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'))]

# 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="""
    ## Instructions
    - Type a question or statement
    - Upload images or audio files
    - You can combine text with media files
    - Support 2 modalities at the same time
    - The model can analyze images and transcribe audio
    - For best results with images, use JPG or PNG files
    - For audio, use WAV, MP3, or FLAC files

    ## Capabilities
    This chatbot can:
    - Answer questions and provide explanations
    - Describe and analyze images
    - Transcribe, translate, summarize, and analyze audio content
    - Process multiple inputs in the same message
    - Maintain context throughout the conversation
    """
)

# Launch the Gradio app
if __name__ == "__main__":
    interface.launch()