app.py

# -*- coding: utf-8 -*-
"""Copy of app.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1KoR_JrJMqzUq-XagPaxVUp874bVW_qNI
"""

#/default_exp app



#
#from fastai.vision.all import *
#!pip install gradio
import gradio as gr

#/export
def is_monet(x): return x[0].issupper

#/export
from fastai.vision.all import *

#/export
learn = load_learner('model.pkl')

#/export
categories = ('Manet', 'Monet')
def classify_image(img):
  pred, idx, probs = learn.predict(img)
  return dict(zip(categories, map(float, probs)))

# export
image = gr.Image()  # Image input without shape argument
label = gr.Label()  # Label output

# Define some example images (make sure these paths are correct)
examples = ['monet.jpg', 'manet2.jpg', 'manet1.jpeg']

# Create the Gradio interface
#intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)

# Create the Gradio interface with a title and description
intf = gr.Interface(
    fn=classify_image, 
    inputs=image, 
    outputs=label, 
    examples=examples,
    title="Monet vs Manet Image Classifier",  # Add title here
    description="Upload an image to classify it as either a Monet or Manet painting."  # Optional description
)

# Launch the interface with Inline=False to open in a separate window
intf.launch(share=True)

#!pip install nbdev

import os
os.listdir('/content')

import os
notebook_path = '/content/app.ipynb'
print(os.path.abspath(notebook_path))

import os
file_exists = os.path.isfile('/content/app.ipynb')
print(file_exists)  # This should return True if the file exists

import os

# Extract the path of the current notebook in Colab
notebook_path = '/content/app.ipynb'  # Replace with the actual path if different
print(f"Notebook is located at: {notebook_path}")

nbdev.export.nb_export('Copy of app.ipynb', 'app')
print('Export successful')