import streamlit as st
from PIL import Image
import numpy as np
import plotly.graph_objects as go
from transformers import pipeline  # Hugging Face's pipeline API, optional

# Streamlit App Configuration
st.title("3D Image Converter")
st.sidebar.header("Upload Image")

# Upload Image
uploaded_file = st.sidebar.file_uploader("Choose an image", type=["png", "jpg", "jpeg"])

if uploaded_file is not None:
    # Load Image
    image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Image", use_column_width=True)
    
    # Convert Image to Grayscale for Simplicity
    grayscale_image = image.convert("L")
    
    # Create 3D Surface Plot
    z_data = np.asarray(grayscale_image)
    x_data, y_data = np.meshgrid(range(z_data.shape[1]), range(z_data.shape[0]))

    # Create a 3D Surface Plot using Plotly
    fig = go.Figure(data=[go.Surface(z=z_data, x=x_data, y=y_data, colorscale='gray')])
    fig.update_layout(scene=dict(zaxis=dict(title='Height'),
                                 xaxis=dict(title='Width'),
                                 yaxis=dict(title='Depth')),
                      title="3D Representation of Image")
    
    st.plotly_chart(fig)

    # Option to Save 3D Data
    save_button = st.sidebar.button("Save 3D Data")
    if save_button:
        np.save("3d_image_data.npy", z_data)
        st.sidebar.success("3D data saved as 3d_image_data.npy!")

# Optional: Integrate Hugging Face models for advanced transformations
if st.sidebar.checkbox("Apply Hugging Face Transformation"):
    st.sidebar.text("Using Transformers API...")
    model = pipeline("image-to-image", model="stabilityai/stable-diffusion")  # Replace with appropriate Hugging Face model
    result = model(uploaded_file)
    st.image(result["output_image"], caption="Transformed Image", use_column_width=True)

# Note:
# Ensure all dependencies are listed in requirements.txt for deployment.