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import gradio as gr
from fastai.vision.all import *
import matplotlib.pyplot as plt
import numpy as np
from transformers import pipeline
from huggingface_hub import from_pretrained_fastai
pipe = pipeline(
task="image-classification",
model ="ethanmac/dr-maclboomber-retina-classifier",
description="Retinal Condition Classifier",
examples=['sick-eye.jpeg', 'healthy.jpg']
).launch()
# from huggingface_hub import from_pretrained_fastai
# # repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
# repo_id = "espejelomar/identify-my-cat"
# learner = from_pretrained_fastai(repo_id)
# # Load your trained FastAI model
# learn = load_learner('export.pkl')
# def predict_and_plot(img):
# # Get predictions
# pred, pred_idx, probs = learn.predict(img)
# # Get class names
# class_names = ['Normal',
# 'Hollenhorst Emboli',
# 'Hypertensive Retinopathy',
# 'Coat\'s',
# 'Macroaneurism',
# 'Choroidal Neovascularization',
# 'Other',
# 'Branch Retinal Artery Occlusion',
# 'Cilio-Retinal Artery Occlusion',
# 'Branch Retinal Vein Occlusion',
# 'Central Retinal Vein Occlusion',
# 'Hemi-Central Retinal Vein Occlusion',
# 'Background Diabetic Retinopathy',
# 'Proliferative Diabetic Retinopathy',
# 'Arteriosclerotic Retinopathy']
# probs_np = probs.numpy()
# threshold = 0.5
# present = probs_np > threshold
# fig, ax = plt.subplots(figsize=(10, 6))
# y_pos = np.arange(len(class_names))
# colors = ['green' if is_present else 'red' for is_present in present]
# ax.barh(y_pos, probs_np, color=colors)
# ax.set_yticks(y_pos)
# ax.set_yticklabels(class_names)
# ax.invert_yaxis()
# ax.set_xlabel('Probability')
# ax.set_xlim(0, 1)
# ax.set_title('Predicted Probabilities for Each Condition')
# plt.tight_layout()
# return fig
# # Create the Gradio interface
# interface = gr.Interface(
# fn=predict_and_plot,
# inputs=gr.Image(type='pil'),
# outputs=gr.Plot(),
# title="Dr. Macloomber",
# description="Upload an image of a retina to predict the probabilities of various eye conditions."
# )
# # Launch the app
# interface.launch()
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