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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"
# Load the learner
learner_inf = load_learner(model_path)
# Load the DataLoaders
dls = torch.load(dls_path)
# Create a mapping from class labels to indices
class_label_mapping = {label: idx for idx, label in enumerate(learner_inf.dls.vocab)}
# Define a function to make predictions
def predict_text(text):
prediction = learner_inf.blurr_predict(text)[0]
predicted_class_index = prediction['class_index']
predicted_class_label = list(class_label_mapping.keys())[list(class_label_mapping.values()).index(predicted_class_index)]
return predicted_class_label
# Create a Gradio interface
iface = gr.Interface(
fn=predict_text,
inputs="text",
outputs="text",
title="Text Classification App",
description="Enter a text, and it will predict the class label.",
)
# Start the Gradio app
iface.launch()
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