app.py

#IMPORTS
import fastai
from fastai.vision.all import *
import gradio as gr

# FILE NAME OF PRETRAINED MODEL
model_name = 'ocr_2.pkl'

# LOAD MODEL
learn = load_learner(model_name)
# RETRIEVE VOCABULARY
categories = learn.dls.vocab
#print('-- MODEL VOCABULARY --')
#print(learn.dls.vocab)

# MAIN FUNCTION (To categorize our image)
def checkImage(test_image):
    img = PILImage.create(test_image)
    # prediction = the models most likely category as text
    # pred_idx = index in the categories list where the prediction is
    # probabilities is a rank 2 tensor with the first rank being the probability
    # and the second rank being the index of that category
    prediction, pred_idx, probabilities = learn.predict(img)
    
    # This retrieves just the top 5 results
    top_5_probabilities, top_5_indices = probabilities.topk(5)
    #print(probabilities.topk(5))
    #print('-- TOP 5 PROBABILITIES --')
    result = {}
    for idx, probability in zip(top_5_indices, top_5_probabilities):
        print(f"Type: {categories[idx]}- Probability: {probabilities[idx].item():.2f}") 
        result[f"{categories[idx]}"] = f"{probabilities[idx].item():.4f}"
    #print('----------------')
    return prediction,result

# USER INTERFACE BUILDER
iface = gr.Interface(
    fn=checkImage, 
    title="SK Simple OCR",
    inputs="image",
    outputs=["label","label"]
    )
# LAUNCHER share=True means its publically available
iface.launch(share=True)