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cf2752b | 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 58 59 60 61 62 63 64 65 66 | # load dependencies
import transformers
import torch
import gradio as gr
from transformers import AutoModelForCausalLM,AutoTokenizer
from transformers import pipeline
import time
def pred_on_text(input_text):
start_time=time.time()
raw_output = loaded_model_pipeline(text_inputs=[{"role":"user",
"content":input_text}],
max_new_tokens=256,
disable_compile=True)
end_time = time.time()
total_time = round(end_time - start_time,4)
generated_text = raw_output[0]["generated_text"][1]["content"]
return generated_text,raw_output,total_time
# load the model (from our hugging face repo)
MODEL_PATH = "csvis/food-extract-gemma-3-270m-finetune-v1"
# load the model into pipeline
loaded_model=AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path = MODEL_PATH,
dtype="auto",
device_map="auto",
attn_implementation="eager"
)
tokenizer= AutoTokenizer.from_pretrained(
MODEL_PATH
)
# Create model pipeline
loaded_model_pipeline=pipeline("text-generation",
model=loaded_model,
tokenizer=tokenizer)
# create the demo
description = """Extract food and drinks items from text with a fine-tuned SLM(small language model)
* Input(str) : Raw text strings or image captions (e.g . "A photo of dog sitting on a beach" or "A breakfast plate wit bacon, eggs and toast")
* Output(str): Generated text with food/not_food classification as well as noun extracted food and drink items and various food tags.
For example :
Input: "For breakfast I had eggs, bread and a banana"
Output: food_or_drink: 1
tags: fi
foods: eggs, bread, banana
drinks:"""
demo = gr.Interface(fn=pred_on_text,
inputs=gr.TextArea(lines=4,label="Input Text"),
outputs=[gr.TextArea(lines=4,label="Generated Text"),
gr.TextArea(lines=7,label ="Raw Output"),
gr.Number(label="Generation Time (s)")],
title="🥭🍎Structured FoodExtract with a Fine-TunedGemma 3 270M",
description=description,
examples=[["Hello This is my Second fine-tuned LLm!"],
["A plate of food with grilled tuna,salad with avocados,olives,tomatoes and carrot and Italian dressing"],
["Chicken wings"],
["She was Looking beautiful Yesterday at the event"]]
)
if __name__=="__main__":
demo.launch(share=True)
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