app.py

import gradio as gr
from transformers import AutoTokenizer
import torch
from fastai.text.all import *
from blurr.text.data.all import *
from blurr.text.modeling.all import *

# Define the path to your model and dataloaders
model_path = "origin-classifier-stage-2.pkl"
dls_path = "dls_origin-classifier_v1.pkl"

learner_inf = load_learner(model_path)

dls = torch.load(dls_path)

class_label_mapping = {label: idx for idx, label in enumerate(learner_inf.dls.vocab)}

def predict_text(text):
    prediction = learner_inf.blurr_predict(text)
    predicted_probs = prediction[0]['scores']
    predicted_labels = prediction[0]['class_labels']
    result = {label: f"{prob*100:.2f}%" for label, prob in zip(predicted_labels, predicted_probs)}
    return result

iface = gr.Interface(
    fn=predict_text,
    inputs=gr.inputs.Textbox(lines=2, placeholder='Enter Recipe Here...'),
    outputs=gr.outputs.Label(num_top_classes=3),
    title="Food Origin Classification App",
    description="Enter a Recipe, and it will predict the class label.",
)

iface.launch()