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import gradio as gr | |
import requests | |
import json | |
import os | |
API_URL = "https://api-inference.huggingface.co/models/davidaf3/ReverseNutrition_TFIngPort" | |
headers = {"Authorization": f"Bearer {os.environ['API_TOKEN']}"} | |
def predict(image_file): | |
with open(image_file, "rb") as f: | |
data = f.read() | |
response = requests.request("POST", API_URL, headers=headers, data=data) | |
predictions = json.loads(response.content.decode("utf-8")) | |
return [[element["label"], element["score"]] for element in predictions if element["score"] > 0] | |
app = gr.Interface( | |
fn=predict, | |
inputs=gr.Image(type="filepath"), | |
outputs=gr.Dataframe(headers=["ingredient", "amount per 100g (in g)"]), | |
allow_flagging="never", | |
description= | |
"Upload food images and get an estimation about their ingredients and the ingredient proportions.\ | |
The model used is [ReverseNutrition_TFIngPort](https://huggingface.co/davidaf3/ReverseNutrition_TFIngPort).\ | |
If the output table shows an error, wait until the model is loaded." | |
) | |
app.launch() |