import gradio as gr
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np

# Load your pre-trained model
model_path = 'my_model.h5'
model = load_model(model_path)

# Function to make predictions
def predict_image_to_text(image):
    # Preprocess the input image (adjust based on your model's requirements)
    img = Image.open(image)

    # Resize the image to (224, 224)
    img = img.resize((224, 224))

    # Convert the image to a NumPy array
    img_array = np.array(img)

    # Ensure the image has 3 channels (R, G, B)
    if img_array.shape[-1] == 4:
        img_array = img_array[:, :, :3]

    # Normalize the image data if needed
    img_array = img_array / 255.0

    # Add batch dimension
    img_array = np.expand_dims(img_array, axis=0)

    # Make prediction
    prediction = model.predict(img_array)[0]

    

    return prediction

# Gradio Interface
iface = gr.Interface(
    fn=predict_image_to_text,
    inputs=gr.Image(),  # Gradio will automatically handle image input
    outputs="text",
    live=True,
)

# Launch the Gradio interface
iface.launch()