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import gradio as gr
from transformers import pipeline, AutoImageProcessor, AutoModelForImageClassification
import torch
import os
from huggingface_hub import login
# Login to Hugging Face Hub
if 'HF_TOKEN' in os.environ:
login(token=os.environ['HF_TOKEN'])
try:
# Initialize the model and processor
processor = AutoImageProcessor.from_pretrained(
"alexdekan030/autotrain-awcru-nr8j7",
use_auth_token=os.environ.get('HF_TOKEN')
)
model = AutoModelForImageClassification.from_pretrained(
"alexdekan030/autotrain-awcru-nr8j7",
use_auth_token=os.environ.get('HF_TOKEN')
)
pipe = pipeline("image-classification", model=model, image_processor=processor)
def predict_pneumonia(image):
"""
Predict whether an image shows pneumonia or normal chest X-ray
Args:
image: Input image
Returns:
dict: Dictionary containing prediction probabilities
"""
if image is None:
return {"Error": 1.0}
try:
# Make prediction
result = pipe(image)
# Create a formatted output dictionary
probabilities = {pred['label']: float(pred['score']) for pred in result}
return probabilities
except Exception as e:
return {"Error": 1.0}
# Create the Gradio interface
demo = gr.Interface(
fn=predict_pneumonia,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=2),
title="Pneumonia Detection from Chest X-rays",
description="""Upload a chest X-ray image to detect if it shows signs of pneumonia.
The model will classify the image as either 'NORMAL' or 'PNEUMONIA'
and provide confidence scores for each class.""",
examples=[
# You can add example images here if you have them
# ["path/to/example1.jpg"],
# ["path/to/example2.jpg"]
]
)
except Exception as e:
# Create a simple interface if model loading fails
def error_interface(image):
return {"Error": "Model failed to load. Please check authentication and model availability."}
demo = gr.Interface(
fn=error_interface,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=1),
title="Error Loading Model",
description="There was an error loading the model. Please check if the model is accessible and authentication is correct."
)
# Launch the app
demo.launch() |