\": <string> }'\n"
)
instructions = (
"Requirements:\n"
"1. **Translate ALL Spanish text into English**, including:\n"
" - restaurant_name (only if it's in Spanish).\n"
" - All category names.\n"
" - All item names.\n"
" - Any Spanish words in the final descriptions.\n\n"
"2. If an item name is a distinct dish with no direct English equivalent, "
" **still attempt** an English literal translation, or provide a parenthetical explanation if needed.\n\n"
"3. For every item, add a new field called description in **concise, appetizing English**.\n\n"
"4. **Do not** change or remove existing fields. The only added field is description.\n\n"
"5. **Do not** change meaning or make up information."
"6. Return **only** the JSON object. **No markdown**, no code fences, no extra text.\n"
"7. Ensure the output is **valid JSON** with correct brackets, commas, and quotes.\n\n"
)
system_message = data_structure + instructions
user_message = (
"Process the following menu:\n\n"
f"{input_json}"
)
return [
{"role": "user", "content": system_message + user_message},
]
def process_menu(input_text: str) -> str:
client = InferenceClient(model=MODEL_REPO_ID, token=HF_API_TOKEN)
messages = build_messages(input_text)
logger.info("Generating response from the model.")
response = client.chat_completion(
messages,
max_tokens=2048,
temperature=0.1,
seed=42,
)
if response is not None and response.choices is not None:
content = response.choices[0].message.content
logger.info(response)
logger.info(content)
parsed = json.loads(content)
logger.info("Parsed JSON successfully.")
return json.dumps(parsed, indent=2, ensure_ascii=False)
def process_data(data: t.Any) -> str:
logger.info("Reading input file: %s", data)
input_name = os.path.basename(data)
preprocessed_name = "preprocessed_" + input_name
with open(data, "r", encoding="utf-8") as raw_data:
menu = raw_data.read()
logger.info("Processing the menu data through the model.")
preprocessed_data = process_menu(menu)
logger.info("Writing preprocessed data to file: %s", preprocessed_name)
with open(preprocessed_name, "w", encoding="utf-8") as temp_data:
temp_data.write(preprocessed_data)
logger.info("Processing complete. Preprocessed file created: %s", preprocessed_name)
return preprocessed_name
with gr.Blocks() as demo:
gr.Markdown("# Restaurant Menu Processor")
gr.Markdown(
"Upload a JSON file containing a restaurant menu in Spanish. "
"This tool will translate the menu into English and add descriptions."
)
with gr.Row():
input_file = gr.File(label="Upload Restaurant Menu JSON (Spanish)")
output_file = gr.File(label="Download Augmented Menu JSON (English)")
process_button = gr.Button("Process Menu")
process_button.click(process_data, inputs=input_file, outputs=output_file)
demo.launch()
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