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import gradio as gr
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing import image
from PIL import Image
import os
# Load the trained model
model = tf.keras.models.load_model("my_keras_model.h5")
# Define image size based on the model's input requirement
image_size = (224, 224)
# Function to make predictions
def predict_image(img):
img = img.resize(image_size) # Resize image to model's expected size
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) / 255.0 # Normalize
prediction = model.predict(img_array)
# Assuming binary classification (fractured or normal)
class_names = ['Fractured', 'Normal']
predicted_class = class_names[int(prediction[0] > 0.5)] # Threshold at 0.5
return f"Prediction: {predicted_class} (Confidence: {prediction[0][0]:.2f})"
# Get image paths dynamically
sample_images_dir = "samples"
sample_images = [os.path.join(sample_images_dir, f) for f in os.listdir(sample_images_dir) if f.endswith(('.jpg', '.png'))]
# File download paths
report_path = "report.pdf"
notebook_path = "bfd.ipynb"
# Function to provide file download links
def get_download_links():
return f"""
**Download Project Files:**
- [Download Report (PDF)]({report_path})
- [Download Notebook (IPYNB)]({notebook_path})
"""
# Define Gradio Interface
interface = gr.Interface(
fn=predict_image,
inputs=gr.Image(type="pil"),
outputs=gr.Textbox(),
examples=sample_images, # Preloaded images for testing
title="Bone Fracture Detection",
description="Upload an X-ray image or select a sample image to check for fractures.",
article=f"""
## Instructions
- Upload an X-ray image to check for fractures.
- Select a sample image for quick testing.
- The model will predict if the bone is fractured or normal.
## Capabilities
- Detects bone fractures from X-ray images.
- Uses a trained deep learning model for classification.
{get_download_links()}
"""
)
# Launch the Gradio app
if __name__ == "__main__":
interface.launch() |