app.py

import gradio as gr
import fastai
import sys
from fastai.vision.all import load_learner, PILImage
import json # Import the json library

print(f"Current Gradio version: {gr.__version__}: tested: 5.49.1")
print(f"CurrentFastai version: {fastai.__version__}: tested: 2.8.5")
print(f"Current Python sys version: {sys.version}: tested: 3.12.12")
# load learner
fname = './butterfly_learner_1762396100.pkl'
learn = load_learner(fname)
#
print("load learner complete")

# Define the classify_butterfly function

def classify_butterfly(image):
  pred, pred_idx, probs = learn.predict(PILImage(image))
  # Create a dictionary with the results
  results = {
      "prediction": str(pred),  # Convert prediction to string
      "prediction_index": int(pred_idx), # Convert prediction index to int
      "probabilities": probs.tolist() # Convert probabilities to a list
  }
  # Return the results as a JSON string
  return json.dumps(results, indent=2)

#
# Gradio
cnn_butterfly = gr.Interface(fn=classify_butterfly,
  inputs=gr.Image(type="pil"),
  outputs=gr.Json(label="Prediction (JSON)"), # Change output type to Json
  title="Butterfly CNN Tester",
  description="Upload a butterfly image to get the model’s prediction and class probabilities in JSON format.",
)
#
# lauch it
cnn_butterfly.launch()
#Gradio version: 5.49.1
#Fastai version: 2.8.5
#